Introduction



Urban development as a continuum: A multinomial logistic regression approachAhmed M Mustafa1,*, Mario Cools1, Ismail Saadi1, Jacques Teller11 LEMA, University of Liège, Liège, Belgium*a.mustafa@ulg.ac.beAbstract. Urban development is a complex process influenced by a number of driving forces, including spatial planning, topography and urban economics. Identifying these drivers is crucial for the regulation of urban development and the calibration of predictive models. Existing land-use models generally consider urban development as a binary process, through the identification of built versus non-built areas. This study considers urban development as a continuum, characterized by different level of densities, which can be related to different driving forces.A multinomial logistic regression model was employed to investigate the effects of drivers on different urban densities during the past decade in Wallonia, Belgium. Sixteen drivers were selected from sets of driving forces including accessibility, geo-physical features, policies and socio-economic factors.It appears that urban development in Wallonia is remarkably influenced by land-use policies and accessibility. Most importantly, our results highlight that the impact of different drivers varies along with urban density.Keywords: urban development · driving forces · multinomial logistic regression model · cadastral data · urban densities.IntroductionUrban development is a global issue with paramount socioeconomic and environmental implications, which may affect our well-being in terms of society, economy and/or culture. It may lead to a number of problems related to water quality degradation, air pollution, socio-economic disparities and social fragmentation ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"18lp1epag","properties":{"formattedCitation":"[1, 2]","plainCitation":"[1, 2]"},"citationItems":[{"id":382,"uris":[""],"uri":[""],"itemData":{"id":382,"type":"article-journal","title":"Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island","container-title":"International Journal of Climatology","page":"1-26","volume":"23","issue":"1","source":"Wiley Online Library","abstract":"Progress in urban climatology over the two decades since the first publication of the International Journal of Climatology is reviewed. It is emphasized that urban climatology during this period has benefited from conceptual advances made in microclimatology and boundary-layer climatology in general. The role of scale, heterogeneity, dynamic source areas for turbulent fluxes and the complexity introduced by the roughness sublayer over the tall, rigid roughness elements of cities is described. The diversity of urban heat islands, depending on the medium sensed and the sensing technique, is explained. The review focuses on two areas within urban climatology. First, it assesses advances in the study of selected urban climatic processes relating to urban atmospheric turbulence (including surface roughness) and exchange processes for energy and water, at scales of consideration ranging from individual facets of the urban environment, through streets and city blocks to neighbourhoods. Second, it explores the literature on the urban temperature field. The state of knowledge about urban heat islands around 1980 is described and work since then is assessed in terms of similarities to and contrasts with that situation. Finally, the main advances are summarized and recommendations for urban climate work in the future are made. Copyright ? 2003 Royal Meteorological Society.","DOI":"10.1002/joc.859","ISSN":"1097-0088","shortTitle":"Two decades of urban climate research","journalAbbreviation":"Int. J. Climatol.","language":"en","author":[{"family":"Arnfield","given":"A. John"}],"issued":{"date-parts":[["2003",1,1]]},"accessed":{"date-parts":[["2015",3,15]]}}},{"id":377,"uris":[""],"uri":[""],"itemData":{"id":377,"type":"article-journal","title":"Assessments of urban growth in the Tampa Bay watershed using remote sensing data","container-title":"Remote Sensing of Environment","page":"203-215","volume":"97","issue":"2","source":"CrossRef","DOI":"10.1016/j.rse.2005.04.017","ISSN":"00344257","language":"en","author":[{"family":"Xian","given":"George"},{"family":"Crane","given":"Mike"}],"issued":{"date-parts":[["2005",7]]},"accessed":{"date-parts":[["2015",3,15]]}}}],"schema":""} [1, 2]. Several modelling approaches have been adopted in the analysis of urban growth. Those models can be aimed at predicting spatial location and/or the amount of change. Prediction of the location of urban growth can be modeled through different approaches including statistical methods, cellular automata and agent-based models. For different modelling approaches, it is important to explore and analyze the main drivers of urban growth on space bases in order to better understand, control and model the future growth of urban settlements. Urban growth models do usually not differentiate between high-density and low-density urban development ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1e5uv7ejlh","properties":{"formattedCitation":"{\\rtf [3\\uc0\\u8211{}5]}","plainCitation":"[3–5]"},"citationItems":[{"id":105,"uris":[""],"uri":[""],"itemData":{"id":105,"type":"article-journal","title":"Forty years of urban expansion in Beijing: What is the relative importance of?physical, socioeconomic, and neighborhood factors?","container-title":"Applied Geography","page":"1-10","volume":"38","source":"ScienceDirect","abstract":"Urban expansion is one of the major causes of many ecological and environmental problems in urban areas and the surrounding regions. Understanding the process of urban expansion and its driving factors is crucial for urban growth planning and management to mitigate the adverse impacts of such growth. Previous studies have primarily been conducted from a static point of view by examining the process of urban expansion for only one or two time periods. Few studies have investigated the temporal dynamics of the effects of the driving factors in urban expansion. Using Beijing as a case study, this research aims to fill this gap. Urban expansion from 1972 to 2010 was detected from multi-temporal remote sensing images for four time periods. The effects of physical, socioeconomic, and neighborhood factors on urban expansion and their temporal dynamics were investigated using binary logistic regression. In addition, the relative importance of the three types of driving factors was examined using variance partitioning. The results showed that Beijing has undergone rapid and magnificent urban expansion in the past forty years. Physical, socioeconomic, and neighborhood factors have simultaneously affected this expansion. Socioeconomic factors were the most important driving force, except during the period of 1972–1984. In addition, the effects of these driving factors on urban expansion varied with time. The magnitude of the unique effects of physical factors and neighborhood factors declined while that of socioeconomic factors increased along with the urbanization process. The findings of this study can help us better understand the process of urban expansion and thus have important implications for urban planning and management in Beijing and similar cities.","DOI":"10.1016/j.apgeog.2012.11.004","ISSN":"0143-6228","shortTitle":"Forty years of urban expansion in Beijing","journalAbbreviation":"Applied Geography","author":[{"family":"Li","given":"Xiaoma"},{"family":"Zhou","given":"Weiqi"},{"family":"Ouyang","given":"Zhiyun"}],"issued":{"date-parts":[["2013",3]]},"accessed":{"date-parts":[["2014",6,25]]}}},{"id":323,"uris":[""],"uri":[""],"itemData":{"id":323,"type":"article-journal","title":"Drivers of land cover and land use changes in St. Louis metropolitan area over the past 40 years characterized by remote sensing and census population data","container-title":"International Journal of Applied Earth Observation and Geoinformation","page":"161-174","volume":"35, Part B","source":"ScienceDirect","abstract":"In this study, we explored the spatial and temporal patterns of land cover and land use (LCLU) and population change dynamics in the St. Louis Metropolitan Statistical Area. The goal of this paper was to quantify the drivers of LCLU using long-term Landsat data from 1972 to 2010. First, we produced LCLU maps by using Landsat images from 1972, 1982, 1990, 2000, and 2010. Next, tract level population data of 1970, 1980, 1990, 2000, and 2010 were converted to 1-km square grid cells. Then, the LCLU maps were integrated with basic grid cell data to represent the proportion of each land cover category within a grid cell area. Finally, the proportional land cover maps and population census data were combined to investigate the relationship between land cover and population change based on grid cells using Pearson's correlation coefficient, ordinary least square (OLS), and local level geographically weighted regression (GWR). Land cover changes in terms of the percentage of area affected and rates of change were compared with population census data with a focus on the analysis of the spatial-temporal dynamics of urban growth patterns. The correlation coefficients of land cover categories and population changes were calculated for two decadal intervals between 1970 and 2010. Our results showed a causal relationship between LCLU changes and population dynamics over the last 40 years. Urban sprawl was positively correlated with population change. However, the relationship was not linear over space and time. Spatial heterogeneity and variations in the relationship demonstrate that urban sprawl was positively correlated with population changes in suburban area and negatively correlated in urban core and inner suburban area of the St. Louis Metropolitan Statistical Area. These results suggest that the imagery reflects processes of urban growth, inner-city decline, population migration, and social spatial inequality. The implications provide guidance for sustainable urban planning and development. We also demonstrate that grid cells allow robust synthesis of remote sensing and socioeconomic data to advance our knowledge of urban growth dynamics from both spatial and temporal scales and its association with population change.","DOI":"10.1016/j.jag.2014.08.020","ISSN":"0303-2434","journalAbbreviation":"International Journal of Applied Earth Observation and Geoinformation","author":[{"family":"Maimaitijiang","given":"Maitiniyazi"},{"family":"Ghulam","given":"Abduwasit"},{"family":"Sandoval","given":"J. S. Onésimo"},{"family":"Maimaitiyiming","given":"Matthew"}],"issued":{"date-parts":[["2015",3]]},"accessed":{"date-parts":[["2014",11,22]]}}},{"id":335,"uris":[""],"uri":[""],"itemData":{"id":335,"type":"article-journal","title":"Measuring the Effect of Stochastic Perturbation Component in Cellular Automata Urban Growth Model","container-title":"Procedia Environmental Sciences","collection-title":"12th International Conference on Design and Decision Support Systems in Architecture and Urban Planning, DDSS 2014","page":"156-168","volume":"22","source":"ScienceDirect","abstract":"Urban environments are complex dynamic systems whose prediction of the future states cannot exclusively rely on deterministic rules. Although several studies on urban growth were carried out using different modelling approaches, the measurement of uncertainties was commonly neglected in these studies. This paper investigates the effect of uncertainty in urban growth models by introducing a stochastic perturbation method. A cellular automaton is used to simulate predicted urban growth. The effect of stochastic perturbation is addressed by comparing series of urban growth simulations based on different degree of stochastic perturbation randomness with the original urban growth simulation, obtained with the sole cellular automata neighbouring effects. These simulations are evaluated using cell-to-cell location agreement and a number of spatial metrics. The model framework has been applied to the Ourthe river basin in Belgium. The results show that the accuracy of the model is increased by introducing a stochastic perturbation component with a limited degree of randomness, in the cellular automata urban growth model.","DOI":"10.1016/j.proenv.2014.11.016","ISSN":"1878-0296","journalAbbreviation":"Procedia Environmental Sciences","author":[{"family":"Mustafa","given":"Ahmed"},{"family":"Saadi","given":"Isma?l"},{"family":"Cools","given":"Mario"},{"family":"Teller","given":"Jacques"}],"issued":{"date-parts":[["2014"]]},"accessed":{"date-parts":[["2014",12,17]]}}}],"schema":""} [e.g. 3–5] . The main goal of this paper is to investigate the major drivers of different urban densities. This requires analyzing the relationship between urban growth and a number of forces related to people choices in terms of spatial location of new urban developments.Empirical estimation models use statistical methods to model the relationship between urban growth and its drivers based on past observation. In this context, logistic regression models are commonly employed to model urban development potentials ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"24cf3i0o4t","properties":{"formattedCitation":"{\\rtf [3, 5\\uc0\\u8211{}8]}","plainCitation":"[3, 5–8]"},"citationItems":[{"id":319,"uris":[""],"uri":[""],"itemData":{"id":319,"type":"article-journal","title":"Assessing spatial dynamics of urban growth using an integrated land use model. Application in Santiago Metropolitan Area, 2010–2045","container-title":"Land Use Policy","page":"415-425","volume":"38","source":"ScienceDirect","abstract":"Scenario analysis of urban dynamics from spatial land use models can support urban, planning and policy. An integrated modeling approach, linking assessment of urban spatial dynamics, was applied to the Santiago Metropolitan Area (SMA). The integrated land use change model combines, a logistic regression model, Markov chain, and cellular automata. This model was calibrated with data, from 1975 to 2010, and was used to make predictions for the years 2030 and 2045, using two datasets of, urban and non-urban explanatory variables. Urban change estimates showed the highest fit during the, model calibration phase. The true-positive proportion and standard Kappa value (κ) were of 99% and, 0.87 respectively when validating against an urban cover reference map from 2010. Urban growth was, equal to +27,000 ha (72%) for the period 1975–2010, and the city of Santiago is projected to, reach approximately 93,000 ha by 2045 (+43% from 2010). In the SMA the most important, urban growth pattern is peri-urban development, referring to widespread boundaries and higher, fragmentation in peripheral municipalities. Predictions for 2030 estimate that ～15% of the projected, urban expansion will occur outside the boundary set by the current Regulatory Plan proposal. These, results demonstrate the capacity of the integrated model to establish comparisons with urban plans, and its utility to explain both the amount and constraints of urban growth. The integrated approach of, urban dynamic assessment using land use modeling is useful for spatiotemporal representation of, distinct urban development forms.","DOI":"10.1016/j.landusepol.2013.11.024","ISSN":"0264-8377","journalAbbreviation":"Land Use Policy","author":[{"family":"Puertas","given":"Olga Lucia"},{"family":"Henríquez","given":"Cristian"},{"family":"Meza","given":"Francisco Javier"}],"issued":{"date-parts":[["2014",5]]},"accessed":{"date-parts":[["2014",11,27]]}},"label":"page"},{"id":216,"uris":[""],"uri":[""],"itemData":{"id":216,"type":"article-journal","title":"Calibration of stochastic cellular automata: the application to rural-urban land conversions","container-title":"International Journal of Geographical Information Science","page":"795-818","volume":"16","issue":"8","source":"Taylor and Francis+NEJM","abstract":"Despite the recognition of cellular automata (CA) as a flexible and powerful tool for urban growth simulation, the calibration of CA had been largely heuristic until recent efforts to incorporate multi-criteria evaluation and artificial neural network into rule definition. This study developed a stochastic CA model, which derives its initial probability of simulation from observed sequential land use data. Furthermore, this initial probability is updated dynamically through local rules based on the strength of neighbourhood development. Consequentially the integration of global (static) and local (dynamic) factors produces more realistic simulation results. The procedure of calibrated CA can be applied in other contexts with minimum modification. In this study we applied the procedure to simulate rural-urban land conversions in the city of Guangzhou, China. Moreover, the study suggests the need to examine the result of CA through spatial, tabular and structural validation.","DOI":"10.1080/13658810210157769","ISSN":"1365-8816","shortTitle":"Calibration of stochastic cellular automata","author":[{"family":"Wu","given":"Fulong"}],"issued":{"date-parts":[["2002",12,1]]},"accessed":{"date-parts":[["2014",9,22]]}},"label":"page"},{"id":105,"uris":[""],"uri":[""],"itemData":{"id":105,"type":"article-journal","title":"Forty years of urban expansion in Beijing: What is the relative importance of?physical, socioeconomic, and neighborhood factors?","container-title":"Applied Geography","page":"1-10","volume":"38","source":"ScienceDirect","abstract":"Urban expansion is one of the major causes of many ecological and environmental problems in urban areas and the surrounding regions. Understanding the process of urban expansion and its driving factors is crucial for urban growth planning and management to mitigate the adverse impacts of such growth. Previous studies have primarily been conducted from a static point of view by examining the process of urban expansion for only one or two time periods. Few studies have investigated the temporal dynamics of the effects of the driving factors in urban expansion. Using Beijing as a case study, this research aims to fill this gap. Urban expansion from 1972 to 2010 was detected from multi-temporal remote sensing images for four time periods. The effects of physical, socioeconomic, and neighborhood factors on urban expansion and their temporal dynamics were investigated using binary logistic regression. In addition, the relative importance of the three types of driving factors was examined using variance partitioning. The results showed that Beijing has undergone rapid and magnificent urban expansion in the past forty years. Physical, socioeconomic, and neighborhood factors have simultaneously affected this expansion. Socioeconomic factors were the most important driving force, except during the period of 1972–1984. In addition, the effects of these driving factors on urban expansion varied with time. The magnitude of the unique effects of physical factors and neighborhood factors declined while that of socioeconomic factors increased along with the urbanization process. The findings of this study can help us better understand the process of urban expansion and thus have important implications for urban planning and management in Beijing and similar cities.","DOI":"10.1016/j.apgeog.2012.11.004","ISSN":"0143-6228","shortTitle":"Forty years of urban expansion in Beijing","journalAbbreviation":"Applied Geography","author":[{"family":"Li","given":"Xiaoma"},{"family":"Zhou","given":"Weiqi"},{"family":"Ouyang","given":"Zhiyun"}],"issued":{"date-parts":[["2013",3]]},"accessed":{"date-parts":[["2014",6,25]]}},"label":"page"},{"id":75,"uris":[""],"uri":[""],"itemData":{"id":75,"type":"article-journal","title":"Logistic regression and cellular automata-based modelling of retail, commercial and residential development in the city of Ahmedabad, India","container-title":"Cities","page":"68-86","volume":"39","source":"ScienceDirect","abstract":"This study presents a hybrid simulation model that combines logistic regression and cellular automata-based modelling to simulate future urban growth and development for the city of Ahmedabad in India. The model enables to visualize the consequence of development projections in combination with present zoning and development control regulations. The growth in activities’ floor space is projected at a zonal level using time series data. Then, a logistic regression model is used to calculate a probability surface of development transition, while a cellular automata-based spatial interaction model is used to simulate change in activity floor space per activity, and thus urban growth. The developed model has the capacity to simulate urban growth space and hence vertical growth. The structure of the model allows for a detailed urban growth simulation and is flexible enough to incorporate changes in development control regulations and settings for spatial interaction. Therefore, it carries scope of being used to visualize growth for other, similar, cities and help urban planners and decision makers to understand the consequences of their decisions on urban growth and development.","DOI":"10.1016/j.cities.2014.02.007","ISSN":"0264-2751","journalAbbreviation":"Cities","author":[{"family":"Munshi","given":"Talat"},{"family":"Zuidgeest","given":"Mark"},{"family":"Brussel","given":"Mark"},{"family":"van Maarseveen","given":"Martin"}],"issued":{"date-parts":[["2014",8]]},"accessed":{"date-parts":[["2014",7,2]]}},"label":"page"},{"id":335,"uris":[""],"uri":[""],"itemData":{"id":335,"type":"article-journal","title":"Measuring the Effect of Stochastic Perturbation Component in Cellular Automata Urban Growth Model","container-title":"Procedia Environmental Sciences","collection-title":"12th International Conference on Design and Decision Support Systems in Architecture and Urban Planning, DDSS 2014","page":"156-168","volume":"22","source":"ScienceDirect","abstract":"Urban environments are complex dynamic systems whose prediction of the future states cannot exclusively rely on deterministic rules. Although several studies on urban growth were carried out using different modelling approaches, the measurement of uncertainties was commonly neglected in these studies. This paper investigates the effect of uncertainty in urban growth models by introducing a stochastic perturbation method. A cellular automaton is used to simulate predicted urban growth. The effect of stochastic perturbation is addressed by comparing series of urban growth simulations based on different degree of stochastic perturbation randomness with the original urban growth simulation, obtained with the sole cellular automata neighbouring effects. These simulations are evaluated using cell-to-cell location agreement and a number of spatial metrics. The model framework has been applied to the Ourthe river basin in Belgium. The results show that the accuracy of the model is increased by introducing a stochastic perturbation component with a limited degree of randomness, in the cellular automata urban growth model.","DOI":"10.1016/j.proenv.2014.11.016","ISSN":"1878-0296","journalAbbreviation":"Procedia Environmental Sciences","author":[{"family":"Mustafa","given":"Ahmed"},{"family":"Saadi","given":"Isma?l"},{"family":"Cools","given":"Mario"},{"family":"Teller","given":"Jacques"}],"issued":{"date-parts":[["2014"]]},"accessed":{"date-parts":[["2014",12,17]]}},"label":"page"}],"schema":""} [e.g. 3, 5–8]. A multinomial logistic regression model (MNL) is a generalization of a logistic regression model for more than two discrete and unordered response categories. Thus, the MNL methodology allows for the consideration of several classes or urban densities as the dependent, responsible, variable (Y), using a set of independent, explanatory, variables (X), which were selected based on the following literature review.Potential driving forcesThe identification of dominant urban growth driving forces is the main objective of this paper. In literature ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"230vsdsfof","properties":{"formattedCitation":"{\\rtf [3, 9\\uc0\\u8211{}17]}","plainCitation":"[3, 9–17]"},"citationItems":[{"id":8,"uris":[""],"uri":[""],"itemData":{"id":8,"type":"article-journal","title":"Land-use changes and their social driving forces in Czechia in the 19th and 20th centuries","container-title":"Land Use Policy","page":"65-73","volume":"18","issue":"1","source":"ScienceDirect","abstract":"This paper is an overview of the major land-use changes in Czechia over the past 150 years, with a focus on the social forces driving these changes. Sources of land-use data are also discussed. Though economic development is seen as the key impact on land use before 1945, under communism (1948–89), the importance of political decisions was crucial. The post-war period is analysed in greater detail as this was the era of the most significant landscape changes. The most recent period encompassed a return to market conditions, resulting in environmentally favourable land-use changes.","DOI":"10.1016/S0264-8377(00)00047-8","ISSN":"0264-8377","journalAbbreviation":"Land Use Policy","author":[{"family":"Bi???k","given":"Ivan"},{"family":"Jele?ek","given":"Leo?"},{"family":"?těpánek","given":"V??t"}],"issued":{"date-parts":[["2001",1]]},"accessed":{"date-parts":[["2014",9,30]]}}},{"id":276,"uris":[""],"uri":[""],"itemData":{"id":276,"type":"article-journal","title":"Proximate causes of land-use change in Narok District, Kenya: a spatial statistical model","container-title":"Agriculture, Ecosystems & Environment","page":"65-81","volume":"85","issue":"1–3","source":"ScienceDirect","abstract":"This study attempts to identify how much understanding of the driving forces of land-use changes can be gained through a spatial, statistical analysis. Hereto, spatial, statistical models of the proximate causes of different processes of land-use change in the Mara Ecosystem (Kenya) were developed, taking into account the spatial variability of the land-use change processes. The descriptive spatial models developed here suggest some important factors driving the land-use changes that can be related to some well-established theoretical frameworks. The explanatory variables of the spatial model of mechanised agriculture suggest a von Thünen-like model, where conversion to agriculture is controlled by the distance to the market, as a proxy for transportation costs, and agro-climatic potential. Expansion of smallholder agriculture and settlements is also controlled by land rent, defined, in this case, by proximity to permanent water, land suitability, location near a tourism market, and vicinity to villages to gain access to social services (e.g. health clinics, schools, local markets). This difference in perception of land rent reflects the widely different social and economic activities and objectives of smallholders versus the large entrepreneurs involved in mechanised farming. Spatial heterogeneity as well as the variability in time of land-use change processes affect our ability to use regression models for wide ranging extrapolations. The models allow evaluating the impact of changes in driving forces that are well represented by proximate causes of land-use change.","DOI":"10.1016/S0167-8809(01)00188-8","ISSN":"0167-8809","shortTitle":"Proximate causes of land-use change in Narok District, Kenya","journalAbbreviation":"Agriculture, Ecosystems & Environment","author":[{"family":"Serneels","given":"Suzanne"},{"family":"Lambin","given":"Eric F"}],"issued":{"date-parts":[["2001",6]]},"accessed":{"date-parts":[["2014",7,2]]}}},{"id":46,"uris":[""],"uri":[""],"itemData":{"id":46,"type":"article-journal","title":"Land use change modelling: current practice and research priorities","container-title":"GeoJournal","page":"309-324","volume":"61","issue":"4","source":"link.","abstract":"Land use change models are tools to support the analysis of the causes and consequences of land use dynamics. Scenario analysis with land use models can support land use planning and policy. Numerous land use models are available, developed from different disciplinary backgrounds. This paper reviews current models to identify priority issues for future land use change modelling research. This discussion is based on six concepts important to land use modelling: (1) Level of analysis; (2) Cross-scale dynamics; (3) Driving forces; (4) Spatial interaction and neighbourhood effects; (5) Temporal dynamics; and (6) Level of integration. For each of these concepts an overview is given of the variety of methods used to implement these concepts in operational models. It is concluded that a lot of progress has been made in building land use change models. However, in order to incorporate more aspects important to land use modelling it is needed to develop a new generation of land use models that better address the multi-scale characteristics of the land use system, implement new techniques to quantify neighbourhood effects, explicitly deal with temporal dynamics and achieve a higher level of integration between disciplinary approaches and between models studying urban and rural land use changes. If these requirements are fulfilled models will better support the analysis of land use dynamics and land use policy formulation.","DOI":"10.1007/s10708-004-4946-y","ISSN":"0343-2521, 1572-9893","shortTitle":"Land use change modelling","journalAbbreviation":"GeoJournal","language":"en","author":[{"family":"Verburg","given":"Peter H."},{"family":"Schot","given":"Paul P."},{"family":"Dijst","given":"Martin J."},{"family":"Veldkamp","given":"A."}],"issued":{"date-parts":[["2004",12,1]]},"accessed":{"date-parts":[["2014",9,26]]}}},{"id":210,"uris":[""],"uri":[""],"itemData":{"id":210,"type":"article-journal","title":"Spatial-Temporal Pattern and Driving Forces of Land Use Changes in Xiamen","container-title":"Pedosphere","page":"477-488","volume":"16","issue":"4","source":"ScienceDirect","abstract":"Using Landsat TM data of 1988, 1998 and 2001, the dynamic process of the spatial-temporal characteristics of land use changes during 13 years from 1988 to 2001 in the special economic zone of Xiamen, China was analyzed to improve understanding and to find the driving forces of land use change so that sustainable land utilization could be practiced. During the 13 years cropland decreased remarkably by nearly 11304.95 ha. The areas of rural-urban construction and water body increased by 10 152.24 ha and 848.94 ha, respectively. From 1988 to 2001, 52.5% of the lost cropland was converted into rural-urban industrial land. Rapid urbanization contributed to a great change in the rate of cropland land use during these years. Land-reclamation also contributed to a decrease in water body area as well as marine ecological and environmental destruction. In the study area 1) urbanization and industrialization, 2) infrastructure and agricultural intensification, 3) increased affluence of the farming community, and 4) policy factors have driven the land use changes. Possible sustainable land use measures included construction of a land management system, land planning, development of potential land resources, new technology applications, and marine ecological and environmental protection.","DOI":"10.1016/S1002-0160(06)60078-7","ISSN":"1002-0160","journalAbbreviation":"Pedosphere","author":[{"family":"QUAN","given":"Bin"},{"family":"CHEN","given":"Jian-Fei"},{"family":"QIU","given":"Hong-Lie"},{"family":"R?MKENS","given":"M. J. M."},{"family":"YANG","given":"Xiao-Qi"},{"family":"JIANG","given":"Shi-Feng"},{"family":"LI","given":"Bi-Cheng"}],"issued":{"date-parts":[["2006",8]]},"accessed":{"date-parts":[["2014",9,30]]}}},{"id":5,"uris":[""],"uri":[""],"itemData":{"id":5,"type":"article-journal","title":"Spatial determinants of urban land use change in Lagos, Nigeria","container-title":"Land Use Policy","page":"502-515","volume":"24","issue":"2","source":"ScienceDirect","abstract":"The objective of this research was to identify the factors responsible for residential and industrial/commercial land development in Lagos between 1984 and 2000. Land use changes were mapped using satellite images, while binary logistic regression was used to model the probability of observing urban development as a function of spatially explicit independent variables. Accessibility, spatial interaction effects and policy variables were the major determinants of land use change. Variables that influenced residential development were not necessarily those responsible for the expansion of industrial/commercial land areas. The evidence of frontier residential development calls for land tenure and housing development reforms, and land use controls to minimize the environmental consequences of unplanned urban expansion.","DOI":"10.1016/j.landusepol.2006.09.001","ISSN":"0264-8377","journalAbbreviation":"Land Use Policy","author":[{"family":"Braimoh","given":"Ademola K."},{"family":"Onishi","given":"Takashi"}],"issued":{"date-parts":[["2007",4]]},"accessed":{"date-parts":[["2014",6,25]]}}},{"id":248,"uris":[""],"uri":[""],"itemData":{"id":248,"type":"article-journal","title":"Detecting and modelling spatial patterns of urban sprawl in highly fragmented areas: A case study in the Flanders–Brussels region","container-title":"Landscape and Urban Planning","page":"10-19","volume":"93","issue":"1","source":"ScienceDirect","abstract":"The Flanders–Brussels region (Belgium) is one of the most urbanised regions in Europe. Since the 1960s the region is subject to urban sprawl, which resulted in highly fragmented landscapes. In this study, urban expansion in the period 1976–2000 is detected using LANDSAT satellite imagery in two contrasting study areas (highly urbanised vs. semi-urbanised) in the Flanders–Brussels area. The highly urbanised study area is characterised by a concentric growth pattern, while the urban expansion in the semi-urban area is much more fragmented. Next, the observed urban sprawl pattern of 2000 was reproduced by means of a spatial model, based on suitability maps. Employment potential, distance to roads and to motorway entry points and flood risk were used to assess the suitability for new built-up land. The observed expansion of the built-up area between 1976 and 1988 was used to calibrate the model parameters. The land cover map of 2000 was used to validate the model output. The analysis shows that the model output should not be interpreted at the level of individual grid cells. At aggregation levels of 240 m × 240 m and above the model produces significant results. The model performance is better in areas with concentric urban sprawl patterns than in highly fragmented areas. Because of its simplicity, the proposed methodology is a useful tool for land managers and policy makers that want to evaluate the impact of their decisions and develop future scenarios.","DOI":"10.1016/j.landurbplan.2009.05.018","ISSN":"0169-2046","shortTitle":"Detecting and modelling spatial patterns of urban sprawl in highly fragmented areas","journalAbbreviation":"Landscape and Urban Planning","author":[{"family":"Poelmans","given":"Lien"},{"family":"Van Rompaey","given":"Anton"}],"issued":{"date-parts":[["2009",10,30]]},"accessed":{"date-parts":[["2014",6,25]]}}},{"id":65,"uris":[""],"uri":[""],"itemData":{"id":65,"type":"article-journal","title":"Analysis to driving forces of land use change in Lu'an mining area","container-title":"Transactions of Nonferrous Metals Society of China","page":"s727-s732","volume":"21, Supplement 3","source":"ScienceDirect","abstract":"By selecting impact factors of driving force and formulating evaluation criteria of the impacts, the evaluation system of corresponding driving force impact of land use change was established. Taking Lu'an mining area as an example, the specific impact factors of coal mine were comprehensively evaluated and analyzed in order to carry out qualitative and quantitative analysis for the driving force of mining-land use change. The principal component analysis shows that the social and economic development in mining area from 2000 to 2007 demonstrates continuous accelerate trends, and the impacts of its overall driving force to land use change are increased gradually. The socio-economic factors have more impacts to mining-land use change than those of the natural resources. The main driving force of mining-land use change also include population, technological progress and policy.","DOI":"10.1016/S1003-6326(12)61670-7","ISSN":"1003-6326","journalAbbreviation":"Transactions of Nonferrous Metals Society of China","author":[{"family":"Liu","given":"Chang-hua"},{"family":"Ma","given":"Xiao-xiao"}],"issued":{"date-parts":[["2011",12]]},"accessed":{"date-parts":[["2014",9,30]]}}},{"id":273,"uris":[""],"uri":[""],"itemData":{"id":273,"type":"article-journal","title":"Suburban change: A time series approach to measuring form and spatial configuration","container-title":"The Journal of Space Syntax","page":"74-91","volume":"4","issue":"1","source":"joss.bartlett.ucl.ac.uk","archive_location":"Review of one case study neighborhood looking at approximately 4000 buildings, plots, blocks, and axial lines","abstract":"Suburban change: A time series approach to measuring form and spatial configuration","ISSN":"2044-7507","note":"With few exceptions, the field of suburban studies has largely ignored the question of what happens to a suburb after initial development, and efforts toward this end are often hampered by limited techniques for the direct measurement of built form and space over time. Historic data sources and computational advancements are prevailing against some of these limitations, but there remains a need for techniques to gather and process formal and spatial suburban data: not merely in the aggregate, but also in detailed patterns within a study area. Given the permanence of spatial and formal configurations in our cities, it is essential to develop the tools to better understand and predict future patterns of growth and change. In this study, a method for examining the relationship between long-term changes in built form and predictive characteristics such as global integration and block size is developed and explored. Conzenian morphology and space syntax approaches are integrated within a geographic information system (GIS) framework, and used to study an historic first-ring suburb in Raleigh, NC at four points in time over a 96-year span. Aerial images, historic road and insurance maps, and GIS sources are used to generate spatial configuration and building data for each study time period. These data are then processed and analysed to identify statistical and map-pattern morphological and syntactic relationships. It is concluded that the resulting database is capable of identifying and successfully investigating relationships between predictor and outcome variables such as global integration and building demolitions – both in the aggregate and in bivariate patterns. Further, this methodological approach should provide a rich set of prediction tools for urban designers and planners.","shortTitle":"Suburban change","language":"en","author":[{"family":"Hallowell","given":"George D."},{"family":"Baran","given":"Perver K."}],"issued":{"date-parts":[["2013",8,5]]},"accessed":{"date-parts":[["2014",9,23]]}}},{"id":105,"uris":[""],"uri":[""],"itemData":{"id":105,"type":"article-journal","title":"Forty years of urban expansion in Beijing: What is the relative importance of?physical, socioeconomic, and neighborhood factors?","container-title":"Applied Geography","page":"1-10","volume":"38","source":"ScienceDirect","abstract":"Urban expansion is one of the major causes of many ecological and environmental problems in urban areas and the surrounding regions. Understanding the process of urban expansion and its driving factors is crucial for urban growth planning and management to mitigate the adverse impacts of such growth. Previous studies have primarily been conducted from a static point of view by examining the process of urban expansion for only one or two time periods. Few studies have investigated the temporal dynamics of the effects of the driving factors in urban expansion. Using Beijing as a case study, this research aims to fill this gap. Urban expansion from 1972 to 2010 was detected from multi-temporal remote sensing images for four time periods. The effects of physical, socioeconomic, and neighborhood factors on urban expansion and their temporal dynamics were investigated using binary logistic regression. In addition, the relative importance of the three types of driving factors was examined using variance partitioning. The results showed that Beijing has undergone rapid and magnificent urban expansion in the past forty years. Physical, socioeconomic, and neighborhood factors have simultaneously affected this expansion. Socioeconomic factors were the most important driving force, except during the period of 1972–1984. In addition, the effects of these driving factors on urban expansion varied with time. The magnitude of the unique effects of physical factors and neighborhood factors declined while that of socioeconomic factors increased along with the urbanization process. The findings of this study can help us better understand the process of urban expansion and thus have important implications for urban planning and management in Beijing and similar cities.","DOI":"10.1016/j.apgeog.2012.11.004","ISSN":"0143-6228","shortTitle":"Forty years of urban expansion in Beijing","journalAbbreviation":"Applied Geography","author":[{"family":"Li","given":"Xiaoma"},{"family":"Zhou","given":"Weiqi"},{"family":"Ouyang","given":"Zhiyun"}],"issued":{"date-parts":[["2013",3]]},"accessed":{"date-parts":[["2014",6,25]]}}},{"id":251,"uris":[""],"uri":[""],"itemData":{"id":251,"type":"article-journal","title":"Spatiotemporal variation analysis of driving forces of urban land spatial expansion using logistic regression: A case study of port towns in Taicang City, China","container-title":"Habitat International","page":"181-190","volume":"43","source":"ScienceDirect","abstract":"Understanding the driving mechanisms of urban land spatial expansion (ULSE) is crucial for the guidance of rational urban land expansion. Previous studies have primarily focused on single large cities, with few explorations of the spatiotemporal differences in driving forces of ULSE of different towns in the same administrative region. This study aims to fill this gap. Three port towns of Taicang, located in China's Yangtze River Delta region, were taken as examples to analyze the expansion process of urban land during 1989–2008. Eight factors, including ecological suitability, prime croplands, etc., were selected from four aspects of natural eco-environment, land control policies, accessibility and neighborhood. Binary logistic regression was employed to investigate the effects of various factors on ULSE during various periods in different regions. Results reveal that over the past two decades, urban land expanded rapidly in the three towns, but with different expansion speeds and growth rates. Diversified ULSE factor combinations exist during different periods in different regions, and the factors' relative importance also varies with time and space. The four types of driving factors simultaneously affect ULSE, among which the accessibility is dominant. Based on the findings, we suggest that differentiated policies should be formulated to guide reasonable expansion of urban land. This study can help us better understand the driving mechanism of urban land expansion in small cities and towns, thus has important implications for urban planning and management in China and similar countries.","DOI":"10.1016/j.habitatint.2014.02.004","ISSN":"0197-3975","shortTitle":"Spatiotemporal variation analysis of driving forces of urban land spatial expansion using logistic regression","journalAbbreviation":"Habitat International","author":[{"family":"Shu","given":"Bangrong"},{"family":"Zhang","given":"Honghui"},{"family":"Li","given":"Yongle"},{"family":"Qu","given":"Yi"},{"family":"Chen","given":"Lihong"}],"issued":{"date-parts":[["2014",7]]},"accessed":{"date-parts":[["2014",6,25]]}}}],"schema":""} [e.g. 3, 9–17], numerous explanatory urban development drivers are proposed, which can be grouped into four main sets: accessibility indicators, geo-physical features, land-use policies and socio-economic factors. Accessibility indicators are often implemented in urban models by means of simple accessibility indicators, such as distance to cities, distance to the road network and distance to water bodies ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2i1lmgfirn","properties":{"formattedCitation":"[10, 12, 13]","plainCitation":"[10, 12, 13]"},"citationItems":[{"id":276,"uris":[""],"uri":[""],"itemData":{"id":276,"type":"article-journal","title":"Proximate causes of land-use change in Narok District, Kenya: a spatial statistical model","container-title":"Agriculture, Ecosystems & Environment","page":"65-81","volume":"85","issue":"1–3","source":"ScienceDirect","abstract":"This study attempts to identify how much understanding of the driving forces of land-use changes can be gained through a spatial, statistical analysis. Hereto, spatial, statistical models of the proximate causes of different processes of land-use change in the Mara Ecosystem (Kenya) were developed, taking into account the spatial variability of the land-use change processes. The descriptive spatial models developed here suggest some important factors driving the land-use changes that can be related to some well-established theoretical frameworks. The explanatory variables of the spatial model of mechanised agriculture suggest a von Thünen-like model, where conversion to agriculture is controlled by the distance to the market, as a proxy for transportation costs, and agro-climatic potential. Expansion of smallholder agriculture and settlements is also controlled by land rent, defined, in this case, by proximity to permanent water, land suitability, location near a tourism market, and vicinity to villages to gain access to social services (e.g. health clinics, schools, local markets). This difference in perception of land rent reflects the widely different social and economic activities and objectives of smallholders versus the large entrepreneurs involved in mechanised farming. Spatial heterogeneity as well as the variability in time of land-use change processes affect our ability to use regression models for wide ranging extrapolations. The models allow evaluating the impact of changes in driving forces that are well represented by proximate causes of land-use change.","DOI":"10.1016/S0167-8809(01)00188-8","ISSN":"0167-8809","shortTitle":"Proximate causes of land-use change in Narok District, Kenya","journalAbbreviation":"Agriculture, Ecosystems & Environment","author":[{"family":"Serneels","given":"Suzanne"},{"family":"Lambin","given":"Eric F"}],"issued":{"date-parts":[["2001",6]]},"accessed":{"date-parts":[["2014",7,2]]}}},{"id":210,"uris":[""],"uri":[""],"itemData":{"id":210,"type":"article-journal","title":"Spatial-Temporal Pattern and Driving Forces of Land Use Changes in Xiamen","container-title":"Pedosphere","page":"477-488","volume":"16","issue":"4","source":"ScienceDirect","abstract":"Using Landsat TM data of 1988, 1998 and 2001, the dynamic process of the spatial-temporal characteristics of land use changes during 13 years from 1988 to 2001 in the special economic zone of Xiamen, China was analyzed to improve understanding and to find the driving forces of land use change so that sustainable land utilization could be practiced. During the 13 years cropland decreased remarkably by nearly 11304.95 ha. The areas of rural-urban construction and water body increased by 10 152.24 ha and 848.94 ha, respectively. From 1988 to 2001, 52.5% of the lost cropland was converted into rural-urban industrial land. Rapid urbanization contributed to a great change in the rate of cropland land use during these years. Land-reclamation also contributed to a decrease in water body area as well as marine ecological and environmental destruction. In the study area 1) urbanization and industrialization, 2) infrastructure and agricultural intensification, 3) increased affluence of the farming community, and 4) policy factors have driven the land use changes. Possible sustainable land use measures included construction of a land management system, land planning, development of potential land resources, new technology applications, and marine ecological and environmental protection.","DOI":"10.1016/S1002-0160(06)60078-7","ISSN":"1002-0160","journalAbbreviation":"Pedosphere","author":[{"family":"QUAN","given":"Bin"},{"family":"CHEN","given":"Jian-Fei"},{"family":"QIU","given":"Hong-Lie"},{"family":"R?MKENS","given":"M. J. M."},{"family":"YANG","given":"Xiao-Qi"},{"family":"JIANG","given":"Shi-Feng"},{"family":"LI","given":"Bi-Cheng"}],"issued":{"date-parts":[["2006",8]]},"accessed":{"date-parts":[["2014",9,30]]}}},{"id":5,"uris":[""],"uri":[""],"itemData":{"id":5,"type":"article-journal","title":"Spatial determinants of urban land use change in Lagos, Nigeria","container-title":"Land Use Policy","page":"502-515","volume":"24","issue":"2","source":"ScienceDirect","abstract":"The objective of this research was to identify the factors responsible for residential and industrial/commercial land development in Lagos between 1984 and 2000. Land use changes were mapped using satellite images, while binary logistic regression was used to model the probability of observing urban development as a function of spatially explicit independent variables. Accessibility, spatial interaction effects and policy variables were the major determinants of land use change. Variables that influenced residential development were not necessarily those responsible for the expansion of industrial/commercial land areas. The evidence of frontier residential development calls for land tenure and housing development reforms, and land use controls to minimize the environmental consequences of unplanned urban expansion.","DOI":"10.1016/j.landusepol.2006.09.001","ISSN":"0264-8377","journalAbbreviation":"Land Use Policy","author":[{"family":"Braimoh","given":"Ademola K."},{"family":"Onishi","given":"Takashi"}],"issued":{"date-parts":[["2007",4]]},"accessed":{"date-parts":[["2014",6,25]]}}}],"schema":""} [10, 12, 13]. Road infrastructure consumes a high amount of urban land, around 25% of the total urban area in Europe and 30% in the USA ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"21r2u5rl9m","properties":{"formattedCitation":"[18]","plainCitation":"[18]"},"citationItems":[{"id":188,"uris":[""],"uri":[""],"itemData":{"id":188,"type":"article-journal","title":"Urban mobility and urban form: the social and environmental costs of different patterns of urban expansion","container-title":"Ecological Economics","page":"199-216","volume":"40","issue":"2","source":"ScienceDirect","abstract":"The question of the environmental or social costs of urban form is increasingly attracting attention in spatial policy, but scientific debate in this field is often marred by prejudices and abstract visions; empirical analyses are very rare. The present study aims at establishing, in the metropolitan area of Milan, whether different patterns of urban expansion could be associated with specific environmental costs—in particular, for land consumption and mobility generation. Different typologies of urban expansion were defined, and an impact index weighting differently journey-to-work trips with reference to mode and time length was built at the municipality level. The statistical analysis confirmed the expected “wasteful” character of sprawling development patterns in terms of land consumption, though suggesting that recent urban development is becoming relatively ‘virtuous’ with respect to the past. With reference to the mobility generated, higher environmental impacts were proved to be associated with low densities, sprawling development, more recent urbanisation processes and residential specialisation of the single municipalities. Public transport seems to be strongly influenced, both in terms of efficiency and competitiveness, by the structural organisation of an urban area: the more dispersed and less structured the development, the lower its level of efficiency and competitiveness and consequently its share of the mobility market. On the contrary, trip times for private transport appear to be correlated not so much to urban dimension or density as to the presence of recent housing development, indicating the emergence of new models of lifestyle and mobility which are very different from those of the past.","DOI":"10.1016/S0921-8009(01)00254-3","ISSN":"0921-8009","shortTitle":"Urban mobility and urban form","journalAbbreviation":"Ecological Economics","author":[{"family":"Camagni","given":"Roberto"},{"family":"Gibelli","given":"Maria Cristina"},{"family":"Rigamonti","given":"Paolo"}],"issued":{"date-parts":[["2002",2]]},"accessed":{"date-parts":[["2014",11,1]]}}}],"schema":""} [18]. In this paper, we considered Euclidean?distances to different roads categories and to major 11 Belgian cities.Geo-physical factors are reported as a fundamental driver of the spatial distribution and expansion of urban areas ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1o4ougf1kt","properties":{"formattedCitation":"[3]","plainCitation":"[3]"},"citationItems":[{"id":105,"uris":[""],"uri":[""],"itemData":{"id":105,"type":"article-journal","title":"Forty years of urban expansion in Beijing: What is the relative importance of?physical, socioeconomic, and neighborhood factors?","container-title":"Applied Geography","page":"1-10","volume":"38","source":"ScienceDirect","abstract":"Urban expansion is one of the major causes of many ecological and environmental problems in urban areas and the surrounding regions. Understanding the process of urban expansion and its driving factors is crucial for urban growth planning and management to mitigate the adverse impacts of such growth. Previous studies have primarily been conducted from a static point of view by examining the process of urban expansion for only one or two time periods. Few studies have investigated the temporal dynamics of the effects of the driving factors in urban expansion. Using Beijing as a case study, this research aims to fill this gap. Urban expansion from 1972 to 2010 was detected from multi-temporal remote sensing images for four time periods. The effects of physical, socioeconomic, and neighborhood factors on urban expansion and their temporal dynamics were investigated using binary logistic regression. In addition, the relative importance of the three types of driving factors was examined using variance partitioning. The results showed that Beijing has undergone rapid and magnificent urban expansion in the past forty years. Physical, socioeconomic, and neighborhood factors have simultaneously affected this expansion. Socioeconomic factors were the most important driving force, except during the period of 1972–1984. In addition, the effects of these driving factors on urban expansion varied with time. The magnitude of the unique effects of physical factors and neighborhood factors declined while that of socioeconomic factors increased along with the urbanization process. The findings of this study can help us better understand the process of urban expansion and thus have important implications for urban planning and management in Beijing and similar cities.","DOI":"10.1016/j.apgeog.2012.11.004","ISSN":"0143-6228","shortTitle":"Forty years of urban expansion in Beijing","journalAbbreviation":"Applied Geography","author":[{"family":"Li","given":"Xiaoma"},{"family":"Zhou","given":"Weiqi"},{"family":"Ouyang","given":"Zhiyun"}],"issued":{"date-parts":[["2013",3]]},"accessed":{"date-parts":[["2014",6,25]]}}}],"schema":""} [3]. There is often a relationship between urban growth and a number of these factors, especially the topography of the study area ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"KKwiR2Mf","properties":{"formattedCitation":"[3, 19]","plainCitation":"[3, 19]"},"citationItems":[{"id":326,"uris":[""],"uri":[""],"itemData":{"id":326,"type":"article-journal","title":"Spatio-temporal dynamics in the flood exposure due to land use changes in the Alpine Lech Valley in Tyrol (Austria)","container-title":"Natural Hazards","page":"1243-1270","volume":"68","issue":"3","source":"link.","abstract":"Flood risk is expected to increase in many regions of the world in the next decades with rising flood losses as a consequence. First and foremost, it can be attributed to the expansion of settlement and industrial areas into flood plains and the resulting accumulation of assets. For a future-oriented and a more robust flood risk management, it is therefore of importance not only to estimate potential impacts of climate change on the flood hazard, but also to analyze the spatio-temporal dynamics of flood exposure due to land use changes. In this study, carried out in the Alpine Lech Valley in Tyrol (Austria), various land use scenarios until 2030 were developed by means of a spatially explicit land use model, national spatial planning scenarios and current spatial policies. The combination of the simulated land use patterns with different inundation scenarios enabled us to derive statements about possible future changes in flood-exposed built-up areas. The results indicate that the potential assets at risk depend very much on the selected socioeconomic scenario. The important conditions affecting the potential assets at risk that differ between the scenarios are the demand for new built-up areas as well as on the types of conversions allowed to provide the necessary areas at certain locations. The range of potential changes in flood-exposed residential areas varies from no further change in the most moderate scenario ‘Overall Risk’ to 119 % increase in the most extreme scenario ‘Overall Growth’ (under current spatial policy) and 159 % increase when disregarding current building restrictions.","DOI":"10.1007/s11069-012-0280-8","ISSN":"0921-030X, 1573-0840","journalAbbreviation":"Nat Hazards","language":"en","author":[{"family":"Cammerer","given":"Holger"},{"family":"Thieken","given":"Annegret H."},{"family":"Verburg","given":"Peter H."}],"issued":{"date-parts":[["2013",9,1]]},"accessed":{"date-parts":[["2014",11,22]]}}},{"id":105,"uris":[""],"uri":[""],"itemData":{"id":105,"type":"article-journal","title":"Forty years of urban expansion in Beijing: What is the relative importance of?physical, socioeconomic, and neighborhood factors?","container-title":"Applied Geography","page":"1-10","volume":"38","source":"ScienceDirect","abstract":"Urban expansion is one of the major causes of many ecological and environmental problems in urban areas and the surrounding regions. Understanding the process of urban expansion and its driving factors is crucial for urban growth planning and management to mitigate the adverse impacts of such growth. Previous studies have primarily been conducted from a static point of view by examining the process of urban expansion for only one or two time periods. Few studies have investigated the temporal dynamics of the effects of the driving factors in urban expansion. Using Beijing as a case study, this research aims to fill this gap. Urban expansion from 1972 to 2010 was detected from multi-temporal remote sensing images for four time periods. The effects of physical, socioeconomic, and neighborhood factors on urban expansion and their temporal dynamics were investigated using binary logistic regression. In addition, the relative importance of the three types of driving factors was examined using variance partitioning. The results showed that Beijing has undergone rapid and magnificent urban expansion in the past forty years. Physical, socioeconomic, and neighborhood factors have simultaneously affected this expansion. Socioeconomic factors were the most important driving force, except during the period of 1972–1984. In addition, the effects of these driving factors on urban expansion varied with time. The magnitude of the unique effects of physical factors and neighborhood factors declined while that of socioeconomic factors increased along with the urbanization process. The findings of this study can help us better understand the process of urban expansion and thus have important implications for urban planning and management in Beijing and similar cities.","DOI":"10.1016/j.apgeog.2012.11.004","ISSN":"0143-6228","shortTitle":"Forty years of urban expansion in Beijing","journalAbbreviation":"Applied Geography","author":[{"family":"Li","given":"Xiaoma"},{"family":"Zhou","given":"Weiqi"},{"family":"Ouyang","given":"Zhiyun"}],"issued":{"date-parts":[["2013",3]]},"accessed":{"date-parts":[["2014",6,25]]}}}],"schema":""} [3, 19]. We considered elevation and slope as geo-physical factors in our study.Zoning status (policies) is often considered as one of the major urban development drivers worldwide. It has been classified as the most pervasive driver in USA ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"20bkmt248u","properties":{"formattedCitation":"[20]","plainCitation":"[20]"},"citationItems":[{"id":132,"uris":[""],"uri":[""],"itemData":{"id":132,"type":"book","title":"Lectures on Urban Economics","publisher":"MIT Press","number-of-pages":"285","source":"Google Books","abstract":"Lectures on Urban Economics offers a rigorous but nontechnical treatment of major topics in urban economics. To make the book accessible to a broad range of readers, the analysis is diagrammatic rather than mathematical. Although nontechnical, the book relies on rigorous economic reasoning. In contrast to the cursory theoretical development often found in other textbooks, Lectures on Urban Economics offers thorough and exhaustive treatments of models relevant to each topic, with the goal of revealing the logic of economic reasoning while also teaching urban economics. Topics covered include reasons for the existence of cities, urban spatial structure, urban sprawl and land-use controls, freeway congestion, housing demand and tenure choice, housing policies, local public goods and services, pollution, crime, and quality of life. Footnotes throughout the book point to relevant exercises, which appear at the back of the book. These 22 extended exercises (containing 125 individual parts) develop numerical examples based on the models analyzed in the chapters. Lectures on Urban Economics is suitable for undergraduate use, as background reading for graduate students, or as a professional reference for economists and scholars interested in the urban economics perspective.","ISBN":"9780262016360","language":"en","author":[{"family":"Brueckner","given":"Jan K."}],"issued":{"date-parts":[["2011"]]}}}],"schema":""} [20]. In Wallonia, land allocation is controlled by several regulations including the regional development plan, referred to as "plan de secteur (PDS)". In this paper, we consider this zoning plan, which defines the legally authorized land-use type for all the territory.This study also selects a number of socio-economic factors. Population is one of the most active driver of urban development ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1anoooimb1","properties":{"formattedCitation":"[15]","plainCitation":"[15]"},"citationItems":[{"id":65,"uris":[""],"uri":[""],"itemData":{"id":65,"type":"article-journal","title":"Analysis to driving forces of land use change in Lu'an mining area","container-title":"Transactions of Nonferrous Metals Society of China","page":"s727-s732","volume":"21, Supplement 3","source":"ScienceDirect","abstract":"By selecting impact factors of driving force and formulating evaluation criteria of the impacts, the evaluation system of corresponding driving force impact of land use change was established. Taking Lu'an mining area as an example, the specific impact factors of coal mine were comprehensively evaluated and analyzed in order to carry out qualitative and quantitative analysis for the driving force of mining-land use change. The principal component analysis shows that the social and economic development in mining area from 2000 to 2007 demonstrates continuous accelerate trends, and the impacts of its overall driving force to land use change are increased gradually. The socio-economic factors have more impacts to mining-land use change than those of the natural resources. The main driving force of mining-land use change also include population, technological progress and policy.","DOI":"10.1016/S1003-6326(12)61670-7","ISSN":"1003-6326","journalAbbreviation":"Transactions of Nonferrous Metals Society of China","author":[{"family":"Liu","given":"Chang-hua"},{"family":"Ma","given":"Xiao-xiao"}],"issued":{"date-parts":[["2011",12]]},"accessed":{"date-parts":[["2014",9,30]]}}}],"schema":""} [15]. In this respect, the evolution of net and gross population densities was considered. Economic development could also be considered as a driver of urban development; there is a relation between economic increase and urban development ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"82cadhb03","properties":{"formattedCitation":"[15]","plainCitation":"[15]"},"citationItems":[{"id":65,"uris":[""],"uri":[""],"itemData":{"id":65,"type":"article-journal","title":"Analysis to driving forces of land use change in Lu'an mining area","container-title":"Transactions of Nonferrous Metals Society of China","page":"s727-s732","volume":"21, Supplement 3","source":"ScienceDirect","abstract":"By selecting impact factors of driving force and formulating evaluation criteria of the impacts, the evaluation system of corresponding driving force impact of land use change was established. Taking Lu'an mining area as an example, the specific impact factors of coal mine were comprehensively evaluated and analyzed in order to carry out qualitative and quantitative analysis for the driving force of mining-land use change. The principal component analysis shows that the social and economic development in mining area from 2000 to 2007 demonstrates continuous accelerate trends, and the impacts of its overall driving force to land use change are increased gradually. The socio-economic factors have more impacts to mining-land use change than those of the natural resources. The main driving force of mining-land use change also include population, technological progress and policy.","DOI":"10.1016/S1003-6326(12)61670-7","ISSN":"1003-6326","journalAbbreviation":"Transactions of Nonferrous Metals Society of China","author":[{"family":"Liu","given":"Chang-hua"},{"family":"Ma","given":"Xiao-xiao"}],"issued":{"date-parts":[["2011",12]]},"accessed":{"date-parts":[["2014",9,30]]}}}],"schema":""} [15] and furthermore economic development has an important influence on people's location choices. In this respect, employment potential, richness level, housing and land prices are considered. The number of households is another factor to be considered in this paper. This number may rely on population lifecycle, migration, societal values, gender relationships and the relationships between parents and children.Methodology Study areaThe study area is Wallonia, occupying the southern part of Belgium (Fig. 1). Wallonia is the predominantly?French-speaking region of?Belgium. It has a territory of 16,844 km? that makes up 55% of the territory of Belgium but with only a third of its population. The population volume in 2010 was 3,498,384 inhabitants ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"12s60a6kjh","properties":{"formattedCitation":"[21]","plainCitation":"[21]"},"citationItems":[{"id":416,"uris":[""],"uri":[""],"itemData":{"id":416,"type":"webpage","title":"Population","container-title":"Statistics Belgium","URL":"","author":[{"family":"Belgian Federal Government","given":""}],"issued":{"date-parts":[["2013"]]},"accessed":{"date-parts":[["2014",4,19]]}}}],"schema":""} [21]. Administratively, it comprises five provinces: Hainaut, Liège, Luxembourg, Namur, and Walloon Brabant. It has 20 administrative arrondissements and 262 municipalities. The geography of the area goes from flat to hilly with altitude ranges from 0 to 693 m above see-level. Over the last decades, land-use change in Wallonia was related to urban growth and a subsequent loss of agricultural land.Major cities in Wallonia are characterized by a strong center–periphery structure with well-off households located in the peripheries ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"dd6r4v2ml","properties":{"formattedCitation":"[22]","plainCitation":"[22]"},"citationItems":[{"id":390,"uris":[""],"uri":[""],"itemData":{"id":390,"type":"article-journal","title":"Commuting in Belgian metropolitan areas: The power of the Alonso-Muth model","container-title":"Journal of Transport and Land Use","volume":"2","issue":"3","source":"","abstract":"In order to understand patterns of urban commuter flows, insight is required into urban spatial structure (and vice versa). The present contribution first provides a concise overview of the theoretical perspectives from which economists and geographers approach commuting issues. Subsequently, the focus shifts to the classical spatial-economic urban models and how they explain commuter movements. We conduct a number of cluster analyses from which we are able to derive a commuting typology of city region areas. We conclude that distance (which also comprises journey time and proximity of traffic infrastructure), housing characteristics, housing environment, and income continue to play key roles in commuting patterns in the metropolitan areas under consideration.","URL":"","DOI":"10.5198/jtlu.v2i3.19","ISSN":"1938-7849","shortTitle":"Commuting in Belgian metropolitan areas","language":"en","author":[{"family":"Verhetsel","given":"Ann"},{"family":"Thomas","given":"Isabelle"},{"family":"Beelen","given":"Marjan"}],"issued":{"date-parts":[["2010",1,25]]},"accessed":{"date-parts":[["2015",3,21]]}}}],"schema":""} [22]. The main metropolitan areas are Charleroi, Liège, Mons and Namur. They are all characterized by a historical city-center around which the urban development was spread. Urban sprawl has affected Wallonia for decades leading to fragmented and isolated landscapes that were developed in space and time ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"14itbint53","properties":{"formattedCitation":"[23]","plainCitation":"[23]"},"citationItems":[{"id":393,"uris":[""],"uri":[""],"itemData":{"id":393,"type":"article-journal","title":"Landscape change and the urbanization process in Europe","container-title":"Landscape and Urban Planning","collection-title":"Development of European Landscapes","page":"9-26","volume":"67","issue":"1–4","source":"ScienceDirect","abstract":"Urbanization is one of the fundamental characteristics of the European civilization. It gradually spread from Southeast Europe around 700 b.c., across the whole continent. Cities and the urban networks they formed were always an important factor in the development and shaping of their surrounding regions. Polarization of territory between urban and rural and accessibility are still important aspects in landscape dynamics. Urbanization and its associated transportation infrastructure define the relationship between city and countryside. Urbanization, expressed as the proportion of people living in urban places shows a recent but explosive growth reaching values around 80% in most European countries. Simultaneously the countryside becomes abandoned. Thinking, valuing and planning the countryside is done mainly by urbanites and future rural development is mainly focused upon the urban needs. Thinking of urban places with their associated rural hinterland and spheres of influence has become complex. Clusters of urban places, their situation in a globalizing world and changing accessibility for fast transportation modes are some new factors that affect the change of traditional European cultural landscapes. Urbanization processes show cycles of evolution that spread in different ways through space. Urbanization phases developed at different speeds and time between Northern and Southern Europe. Main cities are affected first, but gradually urbanization processes affect smaller settlements and even remote rural villages. Functional urban regions (FURs) are a new concept, which is also significant for landscape ecologists. Local landscape change can only be comprehended when situated in its general geographical context and with all its related dynamics. Patterns of change are different for the countryside near major cities, for metropolitan villages and for remote rural villages. Planning and designing landscapes for the future requires that this is understood. Urbanized landscapes are highly dynamic, complex and multifunctional. Therefore, detailed inventories of landscape conditions and monitoring of change are urgently needed in order to obtain reliable data for good decision-making.","DOI":"10.1016/S0169-2046(03)00026-4","ISSN":"0169-2046","journalAbbreviation":"Landscape and Urban Planning","author":[{"family":"Antrop","given":"Marc"}],"issued":{"date-parts":[["2004",3,15]]},"accessed":{"date-parts":[["2015",3,21]]}}}],"schema":""} [23]. Wallonia is highly affected by its neighbors especially by the trans-border workers number evolution.Fig. 1. Study areaMultinomial logistic regression modelAn MNL model was applied to investigate the contribution of the selected driving forces on the probability of urban development along different densities. MNL consists of three components: multi-temporal urban maps, a multivariate function of the hypothesized drivers of change, and the resulting prediction map of urban development potential ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1b7ikdcodu","properties":{"formattedCitation":"[24]","plainCitation":"[24]"},"citationItems":[{"id":400,"uris":[""],"uri":[""],"itemData":{"id":400,"type":"book","title":"Modelling Deforestation Process - A review - Trees Tropical Ecosystem Environment Observations by Satellites -","publisher":"European Commission Luxembourg","number-of-pages":"113","source":"Amazon","language":"English","author":[{"family":"Lambin","given":"Eric F."}],"editor":[{"family":"Centre","given":"European Comission Joint Research"},{"family":"ESA","given":""}],"issued":{"date-parts":[["1994"]]}}}],"schema":""} [24]. MNL analysis yields coefficients for each X. These coefficients are then interpreted as weights in a formula that generates a map for each urban density class depicting the probability of each cell in the landscape to be converted into this class. If the Y variable is a categorical map with k classes, taking on values 0, 1,..., k-1 and X is a set of explanatory variables X1, X2,..., Xn then the logit for each non-reference class k1,..., kn against the reference class k0 model is calculated through: (1)where log(k) is a logit function of class k against the reference class, α is the intercept, β is the regression coefficients of class k. The conditional probabilities of each class can be calculated with the following formula: (2)The goodness-of-fit, in terms of predictive ability and the interpretability, of the MNL outcomes can be evaluated using McFadden pseudo R-square (MFR2) and Relative Operating Characteristic (ROC) statistic respectively ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"29hsq6s2fr","properties":{"formattedCitation":"[5, 13, 17, 25, 26]","plainCitation":"[5, 13, 17, 25, 26]"},"citationItems":[{"id":375,"uris":[""],"uri":[""],"itemData":{"id":375,"type":"book","title":"Statistical Methods for Geographers","publisher":"Wiley","publisher-place":"New York","number-of-pages":"528","edition":"1 edition","source":"","event-place":"New York","abstract":"A textbook for advanced undergraduate/first year graduate level courses in statistical methods in geography. Presents methods useful in research design, hypothesis testing, and analyzing spatial and functional relationships. Introduces basic statistical terms and techniques for displaying and describing distributions, and covers a range of working methods including probability and sampling, simple linear regression, extensions of the simple linear model to multiple regression and its assumptions, stepwise logit regression, and canonical and discriminant analysis.","ISBN":"9780471818076","language":"English","author":[{"family":"Clark","given":"W. A. V."},{"family":"Hosking","given":"P. L."}],"issued":{"date-parts":[["1986",4,4]]}}},{"id":335,"uris":[""],"uri":[""],"itemData":{"id":335,"type":"article-journal","title":"Measuring the Effect of Stochastic Perturbation Component in Cellular Automata Urban Growth Model","container-title":"Procedia Environmental Sciences","collection-title":"12th International Conference on Design and Decision Support Systems in Architecture and Urban Planning, DDSS 2014","page":"156-168","volume":"22","source":"ScienceDirect","abstract":"Urban environments are complex dynamic systems whose prediction of the future states cannot exclusively rely on deterministic rules. Although several studies on urban growth were carried out using different modelling approaches, the measurement of uncertainties was commonly neglected in these studies. This paper investigates the effect of uncertainty in urban growth models by introducing a stochastic perturbation method. A cellular automaton is used to simulate predicted urban growth. The effect of stochastic perturbation is addressed by comparing series of urban growth simulations based on different degree of stochastic perturbation randomness with the original urban growth simulation, obtained with the sole cellular automata neighbouring effects. These simulations are evaluated using cell-to-cell location agreement and a number of spatial metrics. The model framework has been applied to the Ourthe river basin in Belgium. The results show that the accuracy of the model is increased by introducing a stochastic perturbation component with a limited degree of randomness, in the cellular automata urban growth model.","DOI":"10.1016/j.proenv.2014.11.016","ISSN":"1878-0296","journalAbbreviation":"Procedia Environmental Sciences","author":[{"family":"Mustafa","given":"Ahmed"},{"family":"Saadi","given":"Isma?l"},{"family":"Cools","given":"Mario"},{"family":"Teller","given":"Jacques"}],"issued":{"date-parts":[["2014"]]},"accessed":{"date-parts":[["2014",12,17]]}}},{"id":5,"uris":[""],"uri":[""],"itemData":{"id":5,"type":"article-journal","title":"Spatial determinants of urban land use change in Lagos, Nigeria","container-title":"Land Use Policy","page":"502-515","volume":"24","issue":"2","source":"ScienceDirect","abstract":"The objective of this research was to identify the factors responsible for residential and industrial/commercial land development in Lagos between 1984 and 2000. Land use changes were mapped using satellite images, while binary logistic regression was used to model the probability of observing urban development as a function of spatially explicit independent variables. Accessibility, spatial interaction effects and policy variables were the major determinants of land use change. Variables that influenced residential development were not necessarily those responsible for the expansion of industrial/commercial land areas. The evidence of frontier residential development calls for land tenure and housing development reforms, and land use controls to minimize the environmental consequences of unplanned urban expansion.","DOI":"10.1016/j.landusepol.2006.09.001","ISSN":"0264-8377","journalAbbreviation":"Land Use Policy","author":[{"family":"Braimoh","given":"Ademola K."},{"family":"Onishi","given":"Takashi"}],"issued":{"date-parts":[["2007",4]]},"accessed":{"date-parts":[["2014",6,25]]}}},{"id":282,"uris":[""],"uri":[""],"itemData":{"id":282,"type":"article-journal","title":"Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use?","container-title":"Frontiers of Earth Science","page":"1-12","source":"link.","abstract":"Spatially explicit simulation of land use change is the basis for estimating the effects of land use and cover change on energy fluxes, ecology and the environment. At the pixel level, logistic regression is one of the most common approaches used in spatially explicit land use allocation models to determine the relationship between land use and its causal factors in driving land use change, and thereby to evaluate land use suitability. However, these models have a drawback in that they do not determine/allocate land use based on the direct relationship between land use change and its driving factors. Consequently, a multinomial logistic regression method was introduced to address this flaw, and thereby, judge the suitability of a type of land use in any given pixel in a case study area of the Jiangxi Province, China. A comparison of the two regression methods indicated that the proportion of correctly allocated pixels using multinomial logistic regression was 92.98%, which was 8.47% higher than that obtained using logistic regression. Paired t-test results also showed that pixels were more clearly distinguished by multinomial logistic regression than by logistic regression. In conclusion, multinomial logistic regression is a more efficient and accurate method for the spatial allocation of land use changes. The application of this method in future land use change studies may improve the accuracy of predicting the effects of land use and cover change on energy fluxes, ecology, and environment.","DOI":"10.1007/s11707-014-0426-y","ISSN":"2095-0195, 2095-0209","shortTitle":"Comparison of multinomial logistic regression and logistic regression","journalAbbreviation":"Front. Earth Sci.","language":"en","author":[{"family":"Lin","given":"Yingzhi"},{"family":"Deng","given":"Xiangzheng"},{"family":"Li","given":"Xing"},{"family":"Ma","given":"Enjun"}],"issued":{"date-parts":[["2014",5,5]]},"accessed":{"date-parts":[["2014",9,30]]}}},{"id":251,"uris":[""],"uri":[""],"itemData":{"id":251,"type":"article-journal","title":"Spatiotemporal variation analysis of driving forces of urban land spatial expansion using logistic regression: A case study of port towns in Taicang City, China","container-title":"Habitat International","page":"181-190","volume":"43","source":"ScienceDirect","abstract":"Understanding the driving mechanisms of urban land spatial expansion (ULSE) is crucial for the guidance of rational urban land expansion. Previous studies have primarily focused on single large cities, with few explorations of the spatiotemporal differences in driving forces of ULSE of different towns in the same administrative region. This study aims to fill this gap. Three port towns of Taicang, located in China's Yangtze River Delta region, were taken as examples to analyze the expansion process of urban land during 1989–2008. Eight factors, including ecological suitability, prime croplands, etc., were selected from four aspects of natural eco-environment, land control policies, accessibility and neighborhood. Binary logistic regression was employed to investigate the effects of various factors on ULSE during various periods in different regions. Results reveal that over the past two decades, urban land expanded rapidly in the three towns, but with different expansion speeds and growth rates. Diversified ULSE factor combinations exist during different periods in different regions, and the factors' relative importance also varies with time and space. The four types of driving factors simultaneously affect ULSE, among which the accessibility is dominant. Based on the findings, we suggest that differentiated policies should be formulated to guide reasonable expansion of urban land. This study can help us better understand the driving mechanism of urban land expansion in small cities and towns, thus has important implications for urban planning and management in China and similar countries.","DOI":"10.1016/j.habitatint.2014.02.004","ISSN":"0197-3975","shortTitle":"Spatiotemporal variation analysis of driving forces of urban land spatial expansion using logistic regression","journalAbbreviation":"Habitat International","author":[{"family":"Shu","given":"Bangrong"},{"family":"Zhang","given":"Honghui"},{"family":"Li","given":"Yongle"},{"family":"Qu","given":"Yi"},{"family":"Chen","given":"Lihong"}],"issued":{"date-parts":[["2014",7]]},"accessed":{"date-parts":[["2014",6,25]]}}}],"schema":""} [e.g. 5, 13, 17, 25, 26].MFR2 tries to mimic the R-squared analysis of linear regression. An MFR2 of 1 indicates a perfect fit, where MFR2 of 0 indicates no relationship. Clark & Hosking ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2b2nsnlo34","properties":{"formattedCitation":"[25]","plainCitation":"[25]"},"citationItems":[{"id":375,"uris":[""],"uri":[""],"itemData":{"id":375,"type":"book","title":"Statistical Methods for Geographers","publisher":"Wiley","publisher-place":"New York","number-of-pages":"528","edition":"1 edition","source":"","event-place":"New York","abstract":"A textbook for advanced undergraduate/first year graduate level courses in statistical methods in geography. Presents methods useful in research design, hypothesis testing, and analyzing spatial and functional relationships. Introduces basic statistical terms and techniques for displaying and describing distributions, and covers a range of working methods including probability and sampling, simple linear regression, extensions of the simple linear model to multiple regression and its assumptions, stepwise logit regression, and canonical and discriminant analysis.","ISBN":"9780471818076","language":"English","author":[{"family":"Clark","given":"W. A. V."},{"family":"Hosking","given":"P. L."}],"issued":{"date-parts":[["1986",4,4]]}}}],"schema":""} [25] stated that a MFR2 greater than 0.2 can be considered a good fit and it is calculated according to the following formula: (3)where Lm is the value of the likelihood function for the full model as fitted with X and L0 is the value of the likelihood function if all β except α are 0.The ROC statistic compares the probability map, produced by MNL, to a map with the observed changes of the urban cells for each class between two time-steps. It first divides the probability outcomes into percentile groups from high to low probability and then calculates the proportion of true-positives and false-positives for a range of specified threshold values and relates them to each other in a graph. The ROC measures the area under the curve and its value could range between 0.5 (no relationship) and 1 (perfect fit). ROC values higher than 0.70 are considered as a reasonable fit ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"S9ifBauW","properties":{"formattedCitation":"[19, 27, 28]","plainCitation":"[19, 27, 28]"},"citationItems":[{"id":326,"uris":[""],"uri":[""],"itemData":{"id":326,"type":"article-journal","title":"Spatio-temporal dynamics in the flood exposure due to land use changes in the Alpine Lech Valley in Tyrol (Austria)","container-title":"Natural Hazards","page":"1243-1270","volume":"68","issue":"3","source":"link.","abstract":"Flood risk is expected to increase in many regions of the world in the next decades with rising flood losses as a consequence. First and foremost, it can be attributed to the expansion of settlement and industrial areas into flood plains and the resulting accumulation of assets. For a future-oriented and a more robust flood risk management, it is therefore of importance not only to estimate potential impacts of climate change on the flood hazard, but also to analyze the spatio-temporal dynamics of flood exposure due to land use changes. In this study, carried out in the Alpine Lech Valley in Tyrol (Austria), various land use scenarios until 2030 were developed by means of a spatially explicit land use model, national spatial planning scenarios and current spatial policies. The combination of the simulated land use patterns with different inundation scenarios enabled us to derive statements about possible future changes in flood-exposed built-up areas. The results indicate that the potential assets at risk depend very much on the selected socioeconomic scenario. The important conditions affecting the potential assets at risk that differ between the scenarios are the demand for new built-up areas as well as on the types of conversions allowed to provide the necessary areas at certain locations. The range of potential changes in flood-exposed residential areas varies from no further change in the most moderate scenario ‘Overall Risk’ to 119 % increase in the most extreme scenario ‘Overall Growth’ (under current spatial policy) and 159 % increase when disregarding current building restrictions.","DOI":"10.1007/s11069-012-0280-8","ISSN":"0921-030X, 1573-0840","journalAbbreviation":"Nat Hazards","language":"en","author":[{"family":"Cammerer","given":"Holger"},{"family":"Thieken","given":"Annegret H."},{"family":"Verburg","given":"Peter H."}],"issued":{"date-parts":[["2013",9,1]]},"accessed":{"date-parts":[["2014",11,22]]}}},{"id":259,"uris":[""],"uri":[""],"itemData":{"id":259,"type":"book","title":"Applied Logistic Regression","publisher":"John Wiley & Sons","number-of-pages":"397","source":"Google Books","abstract":"From the reviews of the First Edition.\"An interesting, useful, and well-written book on logistic regression models . . . Hosmer and Lemeshow have used very little mathematics, have presented difficult concepts heuristically and through illustrative examples, and have included references.\"—Choice\"Well written, clearly organized, and comprehensive . . . the authors carefully walk the reader through the estimation of interpretation of coefficients from a wide variety of logistic regression models . . . their careful explication of the quantitative re-expression of coefficients from these various models is excellent.\"—Contemporary Sociology\"An extremely well-written book that will certainly prove an invaluable acquisition to the practicing statistician who finds other literature on analysis of discrete data hard to follow or heavily theoretical.\"—The StatisticianIn this revised and updated edition of their popular book, David Hosmer and Stanley Lemeshow continue to provide an amazingly accessible introduction to the logistic regression model while incorporating advances of the last decade, including a variety of software packages for the analysis of data sets. Hosmer and Lemeshow extend the discussion from biostatistics and epidemiology to cutting-edge applications in data mining and machine learning, guiding readers step-by-step through the use of modeling techniques for dichotomous data in diverse fields. Ample new topics and expanded discussions of existing material are accompanied by a wealth of real-world examples-with extensive data sets available over the Internet.","ISBN":"9780471654025","language":"en","author":[{"family":"Jr","given":"David W. Hosmer"},{"family":"Lemeshow","given":"Stanley"}],"issued":{"date-parts":[["2004",10,28]]}}},{"id":289,"uris":[""],"uri":[""],"itemData":{"id":289,"type":"thesis","title":"Modelling urban expansion and its hydrological impacts","publisher":"Katholieke Universiteit Leuven","publisher-place":"Leuven","genre":"Unpublished PhD dissertation","event-place":"Leuven","language":"English","author":[{"family":"Poelmans","given":"Lien"}],"issued":{"date-parts":[["2010"]]}}}],"schema":""} [19, 27, 28].Prior to performing the MNL model, we have to consider three aspects that may exist in X: disparity in units, autocorrelation and multicollinearity. These aspects potentially affect any regression analysis results. Due to disparity in units and scale of X (table.1), the logit coefficients cannot be used directly to measure the relative contribution of the X in urban development process. Consequently, all continuous?X were standardized before performing MNL analysis according to the following formula: (4)where zi is the standardized score of cell i, xi is the original value, μ is the mean value of X variable and σ is the standard deviation of the X variable. z score is negative when the raw value is below the mean and positive when above. Categorical X were not standardized to keep the meaning of the dummy variable.Spatial autocorrelation in one or more X will bias the results of the regression analysis. Autocorrelation is the propensity for cell value to be similar to surrounding cells. Moran's I statistic was processed to detect spatial autocorrelation for each X. It is given as: (5)where M_I is the Moran's I statistic for each X, n is the number of neighbor cells to be taken into account, w spatial weights and xi/j cells values at location i/j. Locations depend on cell neighbors which can be considered as only shared-border neighbors to the cell under evaluation (xi) or also diagonal neighbors (xj). We considered only xj neighbors. Moran's I value ranges between -1 and +1, where +1 means absolute autocorrelation and -1 none autocorrelation. All X show strong degree of spatial autocorrelation with Moran's I value between 0.746 for zoning and 0.999 for distance to cities. A number of scholars ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"7oacmt6b3","properties":{"formattedCitation":"[3, 19, 29]","plainCitation":"[3, 19, 29]"},"citationItems":[{"id":99,"uris":[""],"uri":[""],"itemData":{"id":99,"type":"article-journal","title":"Complexity and performance of urban expansion models","container-title":"Computers, Environment and Urban Systems","page":"17-27","volume":"34","issue":"1","source":"ScienceDirect","abstract":"Urban expansion and spatial patterns of urban land have a large effect on many socioeconomic and environmental processes. A wide variety of modelling approaches has been introduced to predict and simulate future urban development. These models are often based on the interpretation of various determining factors that are used to create a probability map. The main objective of this paper is to evaluate the performance of different modelling approaches for simulating spatial patterns of urban expansion in Flanders and Brussels in the period 1988–2000. Hereto, a set of urban expansion models with increasing complexity was developed based on: (i) logistic regression equations taking various numbers of determining variables into account, (ii) CA transition rules and (iii) hybrid procedures, combining both approaches. The outcome of each model was validated in order to assess the predictive value of the three modelling approaches and of the different determining variables that were used in the logistic regression models. The results show that a hybrid model structure, integrating (static) determining factors (distance to the main roads, distance to the largest cities, employment potential, slope and zoning status of the land) and (dynamic) neighbourhood interactions produces the most accurate probability map. The study, however, points out that it is not useful to make a statement on the validity of a model based on only one goodness-of-fit measure. When the model results are validated at multiple resolutions, the logistic regression model, which incorporates only two explanatory variables, outperforms both the CA-based model and the hybrid model.","DOI":"10.1016/penvurbsys.2009.06.001","ISSN":"0198-9715","journalAbbreviation":"Computers, Environment and Urban Systems","author":[{"family":"Poelmans","given":"Lien"},{"family":"Van Rompaey","given":"Anton"}],"issued":{"date-parts":[["2010",1]]},"accessed":{"date-parts":[["2014",6,25]]}}},{"id":326,"uris":[""],"uri":[""],"itemData":{"id":326,"type":"article-journal","title":"Spatio-temporal dynamics in the flood exposure due to land use changes in the Alpine Lech Valley in Tyrol (Austria)","container-title":"Natural Hazards","page":"1243-1270","volume":"68","issue":"3","source":"link.","abstract":"Flood risk is expected to increase in many regions of the world in the next decades with rising flood losses as a consequence. First and foremost, it can be attributed to the expansion of settlement and industrial areas into flood plains and the resulting accumulation of assets. For a future-oriented and a more robust flood risk management, it is therefore of importance not only to estimate potential impacts of climate change on the flood hazard, but also to analyze the spatio-temporal dynamics of flood exposure due to land use changes. In this study, carried out in the Alpine Lech Valley in Tyrol (Austria), various land use scenarios until 2030 were developed by means of a spatially explicit land use model, national spatial planning scenarios and current spatial policies. The combination of the simulated land use patterns with different inundation scenarios enabled us to derive statements about possible future changes in flood-exposed built-up areas. The results indicate that the potential assets at risk depend very much on the selected socioeconomic scenario. The important conditions affecting the potential assets at risk that differ between the scenarios are the demand for new built-up areas as well as on the types of conversions allowed to provide the necessary areas at certain locations. The range of potential changes in flood-exposed residential areas varies from no further change in the most moderate scenario ‘Overall Risk’ to 119 % increase in the most extreme scenario ‘Overall Growth’ (under current spatial policy) and 159 % increase when disregarding current building restrictions.","DOI":"10.1007/s11069-012-0280-8","ISSN":"0921-030X, 1573-0840","journalAbbreviation":"Nat Hazards","language":"en","author":[{"family":"Cammerer","given":"Holger"},{"family":"Thieken","given":"Annegret H."},{"family":"Verburg","given":"Peter H."}],"issued":{"date-parts":[["2013",9,1]]},"accessed":{"date-parts":[["2014",11,22]]}}},{"id":105,"uris":[""],"uri":[""],"itemData":{"id":105,"type":"article-journal","title":"Forty years of urban expansion in Beijing: What is the relative importance of?physical, socioeconomic, and neighborhood factors?","container-title":"Applied Geography","page":"1-10","volume":"38","source":"ScienceDirect","abstract":"Urban expansion is one of the major causes of many ecological and environmental problems in urban areas and the surrounding regions. Understanding the process of urban expansion and its driving factors is crucial for urban growth planning and management to mitigate the adverse impacts of such growth. Previous studies have primarily been conducted from a static point of view by examining the process of urban expansion for only one or two time periods. Few studies have investigated the temporal dynamics of the effects of the driving factors in urban expansion. Using Beijing as a case study, this research aims to fill this gap. Urban expansion from 1972 to 2010 was detected from multi-temporal remote sensing images for four time periods. The effects of physical, socioeconomic, and neighborhood factors on urban expansion and their temporal dynamics were investigated using binary logistic regression. In addition, the relative importance of the three types of driving factors was examined using variance partitioning. The results showed that Beijing has undergone rapid and magnificent urban expansion in the past forty years. Physical, socioeconomic, and neighborhood factors have simultaneously affected this expansion. Socioeconomic factors were the most important driving force, except during the period of 1972–1984. In addition, the effects of these driving factors on urban expansion varied with time. The magnitude of the unique effects of physical factors and neighborhood factors declined while that of socioeconomic factors increased along with the urbanization process. The findings of this study can help us better understand the process of urban expansion and thus have important implications for urban planning and management in Beijing and similar cities.","DOI":"10.1016/j.apgeog.2012.11.004","ISSN":"0143-6228","shortTitle":"Forty years of urban expansion in Beijing","journalAbbreviation":"Applied Geography","author":[{"family":"Li","given":"Xiaoma"},{"family":"Zhou","given":"Weiqi"},{"family":"Ouyang","given":"Zhiyun"}],"issued":{"date-parts":[["2013",3]]},"accessed":{"date-parts":[["2014",6,25]]}}}],"schema":""} [e.g. 3, 19, 29] suggested that this problem could be addressed through a data sampling approach. A random sample of 15,675 cells, around 1.15% of the study area, distributed throughout the study area were used in the MNL model.Multicollinearity represents a high degree of dependency among a number of X. It commonly occurs when a large number of X are introduced in a regression model. It is because some of X may measure the same phenomena. Strong collinearities cause the erroneous estimation of parameters and further affect the MNL results ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"11pu0ruvcv","properties":{"formattedCitation":"[26]","plainCitation":"[26]"},"citationItems":[{"id":282,"uris":[""],"uri":[""],"itemData":{"id":282,"type":"article-journal","title":"Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use?","container-title":"Frontiers of Earth Science","page":"1-12","source":"link.","abstract":"Spatially explicit simulation of land use change is the basis for estimating the effects of land use and cover change on energy fluxes, ecology and the environment. At the pixel level, logistic regression is one of the most common approaches used in spatially explicit land use allocation models to determine the relationship between land use and its causal factors in driving land use change, and thereby to evaluate land use suitability. However, these models have a drawback in that they do not determine/allocate land use based on the direct relationship between land use change and its driving factors. Consequently, a multinomial logistic regression method was introduced to address this flaw, and thereby, judge the suitability of a type of land use in any given pixel in a case study area of the Jiangxi Province, China. A comparison of the two regression methods indicated that the proportion of correctly allocated pixels using multinomial logistic regression was 92.98%, which was 8.47% higher than that obtained using logistic regression. Paired t-test results also showed that pixels were more clearly distinguished by multinomial logistic regression than by logistic regression. In conclusion, multinomial logistic regression is a more efficient and accurate method for the spatial allocation of land use changes. The application of this method in future land use change studies may improve the accuracy of predicting the effects of land use and cover change on energy fluxes, ecology, and environment.","DOI":"10.1007/s11707-014-0426-y","ISSN":"2095-0195, 2095-0209","shortTitle":"Comparison of multinomial logistic regression and logistic regression","journalAbbreviation":"Front. Earth Sci.","language":"en","author":[{"family":"Lin","given":"Yingzhi"},{"family":"Deng","given":"Xiangzheng"},{"family":"Li","given":"Xing"},{"family":"Ma","given":"Enjun"}],"issued":{"date-parts":[["2014",5,5]]},"accessed":{"date-parts":[["2014",9,30]]}}}],"schema":""} [26]. In this context, a number of procedures is proposed to detect multicollinearity among X such as tolerance value, variance inflation factor and Belsley diagnostics ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1ipu4as60o","properties":{"formattedCitation":"{\\rtf [30\\uc0\\u8211{}33]}","plainCitation":"[30–33]"},"citationItems":[{"id":410,"uris":[""],"uri":[""],"itemData":{"id":410,"type":"book","title":"Regression Diagnostics","publisher":"John Wiley and Sons, New York","source":"Google Scholar","author":[{"family":"Belsley","given":"David A."},{"family":"Kuh","given":"Edwin"},{"family":"Welsh","given":"Roy E."}],"issued":{"date-parts":[["1980"]]}},"label":"page"},{"id":411,"uris":[""],"uri":[""],"itemData":{"id":411,"type":"book","title":"The Theory and Practice of Econometrics","publisher":"Wiley","publisher-place":"New York","number-of-pages":"1056","edition":"2 edition","source":"Amazon","event-place":"New York","abstract":"This broadly based graduate-level textbook covers the major models and statistical tools currently used in the practice of econometrics. It examines the classical, the decision theory, and the Bayesian approaches, and contains material on single equation and simultaneous equation econometric models. Includes an extensive reference list for each topic.","ISBN":"9780471895305","language":"English","author":[{"family":"Judge","given":"George G."},{"family":"Griffiths","given":"William E."},{"family":"Hill","given":"R. Carter"},{"family":"Lütkepohl","given":"Helmut"},{"family":"Lee","given":"Tsoung-Chao"}],"issued":{"date-parts":[["1985",1]]}},"label":"page"},{"id":407,"uris":[""],"uri":[""],"itemData":{"id":407,"type":"book","title":"Conditioning diagnostics","publisher":"Wiley Online Library","source":"Google Scholar","URL":"","author":[{"family":"Belsley","given":"David A."}],"issued":{"date-parts":[["1991"]]},"accessed":{"date-parts":[["2015",3,21]]}},"label":"page"},{"id":404,"uris":[""],"uri":[""],"itemData":{"id":404,"type":"book","title":"A Guide to Econometrics","publisher":"MIT Press","number-of-pages":"644","source":"Google Books","abstract":"A Guide to Econometrics has established itself as a preferred text for teachers and students throughout the world. It provides an overview of the subject and an intuitive feel for its concepts and techniques without the notation and technical detail that characterize most econometrics textbooks.The fifth edition has two major additions, a chapter on panel data and an innovative chapter on applied econometrics. Existing chapters have been revised and updated extensively, particularly the specification chapter (to coordinate with the applied econometrics chapter), the qualitative dependent variables chapter (to better explain the difference between multinomial and conditional logit), the limited dependent variables chapter (to provide a better interpretation of Tobit estimation), and the time series chapter (to incorporate the vector autoregression discussion from the simultaneous equations chapter and to explain more fully estimation of vector error correction models). Several new exercises have been added, some of which form new sections on bootstrapping and on applied econometrics.This edition is for sale in all of the Americas, the West Indies, and U.S. dependencies only.","ISBN":"9780262611831","language":"en","author":[{"family":"Kennedy","given":"Peter"}],"issued":{"date-parts":[["2003"]]}},"label":"page"}],"schema":""} [30–33]. We used Belsley diagnostics, one of the most common procedures, to detect multicollinearity. Although Belsley diagnostics are normally applied to linear regression models, it is still valid to apply it for MNL since multicollinearity is a problem among X ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"15f4cr9k72","properties":{"formattedCitation":"[34]","plainCitation":"[34]"},"citationItems":[{"id":413,"uris":[""],"uri":[""],"itemData":{"id":413,"type":"paper-conference","title":"Multinomial and ordinal logistic regression using PROC LOGISTIC","container-title":"NESUG","publisher":"NESUG","publisher-place":"Baltimore","event":"Northeast SAS Users’ Group 2010 conference","event-place":"Baltimore","abstract":"Logistic regression may be useful when we are trying to model a categorical dependent variable (DV) as a function of one or \nmore independent variables. This paper reviews the case when the DV has more than two levels, either ordered or not, gives \nand explains SAS \nR code for these methods, and illustrates them with examples.","URL":"","author":[{"family":"Flom","given":"Peter L."}],"issued":{"date-parts":[["2010"]]},"accessed":{"date-parts":[["2015",3,21]]}}}],"schema":""} [34]. The outcomes of Belsley diagnostics are condition indices and variance-decomposition proportions for each X. A condition index greater than 30 represents strong multicollinearity ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"8monv0bis","properties":{"formattedCitation":"[33]","plainCitation":"[33]"},"citationItems":[{"id":404,"uris":[""],"uri":[""],"itemData":{"id":404,"type":"book","title":"A Guide to Econometrics","publisher":"MIT Press","number-of-pages":"644","source":"Google Books","abstract":"A Guide to Econometrics has established itself as a preferred text for teachers and students throughout the world. It provides an overview of the subject and an intuitive feel for its concepts and techniques without the notation and technical detail that characterize most econometrics textbooks.The fifth edition has two major additions, a chapter on panel data and an innovative chapter on applied econometrics. Existing chapters have been revised and updated extensively, particularly the specification chapter (to coordinate with the applied econometrics chapter), the qualitative dependent variables chapter (to better explain the difference between multinomial and conditional logit), the limited dependent variables chapter (to provide a better interpretation of Tobit estimation), and the time series chapter (to incorporate the vector autoregression discussion from the simultaneous equations chapter and to explain more fully estimation of vector error correction models). Several new exercises have been added, some of which form new sections on bootstrapping and on applied econometrics.This edition is for sale in all of the Americas, the West Indies, and U.S. dependencies only.","ISBN":"9780262611831","language":"en","author":[{"family":"Kennedy","given":"Peter"}],"issued":{"date-parts":[["2003"]]}}}],"schema":""} [33]. In this case, it is highly recommended to omit all X with variance-decomposition proportions exceeding the tolerance of 0.5 ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1d418v2ndp","properties":{"formattedCitation":"[33]","plainCitation":"[33]"},"citationItems":[{"id":404,"uris":[""],"uri":[""],"itemData":{"id":404,"type":"book","title":"A Guide to Econometrics","publisher":"MIT Press","number-of-pages":"644","source":"Google Books","abstract":"A Guide to Econometrics has established itself as a preferred text for teachers and students throughout the world. It provides an overview of the subject and an intuitive feel for its concepts and techniques without the notation and technical detail that characterize most econometrics textbooks.The fifth edition has two major additions, a chapter on panel data and an innovative chapter on applied econometrics. Existing chapters have been revised and updated extensively, particularly the specification chapter (to coordinate with the applied econometrics chapter), the qualitative dependent variables chapter (to better explain the difference between multinomial and conditional logit), the limited dependent variables chapter (to provide a better interpretation of Tobit estimation), and the time series chapter (to incorporate the vector autoregression discussion from the simultaneous equations chapter and to explain more fully estimation of vector error correction models). Several new exercises have been added, some of which form new sections on bootstrapping and on applied econometrics.This edition is for sale in all of the Americas, the West Indies, and U.S. dependencies only.","ISBN":"9780262611831","language":"en","author":[{"family":"Kennedy","given":"Peter"}],"issued":{"date-parts":[["2003"]]}}}],"schema":""} [33]. All X show low degree of multicollinearity with condition indices between 1 and 9.15 for all X maps and 1 to 9.86 for the selected samples. Thus, all X will be used in the MNL model.DataDependent variable. The Y is constituted by cells whose status not changed from non-urban to urban and changed from non-urban to one of different urban classes between 2000 and 2010. The cadastral dataset (CAD) was used to develop Y. CAD is a vector map representing buildings in two dimensions as polygons provided by the Land Registry Administration of Belgium. Each building comes with different attributes from which the construction date is the most important attribute for our study. Using construction date, two urban land-use maps were developed for 2000 and 2010 years. Preparing the data. CAD vector data were rasterized at a very fine cell dimension (2x2 m2). The rasterized cells were aggregated with a factor of 50 by which to multiply the cell size to obtain 100x100 m2 raster grid. Each aggregated cell has a density value that represents the number of rasterized 2x2 cells. This value will be used to introduce dwellings density in the aggregated CAD maps.Scholars always define a minimum map unit (MMU) to avoid overestimation of one class in land cover data ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"p5crevsk4","properties":{"formattedCitation":"[35, 36]","plainCitation":"[35, 36]"},"citationItems":[{"id":366,"uris":[""],"uri":[""],"itemData":{"id":366,"type":"article-journal","title":"An experimental assessment of minimum mapping unit size","container-title":"IEEE Transactions on Geoscience and Remote Sensing","page":"2132-2134","volume":"41","issue":"9","source":"IEEE Xplore","abstract":"Land-cover (LC) maps derived from remotely sensed data are often presented using a minimum mapping unit (MMU) to characterize a particular landscape theme of interest. The choice of an MMU that is appropriate for the projected use of a classification is an important consideration. The objective of this experiment was to determine the effect of MMU on a LC classification of the Neuse River Basin (NRB) in North Carolina. The results of this work indicate that MMU size had a significant effect on accuracy estimates only when the MMU was changed by relatively large amounts. Typically, an MMU is selected as close as possible to the original data resolution so as to reduce the loss of specificity introduced in the resampling process. Since only large MMU changes resulted in significant differences in the accuracy estimates, an analyst may have the flexibility to select from a range of MMUs that are appropriate for a given application.","DOI":"10.1109/TGRS.2003.816587","ISSN":"0196-2892","author":[{"family":"Knight","given":"J.F."},{"family":"Lunetta","given":"R.S."}],"issued":{"date-parts":[["2003",9]]}},"label":"page"},{"id":368,"uris":[""],"uri":[""],"itemData":{"id":368,"type":"article-journal","title":"Effects of minimum mapping unit on land cover data spatial configuration and composition","container-title":"International Journal of Remote Sensing","page":"4853-4880","volume":"23","issue":"22","source":"Taylor and Francis+NEJM","abstract":"A key issue when generating a land cover map from remotely sensed data is the selection of the minimum mapping unit (MMU) to be employed, which determines the extent of detail contained in the map. This study analyses the effects of MMU in land cover spatial configuration and composition, by using simulated landscape thematic patterns generated by the Modified Random Clusters method. This approach allows a detailed control of the different factors influencing landscape metrics behaviour, as well as taking into account a wide range of land cover pattern possibilities. Land cover classes that are sparse and fragmented can be considerably misrepresented in the final map when increasing MMU, while the classes that occupy a large percentage of map area tend to become more dominant. Mean Patch Size and Number of Patches are very poor indicators of pattern fragmentation in this context. In contrast, Landscape Division (LD) and related indices (Splitting Index and Effective Mesh Size) are clearly suitable for comparing the fragmentation of landscape data with different MMUs. We suggest that the Mean Shape Index, the most sensitive to MMU of those considered in this study, should not be used in further landscape studies if land cover data with different MMU or patch size frequency distribution are to be compared. In contrast, the Area Weighted Mean Shape Index presents a very robust behaviour, which advocates the use of this index for the quantification of the overall irregularity of patch shapes in landscape spatial patterns. The results presented allow quantifying the biases resulting from selecting a certain MMU when generating a land cover dataset. In general, a bigger MMU implies underestimating landscape diversity and fragmentation, as well as over-estimating animal population dispersal success. Guidelines are provided for the proper use and comparison of spatial pattern indices measured in maps with different MMUs.","DOI":"10.1080/01431160110114493","ISSN":"0143-1161","author":[{"family":"Saura","given":"S."}],"issued":{"date-parts":[["2002",1,1]]},"accessed":{"date-parts":[["2015",3,10]]}},"label":"page"}],"schema":""} [35, 36]. The CORINE Land-Cover (CLC) dataset, as an example, was provided with MMU of 25 ha. Due to the nature of the study area, it is possible to find several scattered buildings in a hectare. Consequently, to produce more accurate data we set MMU at one hectare. In order to avoid overestimation of urban lands, two procedures were applied to the aggregated data: minimum building density per cell (MBDC) and minimum building density per neighbor (MBDN).Minimum building density per cell. The average size of residential building in Belgium is about 10x10 m? ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"zKmjFFNR","properties":{"formattedCitation":"[37]","plainCitation":"[37]"},"citationItems":[{"id":235,"uris":[""],"uri":[""],"itemData":{"id":235,"type":"article-journal","title":"Defining and characterizing urban boundaries: A fractal analysis of theoretical cities and Belgian cities","container-title":"Computers, Environment and Urban Systems","page":"234-248","volume":"41","source":"ScienceDirect","abstract":"In this paper we extract the morphological boundaries of urban agglomerations and characterize boundary shapes using eight fractal and nonfractal spatial indexes. Analyses were first performed on six archetypal theoretical cities, and then on Belgium’s 18 largest towns. The results show that: (1) the relationship between the shape of the urban boundary (fractal dimension, dendricity, and compactness) and the built morphology within the urban agglomeration (fractal dimension, proportion of buildings close to the urban boundary) is not straightforward; (2) each city is a unique combination of the morphological characteristics considered here; (3) due to their different morphological characteristics, the planning potential of Flemish and Walloon cities seems to be very different.","DOI":"10.1016/penvurbsys.2013.07.003","ISSN":"0198-9715","shortTitle":"Defining and characterizing urban boundaries","journalAbbreviation":"Computers, Environment and Urban Systems","author":[{"family":"Tannier","given":"Cécile"},{"family":"Thomas","given":"Isabelle"}],"issued":{"date-parts":[["2013",9]]},"accessed":{"date-parts":[["2014",7,2]]}}}],"schema":""} [37]. This figure somehow corresponds to the average size of households in Wallonia ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2h54ucr8bn","properties":{"formattedCitation":"[38]","plainCitation":"[38]"},"citationItems":[{"id":415,"uris":[""],"uri":[""],"itemData":{"id":415,"type":"webpage","title":"Statistics Belgium","container-title":"Statistics Belgium","URL":"","author":[{"family":"Belgian Federal Government","given":""}],"issued":{"date-parts":[["2013"]]},"accessed":{"date-parts":[["2014",4,29]]}}}],"schema":""} [38] multiplied by the single family house floor space per capita in north & west Europe, which is about 41 m? ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"co4elh44h","properties":{"formattedCitation":"[39]","plainCitation":"[39]"},"citationItems":[{"id":398,"uris":[""],"uri":[""],"itemData":{"id":398,"type":"report","title":"Europe’s buildings under the microscope","publisher":"Buildings Performance Institute Europe (BPIE)","publisher-place":"Brussels","event-place":"Brussels","author":[{"family":"Economidou","given":"Marina"},{"family":"Atanasiu","given":"Bogdan"},{"family":"Despret","given":"Chantal"},{"family":"Maio","given":"Joana"},{"family":"Nolte","given":"Ingeborg"},{"family":"Rapf","given":"Oliver"}],"issued":{"date-parts":[["2011"]]}}}],"schema":""} [39]. The multiplication gives an average size of 2.3*41= 94.3 m?. Thus, we set MBDC at 25 (100 m?).Minimum building density per neighbor. A threshold of five dwellings per ha was fixed for considering that a cell was urbanized. Neighborhoods with such a density are indeed observable in Wallonia. We then performed an analysis using different thresholds of MBDN using a search window of 3x3 cells for each MBDN cell less than 125 (5x25). These thresholds are 125, 250, 625, 1250 and 2500. Table 1 lists a comparison between CLC data, CAD original aggregated data and different MBDN thresholds.Table SEQ "Table" \* MERGEFORMAT 1. Comparison of area (km?) between CLC, CAD_Org original aggregated CAD data, MBDN_125, MBDN_250, MBDN_625, MBDN_1250 and MBDN_2500.YearCLCCAD_OrgMBDN125MBDN250MBDN625MBDN1250MBDN25002000250632292599246820931744157920062513------2010-333927162594223018681693We assumed that the number of changed cells between two time-steps would increase until a specific value of MBDN and then start declining along with the increase of MBDN. Actually, those cells that are under urban development at time-step 1 and reach the threshold of MBDN at time-step 2 are then considered as urban. If the MBDN threshold is very high, this condition will not be reached because this threshold exceeds the observed number of built cells at time-step 1 and 2. The number of changed cells calculated in two provinces of Wallonia confirmed our assumption (Fig. 2). The result showed that the most appropriate threshold for MBDN is 625.Fig. 2. Number of changed cells between 2000 and 2010Measuring density. Different definitions of urban density can be found in the literature, according to the application it is used for. Density can refer to either dwellings or inhabitants per unit area ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"osQUhQ3Q","properties":{"formattedCitation":"[40]","plainCitation":"[40]"},"citationItems":[{"id":369,"uris":[""],"uri":[""],"itemData":{"id":369,"type":"book","title":"Future Forms and Design for Sustainable Cities","publisher":"Routledge","number-of-pages":"454","source":"Google Books","abstract":"Concentrating on the planning and design of cities, the three sections take a logical route through the discussion from the broad considerations at regional and city scale, to the larger city at high and lower densities through to design considerations on the smaller block scale. Key design issues such as access to facilities, access for sunlight, life cycle analyses, and the impact of communications on urban design are tackled, and in conclusion, the research is compared to large scale design examples that have been proposed and/or implemented over the past decade to give a vision for the future that might be achievable.* Provides an accessible presentation of the latest research in sustainable urban planning and design* Illustrates recent sustainable plans and schemes to show how they stand up against the latest research* Offers architects, urban designers and planners a view of how urban forms can become more sustainable in the future","ISBN":"9780750663090","language":"en","author":[{"family":"Jenks","given":"Michael"},{"family":"Dempsey","given":"Nicola"}],"issued":{"date-parts":[["2005"]]}}}],"schema":""} [40]. In this paper, we defined urban density as a number of built-up units per cell of one hectare. We performed different MNL models for 4, 6, 8 and 10 urban densities quantile classes and measured the goodness-of-fit. An MFR2 statistic is not appropriate to compare the goodness-of-fit in this case because MFR2 depends on Y which is changeable, in terms of number of classes. Consequently, we measured misclassification rate instead. It equaled 24.23%, 23.60%, 22.70% and 26.07% respectively. As a result, for the final MNL, we used eight classes for urban densities, from class0 (non-urban) to class7 (highest density), each class has almost the same number of cells except for class0 (Fig. 3). Table 2 lists the density range for each class. Urban class7 can be considered as urban cores and urban classes 4, 5 and 6 may be considered as urban peripheries and suburbs. Urban classes 2 and 3 may be considered as rural areas whereas urban class1 may be considered as remote locations. This can be further assessed by measuring how land-use policies control urban development within each class. Land-use policies can highly control urban developments within urban cores whereas urban development in peripheries and suburbs is not strictly following policies. Rural areas are expected to follow policies whereas very remote sites are normally not controlled by policies. Table SEQ "Table" \* MERGEFORMAT 2. Urban classes density ranges in number of 2x2 pixels (% of 100x100 cell area) covered by building footprints.ClassMinMaxμModeClass0a----Class125 (1.0%)78 (3.2%)51.532Class279 (3.2%)132 (5.3%)105.5127Class3133 (5.3%)180 (7.2%)156.5138Class4181 (7.2%)243 (9.8%)212.0182Class5244 (9.8%)330 (13.2%)287.0254Class6331 (13.2%)491 (19.7%)411.0333Class7492 (19.7%)2500 (100.0%)1365.9504a class0 represents all non-urban cells and affected cells by MBDC and MBDN procedures Independent variables. Statistical data related to population volume, households, employment rate, richness index and mean land/housing price were acquired from the official Belgian statistics ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1dm1rere4n","properties":{"formattedCitation":"[38, 41]","plainCitation":"[38, 41]"},"citationItems":[{"id":415,"uris":[""],"uri":[""],"itemData":{"id":415,"type":"webpage","title":"Statistics Belgium","container-title":"Statistics Belgium","URL":"","author":[{"family":"Belgian Federal Government","given":""}],"issued":{"date-parts":[["2013"]]},"accessed":{"date-parts":[["2014",4,29]]}}},{"id":417,"uris":[""],"uri":[""],"itemData":{"id":417,"type":"webpage","title":"Statistiques","container-title":"IWEPS","URL":"","author":[{"family":"Institut wallon de l’évaluation, de la prospective et de la statistique","given":""}],"issued":{"date-parts":[["2011"]]},"accessed":{"date-parts":[["2014",5,12]]}}}],"schema":""} [38, 41] and mapped with a resolution of 100x100 m? raster grid at municipality level. Gross population density was calculated for each municipality as the number of inhabitants divided by the area of municipality in km? whereas net population density was calculated as the number of inhabitants divided by the area of built-up zones of the municipality in km?.Fig. 3. Urban density classes of 2010 (7 highest density, 1 lowest density)Digital Elevation Model (DEM) provided by the Belgian National Geographic Institute was used to calculate elevation and slope in percentage for each cell.Accessibility was measured in terms of Euclidean distance of a cell from the nearest road and city. Road networks for 2002 were provided by Navteq. Four functional classes of roads were introduced in MNL (R_class1: high speed and volume controlled access roads, R_class2: quick travel between and through cities, R_class3: moderate speed travel within cities and R_class4: moderate speed travel between neighborhoods). Major cities of Belgium (Antwerp, Brussels, Wavre, Brugge, Gent, Charleroi, Mons, Liege, Hasselt, Arlon and Namur) were used to develop a map of distances to cities.According to the most recent zoning plan of Wallonia, urban development is only allowed in those zones that are designated for residential, economic or leisure development. In other zones, such as agricultural and forest areas, urban development is not permitted unless specific conditions. A zoning map was developed by discerning zones where urban development is not permitted (code 0) and zones that are designated for urban development (code 1). All maps were created as raster grids with a resolution of 100x100 m? (table 1). The spatial resolution is defined based on the availability of data. The statistical data are available at municipality level whereas other variables could be calculated at cell level. That is common to coupling both resolution levels ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2necducfgb","properties":{"formattedCitation":"[19, 42]","plainCitation":"[19, 42]"},"citationItems":[{"id":326,"uris":[""],"uri":[""],"itemData":{"id":326,"type":"article-journal","title":"Spatio-temporal dynamics in the flood exposure due to land use changes in the Alpine Lech Valley in Tyrol (Austria)","container-title":"Natural Hazards","page":"1243-1270","volume":"68","issue":"3","source":"link.","abstract":"Flood risk is expected to increase in many regions of the world in the next decades with rising flood losses as a consequence. First and foremost, it can be attributed to the expansion of settlement and industrial areas into flood plains and the resulting accumulation of assets. For a future-oriented and a more robust flood risk management, it is therefore of importance not only to estimate potential impacts of climate change on the flood hazard, but also to analyze the spatio-temporal dynamics of flood exposure due to land use changes. In this study, carried out in the Alpine Lech Valley in Tyrol (Austria), various land use scenarios until 2030 were developed by means of a spatially explicit land use model, national spatial planning scenarios and current spatial policies. The combination of the simulated land use patterns with different inundation scenarios enabled us to derive statements about possible future changes in flood-exposed built-up areas. The results indicate that the potential assets at risk depend very much on the selected socioeconomic scenario. The important conditions affecting the potential assets at risk that differ between the scenarios are the demand for new built-up areas as well as on the types of conversions allowed to provide the necessary areas at certain locations. The range of potential changes in flood-exposed residential areas varies from no further change in the most moderate scenario ‘Overall Risk’ to 119 % increase in the most extreme scenario ‘Overall Growth’ (under current spatial policy) and 159 % increase when disregarding current building restrictions.","DOI":"10.1007/s11069-012-0280-8","ISSN":"0921-030X, 1573-0840","journalAbbreviation":"Nat Hazards","language":"en","author":[{"family":"Cammerer","given":"Holger"},{"family":"Thieken","given":"Annegret H."},{"family":"Verburg","given":"Peter H."}],"issued":{"date-parts":[["2013",9,1]]},"accessed":{"date-parts":[["2014",11,22]]}}},{"id":320,"uris":[""],"uri":[""],"itemData":{"id":320,"type":"article-journal","title":"Modelling urban growth in the Indo-Gangetic plain using nighttime OLS data and cellular automata","container-title":"International Journal of Applied Earth Observation and Geoinformation","page":"155-165","volume":"33","source":"ScienceDirect","abstract":"The present study demonstrates the applicability of the Operational Linescan System (OLS) sensor in modelling urban growth at regional level. The nighttime OLS data provides an easy, inexpensive way to map urban areas at a regional scale, requiring a very small volume of data. A cellular automata (CA) model was developed for simulating urban growth in the Indo-Gangetic plain; using OLS data derived maps as input. In the proposed CA model, urban growth was expressed in terms of causative factors like economy, topography, accessibility and urban infrastructure. The model was calibrated and validated based on OLS data of year 2003 and 2008 respectively using spatial metrics measures and subsequently the urban growth was predicted for the year 2020. The model predicted high urban growth in North Western part of the study area, in south eastern part growth would be concentrated around two cities, Kolkata and Howrah. While in the middle portion of the study area, i.e., Jharkhand, Bihar and Eastern Uttar Pradesh, urban growth has been predicted in form of clusters, mostly around the present big cities. These results will not only provide an input to urban planning but can also be utilized in hydrological and ecological modelling which require an estimate of future built up areas especially at regional level.","DOI":"10.1016/j.jag.2014.04.009","ISSN":"0303-2434","journalAbbreviation":"International Journal of Applied Earth Observation and Geoinformation","author":[{"family":"Roy Chowdhury","given":"P. K."},{"family":"Maithani","given":"Sandeep"}],"issued":{"date-parts":[["2014",12]]},"accessed":{"date-parts":[["2014",11,22]]}}}],"schema":""} [e.g. 19, 42].Table SEQ "Table" \* MERGEFORMAT 3. List of the selected drivers of urban development.Driver NameTypeaUnitResolutionbμσX1Elevation1m1 257.14183.4X2Slope1?%1 5.5157.02X3Dist to city1m1 29028.1615479.34X4Dist to R_class11m1 7936.128282.57X5Dist to R_class21?m1 4174.53757.23X6Dist to R_class31m1 1668.271425.25X7Dist to R_class41m1 818.63850.46X8Dist to rail stations1m1 6962.075710.64X9Num households1number26421.5212040.71X10Mean housing price1€213948731965.1X11Mean land price1€/m?251.199.73X12Employment potential1%248.3998.42X13Richness index 1?%295.7161.62X14Growth population density1inh/km?2206.95354.33X15Net population density1?inh/urban km?2819.52522.97X16Zoning status2?binary1??a 1. Continuous, 2. Categorical.?b 1. Cell level, 2. Municipality level.Results and discussionsIn the last decade, Wallonia experienced urban growth distributed between different urban classes. The total area of urban land in 2000 was 2093 km?, accounting for 12.4% of the total area, and in 2010, urban land increased to 2230 km?, accounting for 13.2% of the total area. More than 137.5 km? of land was converted between 2000 and 2010 from non-urban to different urban densities as: 50.4 km? to urban class1, 34.7 km? to class2, 23 km? to class3, 10.5 km? to class4, 7.12 km? to class5, 5.3 km? to class6 and 6.68 km? to class7. Of course, one km? of urban class1 has not the same weight as one km? of urban class7, as densities are very different.The goodness-of-fit for predictive ability was evaluated using MFR2 and it equals 0.244. The model reveals a very good correspondence with ROC values: 0.775, 0.819, 0.829, 0.805, 0.793, 0.813 and 0.914 for classes 1, 2, 3, 4, 5, 6 and 7 respectively. This indicates that the MNL performs well and the MNL’s outcomes could effectively interpret the process of urban development in Wallonia. The results of the MNL model are illustrated in table 4. Scholars and decision-makers are not only interested in the identification of potential driving forces of urban development process but also interested in measuring the relative contribution of these drivers to urban development process ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"qcvdele7t","properties":{"formattedCitation":"[3, 10]","plainCitation":"[3, 10]"},"citationItems":[{"id":105,"uris":[""],"uri":[""],"itemData":{"id":105,"type":"article-journal","title":"Forty years of urban expansion in Beijing: What is the relative importance of?physical, socioeconomic, and neighborhood factors?","container-title":"Applied Geography","page":"1-10","volume":"38","source":"ScienceDirect","abstract":"Urban expansion is one of the major causes of many ecological and environmental problems in urban areas and the surrounding regions. Understanding the process of urban expansion and its driving factors is crucial for urban growth planning and management to mitigate the adverse impacts of such growth. Previous studies have primarily been conducted from a static point of view by examining the process of urban expansion for only one or two time periods. Few studies have investigated the temporal dynamics of the effects of the driving factors in urban expansion. Using Beijing as a case study, this research aims to fill this gap. Urban expansion from 1972 to 2010 was detected from multi-temporal remote sensing images for four time periods. The effects of physical, socioeconomic, and neighborhood factors on urban expansion and their temporal dynamics were investigated using binary logistic regression. In addition, the relative importance of the three types of driving factors was examined using variance partitioning. The results showed that Beijing has undergone rapid and magnificent urban expansion in the past forty years. Physical, socioeconomic, and neighborhood factors have simultaneously affected this expansion. Socioeconomic factors were the most important driving force, except during the period of 1972–1984. In addition, the effects of these driving factors on urban expansion varied with time. The magnitude of the unique effects of physical factors and neighborhood factors declined while that of socioeconomic factors increased along with the urbanization process. The findings of this study can help us better understand the process of urban expansion and thus have important implications for urban planning and management in Beijing and similar cities.","DOI":"10.1016/j.apgeog.2012.11.004","ISSN":"0143-6228","shortTitle":"Forty years of urban expansion in Beijing","journalAbbreviation":"Applied Geography","author":[{"family":"Li","given":"Xiaoma"},{"family":"Zhou","given":"Weiqi"},{"family":"Ouyang","given":"Zhiyun"}],"issued":{"date-parts":[["2013",3]]},"accessed":{"date-parts":[["2014",6,25]]}}},{"id":276,"uris":[""],"uri":[""],"itemData":{"id":276,"type":"article-journal","title":"Proximate causes of land-use change in Narok District, Kenya: a spatial statistical model","container-title":"Agriculture, Ecosystems & Environment","page":"65-81","volume":"85","issue":"1–3","source":"ScienceDirect","abstract":"This study attempts to identify how much understanding of the driving forces of land-use changes can be gained through a spatial, statistical analysis. Hereto, spatial, statistical models of the proximate causes of different processes of land-use change in the Mara Ecosystem (Kenya) were developed, taking into account the spatial variability of the land-use change processes. The descriptive spatial models developed here suggest some important factors driving the land-use changes that can be related to some well-established theoretical frameworks. The explanatory variables of the spatial model of mechanised agriculture suggest a von Thünen-like model, where conversion to agriculture is controlled by the distance to the market, as a proxy for transportation costs, and agro-climatic potential. Expansion of smallholder agriculture and settlements is also controlled by land rent, defined, in this case, by proximity to permanent water, land suitability, location near a tourism market, and vicinity to villages to gain access to social services (e.g. health clinics, schools, local markets). This difference in perception of land rent reflects the widely different social and economic activities and objectives of smallholders versus the large entrepreneurs involved in mechanised farming. Spatial heterogeneity as well as the variability in time of land-use change processes affect our ability to use regression models for wide ranging extrapolations. The models allow evaluating the impact of changes in driving forces that are well represented by proximate causes of land-use change.","DOI":"10.1016/S0167-8809(01)00188-8","ISSN":"0167-8809","shortTitle":"Proximate causes of land-use change in Narok District, Kenya","journalAbbreviation":"Agriculture, Ecosystems & Environment","author":[{"family":"Serneels","given":"Suzanne"},{"family":"Lambin","given":"Eric F"}],"issued":{"date-parts":[["2001",6]]},"accessed":{"date-parts":[["2014",7,2]]}}}],"schema":""} [3, 10]. To relatively measure the contribution of each X to urban development process, Odds Ratio (OR) that equals exp(β) is calculated. It is difficult to directly interpret an MNL coefficients and they become useful when they are converted into an OR. An OR greater than 1 indicates a positive effect, the probability of urban development increases by increasing the OR of the variable, whereas less than 1 indicates a negative effect, the probability of urban development decreases by increasing the OR of the variable. An OR of 1 means neutral contribution to the urban development process ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"remqveun4","properties":{"formattedCitation":"[13]","plainCitation":"[13]"},"citationItems":[{"id":5,"uris":[""],"uri":[""],"itemData":{"id":5,"type":"article-journal","title":"Spatial determinants of urban land use change in Lagos, Nigeria","container-title":"Land Use Policy","page":"502-515","volume":"24","issue":"2","source":"ScienceDirect","abstract":"The objective of this research was to identify the factors responsible for residential and industrial/commercial land development in Lagos between 1984 and 2000. Land use changes were mapped using satellite images, while binary logistic regression was used to model the probability of observing urban development as a function of spatially explicit independent variables. Accessibility, spatial interaction effects and policy variables were the major determinants of land use change. Variables that influenced residential development were not necessarily those responsible for the expansion of industrial/commercial land areas. The evidence of frontier residential development calls for land tenure and housing development reforms, and land use controls to minimize the environmental consequences of unplanned urban expansion.","DOI":"10.1016/j.landusepol.2006.09.001","ISSN":"0264-8377","journalAbbreviation":"Land Use Policy","author":[{"family":"Braimoh","given":"Ademola K."},{"family":"Onishi","given":"Takashi"}],"issued":{"date-parts":[["2007",4]]},"accessed":{"date-parts":[["2014",6,25]]}}}],"schema":""} [13]. Table 5 lists OR values for all X. For interpreting continuous X, multiply a one unit increase in X variable by OR. For instance, five units increase in elevation increase the class7 portability by exp(5x0.110) ~ 1.7 whereas decrease the class1 portability by exp(5x-0.078) ~ 0.7. For categorical X, zoning status, the OR value for class1 is about 15.4. This means that it is around 15.4 (exp(2.735)) times more likely to find a new urban cell related to class1 in zones designated to urban development than in zones designated to other uses in zoning plan.Generally, the impact of different drivers varies with different urban densities. These drivers can be grouped into common drivers with impacts on different urban classes and special drivers with impacts on individual classes.Table SEQ "Table" \* MERGEFORMAT 4. The coefficients (β) of MNL model (class0 is the reference class).Class1Class2Class3Class4Class5Class6Class7α-4.158-4.603-4.700-4.583-4.409-4.500-5.969Elevation-0.078-0.0320.0430.1050.0230.2040.110Slope-0.237-0.078-0.210-0.644-0.693-0.840-1.185Dist to city0.0720.051-0.099-0.0360.1300.0080.070Dist to R_class1-0.1290.014-0.004-0.165-0.146-0.306-0.917Dist to R_class2-0.113-0.036-0.021-0.084-0.244-0.215-0.587Dist to R_class3-0.265-0.257-0.141-0.283-0.214-0.197-0.278Dist to R_class4-0.651-0.536-0.587-0.552-0.427-0.394-0.228Dist to rail stations0.002-0.0200.0690.024-0.136-0.141-0.289Num households0.001-0.016-0.079-0.098-0.131-0.082-0.086Mean housing price0.0270.0290.0740.0370.0960.038-0.170Mean land price0.0790.1270.0830.191-0.0540.2500.198Employment potential-0.174-0.098-0.0270.0010.1050.2050.236Richness index 0.1590.0320.057-0.076-0.043-0.371-0.305Gross population density0.3110.1610.2100.2130.257-0.0760.045Net population density-0.362-0.364-0.451-0.233-0.0780.120-0.070Zoning status2.7353.6393.7453.2782.9422.8073.775Table SEQ "Table" \* MERGEFORMAT 5. The OR value for X (class0 is the reference class).?Class1Class2Class3Class4Class5Class6Class7Elevation0.9250.9691.0431.1111.0231.2261.117Slope0.7890.9250.8110.5250.5000.4320.306Dist to city1.0741.0530.9060.9651.1391.0081.072Dist to R_class10.8791.0140.9960.8480.8640.7360.400Dist to R_class20.8930.9640.9800.9200.7830.8070.556Dist to R_class30.7670.7740.8690.7540.8070.8220.758Dist to R_class40.5210.5850.5560.5760.6530.6740.796Dist to rail stations1.0020.9801.0711.0240.8730.8680.749Num households1.0010.9840.9240.9070.8770.9220.918Mean housing price1.0271.0291.0761.0381.1011.0390.843Mean land price1.0821.1361.0871.2100.9471.2841.219Employment potential0.8400.9070.9741.0011.1111.2281.266Richness index 1.1721.0331.0590.9270.9580.6900.737Gross population density1.3651.1751.2331.2371.2930.9271.046Net population density0.6960.6950.6370.7920.9251.1280.932Zoning status15.40538.05042.31726.52318.95216.55543.598We found that the likelihood of urban development is markedly influenced by policies (zoning status). Zoning status has the strongest impact on urban development within all urban classes. Slope, distance to R_class4, distance to R_class3, net/gross population densities and mean land price respectively also demonstrate an impact on all urban classes, but far less important than zoning status. The result shows that the impact of slope is generally increasing with built-up densities. Quite logically dense urban projects are rather developed in flat areas, in many cases floodplains in Wallonia. In contrast, the impact of distance to R_class4, namely intra-urban or inter-villages roads, is generally decreasing with built-up densities. Distances to R_class1 and R_class2 have a noticeable impact on the development of high density projects (class7) with OR of 0.40 and 0.56 respectively. That indicates a strong relationship between highest urban density class and proximity to high speed roads. It should be stressed at this respect that a number of urban cores are directly accessible via high-speed roads in Wallonia. Employment potential has a significant attraction impact on urban class7. It is generally increasing with density, which is what can be expected. Richness index and elevation have moderate impacts on urban class6. Distance to rail stations has a moderate positive influence on urban class7. Still this influence is much lower than the proximity of high-speed roads, suggesting that urban areas located nearby train stations are not yet sufficiently attractive for new dense urban developments in Wallonia. This should be a major source of concern for urban policy makers. Quite significantly, mean housing price represents a low influence on urban growth. This influence is negative for high density developments, which is another source of concern given the shortage of available housing, especially apartments, in areas characterized by a strong pressure on the real estate market.Our assumption regarding general identification of different urban classes can be assessed by measuring the influence of land-use policies on urban development. OR values for zoning show that policy has a very strong impact on the highest density developments (class7). Those high density developments will most naturally be developed in areas where the legally-binding plan allows such developments, in order to minimize the administrative and financial risks of such operations. Further on they are often located in dense urban areas where the existing plan already allocates a significant share of the land to urban development and where non-urban zones (parks, green areas, etc) are strongly protected for environmental, social and/or heritage reasons. Policies impact is taken a downward trend with classes 4, 5 and 6 respectively. We consider those classes as suburbs. Quite understandably that urban developments in suburbs do not strictly follow policies. The impact of policy on class1 is very low compared to other classes. This class can be considered as remote developments, consisting in scattered constructions (1 to 3 buildings per ha), which can sometimes deviate from existing zoning plans especially in agricultural zones. Land-use policies also show a noticeable impact on classes 2 and 3. We considered those both classes as low density developments in rural areas. It is not surprising that new developments are mainly directed to urbanizable zones, where there is an excess supply of such land. Fig. 4 presents urban development probability maps for classes 1 and 7 as examples of MNL model outcomes. Conclusions Urban development process in Wallonia is dynamic and diverse. The prediction of spatial distribution of such development can be modelled based on a set of geo-physical and socioeconomic attributes that represent proximate causes of urban growth. Considering urban development as a continuum allows us to better understand the interactions between different drivers and different urban densities. In this paper, we examined the driving forces of urban development process in Wallonia over a period of 10 years (2000 to 2010). Multinomial logistic regression model was employed to relatively measure the impact of different drivers on probability of urban development. Sixteen drivers were selected from four sets of driving forces including geo-physical features, land-use policies, socio-economic and accessibility. Generally, result reveals that policies and accessibility are the most important determinants of urban growth process. Most importantly, our results highlight that the impact of different drivers varies along with urban density. This is especially the case of land planning driver, whose effects are much more significant for smaller densities than for higher ones, except in the case of urban cores. This study findings can support urban growth modeling, urban planning, and decision-making process in identifying urban development likelihood for each location in the present and medium term. Fig. SEQ "Figure" \* MERGEFORMAT 4. MNL probability maps for: a. class1 (scattered), b. class7 (generally located in urban zones)Acknowledgments. The research was funded through the ARC grant for Concerted Research Actions, financed by the Wallonia-Brussels Federation.References ADDIN ZOTERO_BIBL {"custom":[]} CSL_BIBLIOGRAPHY 1.Arnfield, A.J.: Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island. Int. J. Climatol. 23, 1–26 (2003).2.Xian, G., Crane, M.: Assessments of urban growth in the Tampa Bay watershed using remote sensing data. Remote Sens. 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