Predicting the expansion of an urban boundary using spatial logistic regression and hybrid raster–vector routines with remote sensing and GIS

This paper presents an urban growth boundary model (UGBM) which utilizes spatial logistic regression (SLR), remote sensing, and GIS to simulate the differentially expanding geometry of a dynamic urban boundary over decadal time periods. SLR is used as the core algorithm in a UGBM quantifying how biophysical factors influence the rate at which all edges of an urban boundary expand over time. Spatial drivers selected from a raster-based environment are used as input predictor variables to the SLR UGBM, the output response variable being the distance between time-separated urban boundary intersections along arcs extending radially from a point centered at the urban core relative to the maximum distance. Percent area match (PAM) quantity and location goodness-of-fit metrics, fit of the predicted distance versus observed distance, and the sensitivity of the SLR UGBM to the contribution of each predictor variable are used to assess the agreement between predicted and observed urban boundaries. The model is built, tested, and validated using satellite images of the city of Las Vegas, United States of America, collected in 1990, 2000, and 2010. We compare urban boundary simulation of full and reduced SLR UGBMs to a null UGBM lacking in specificity of predictor variables. Results indicate that the SLR UGBM has a better goodness of fit compared to a null UGBM using PAM quantity and location goodness-of-fit metrics. Then, we use the SLR UGBM to predict urban boundary expansion between the years 2000 and 2010 and describe how this model can be used to plan ahead for future boundary expansions given what is known about current edge conditions.     

Monitoring land changes in an urban area using satellite imagery,GIS and landscape metrics

Monitoring land changes is an important activity in landscape planning and resource management. In this study, we analyze urban land changes in Atlanta metropolitan area through the combined use of satellite imagery, geographic information systems (GIS), and landscape metrics. The study site is a fast-growing large metropolis in the United States, which contains a mosaic of complex landscape types. Our method consisted of two major components: remote sensing-based land classification and GIS-based land change analysis. Specifically, we adopted a stratified image classification strategy combined with a GIS-based spatial reclassification procedure to map land classes from Landsat Thematic Mapper (TM) scenes acquired in two different years. Then, we analyzed the spatial variation and expansion of urban land changes across the entire metropolitan area through post classification change detection and a variety of GIS-based operations. We further examined the size, pattern, and nature of land changes using landscape metrics to examine the size, pattern, and nature of land changes. This study has demonstrated the usefulness of integrating remote sensing with GIS and landscape metrics in land change analysis that allows the characterization of spatial patterns and helps reveal the underlying processes of urban land changes. Our results indicate a transition of urbanization patterns in the study site with a limited outward expansion despite the dominant suburbanization process.     

中心城区城市增长的情景模拟与空间格局演化

基于1993-2008 年间4 个时相的遥感影像,应用SLEUTH模型模拟与预测自组织和规划引导两类情景下泉州中心城区的城市用地增长过程,并借助空间关联法分析其城市增长的空间格局演化特征,为“两规”空间协调提供科学依据。结果表明：① SLEUTH模型适用于研究区的城市增长模拟与预测,其对城市用地扩展的数量拟合要优于空间匹配,可作为多方案情景模拟的一个技术手段。② 规划引导预案的MPS、ED、AWMSI、MPI 四类景观指数均优于自组织预案,城市用地斑块的整体性、连接性较优,未来城市发展较为紧凑,利于实现土地的集约利用与城市的集聚发展。③ 随预测时间推移,研究区城市用地扩展的速率以及空间集聚性将有所减弱,城市增长的热点区也会发生演变与迁移。2008-2020 年,热点区分布总体呈现“圈层式”结构,局部以“跨江发展”为主要特征;2020-2030 年,热点区总体布局较为发散,局部则呈“环湾发展”与“孤立分布”特征。本研究将情景模拟、景观指数、空间分析等方法有效结合,有助于深刻理解研究区的城市空间增长过程,可为城市管理工作提供决策支持。     

Urban hypotheses and spatiotemporal characterization of urban growth in the Treasure Valley of Idaho,USA

Spatiotemporal patterns of urban growth can help identify impacts of urbanization, assess conceptual models of that growth, help predict future change, and inform associated urban management policies. Using multi-temporal spatial data (1938-2014), we categorized the newly urbanized area in Treasure Valley, Idaho into four urban growth forms and six urban land use classes. A time series analysis of new development revealed the existence of decadal-scale variability of urbanization at various levels of urban land use. Alternating dominance of dispersion and compaction processes were observed at the urban patch level. A similar periodicity was observed between edge-expansion and infill in terms of growth forms, and between residential and commercial development at the land use level. Our observations also indicate that recent urban densification is occurring in the Treasure Valley, similar to some other metropolitan regions in the United States.     

The suburban bias of American society?

Past research has characterized countries as displaying the traits of urban or rural bias. Neither concept fits the United States well. We propose, as a hypothesis for research, that it may better be understood as displaying a suburban bias vis-à-vis both urban and rural populations. Drawing on the urban and rural bias literatures, we discuss two forms that suburban bias might take, allocational and dispositional, and the ways in which they might be identified. We offer initial evidence of a prevailing suburban bias in the United States in two spheres, those of judicial interpretation and American planning history, and conclude with suggestions for further research on the hypothesis.     

美国城市郊区化及对策对中国城市节约增长的启示

城市的空间发展中一直同时存在着向心集聚和离心扩散两个过程,郊区化是城市中心区离心扩散力量超过向心集聚力量的一种空间表现形式,能有效缓解大城市因规模扩大而带来的人口密集、交通拥挤等现象;但同时也会产生土地资源浪费、生态环境破坏、阶层分化隔离以及内城衰退等负面影响。美国的城市郊区化现象最为典型,在应对郊区化问题,如怎样有序的建设和管理以及处理好建设和保护的关系等方面积累了丰富的经验。然而,由于土地所有制和城市化阶段的不同,中国与西方发达国家的郊区化呈现出不同特点。中国目前正处于快速城市化时期,由于GDP导向下的城市空间跨越式发展使得一些大城市已出现郊区化现象,导致"外溢—回波"效应突出等问题。通过对中﹑美城市郊区化现象进行比较分析,同时借鉴美国在有效发挥郊区化作用和解决郊区化造成的问题方面的先进经验,并基于城市节约增长的视角,研究如何提高城市空间增长的效率,希望在先进理念、管理技术以及协调政府和市场职能作用方面对城市发展做出有效的把握和引导。     

城市建设对暴雨内涝空间分布的影响研究——以武汉市主城区为例

城市内涝灾害频繁,用地开发与空间扩张被普遍认为是其致因之一。对比武汉市遥感数据,1984—2017年,超过30%的自然水体被填占开发,城市建设开发活跃、填湖造陆强度大。论文以武汉市为例,采用二项Logistic模型,定量分析不同降雨强度情景下的内涝影响因素。研究表明,填湖造陆将极大地增加极端降雨情景下城市滨水区域的内涝风险。城市地形地势、排水管网条件、用地类型以及邻域用地结构等因素,也直接影响内涝风险。基于2种不同的用地开发策略,预测城市内涝风险结果显示,城市用地的不当开发将引致严重内涝风险。依据内涝风险的空间分布预测结果,论文提出了相应的改善策略,以为科学地制定防涝减灾规划提供参考。     

TOLLEY'S MODEL: SOME MORE TIME SERIES OF ACTUAL AND PREDICTED URBAN GROWTH

Requests for information concerning the performance of Tolley's urban growth model following my 1991 Research Note on the stickiness of urbanward migration to the United States prompts the publication of twelve more cases, for each of which Tolley model predictions of the urban growth rate are compared with the actuals, for time periods that are as long as 150 years.     

基于城市夜间灯光数据的中美两国城市位序规模分布对比

工业革命以来,人类开启了大规模的城市化进程。城市随着交通和通讯技术的发展不断突破原有的规模限制,城市规模不断变大。以人口为指标的城市规模分布服从齐普夫法则。不同城市化发展阶段,其城市位序规模分布存在差异。城市夜间灯光数据一般可用来衡量城市的经济活动、建成区面积、人口密度、发展水平等。本文以同源的城市夜间灯光数据代表城市规模,在国家尺度和省州尺度研究中美两国城市规模位序分布,并比较其异同。研究表明,在国家尺度,2013-2016年间,中美两国城市规模均变得集聚,而中国城市规模分布比美国更为分散,齐普夫指数相差约0.1。在省州尺度,不同省州的城市规模分布存在差异,中国和美国分别有44%和84%的省份或州的齐普夫指数大于1,中国城市规模分布分散型省份占一半以上,而美国的集中型的州则占有84%,总体而言,中国城市的规模分布更为分散。中美两国高位序城市实际值远低于拟合值,理论上仍然具备很大的发展潜力。中国高位序城市需要进一步提高城市经济活动的聚集程度,使得城市规模体系更趋完善与成熟。     

Evolving United States metropolitan land use patterns

We investigate spatial patterns of residential and nonresidential land use for 257 United States metropolitan areas in 1990 and 2000, measured with 14 empirical indices. We find that metropolitan areas became denser during the 1990s but developed in more sprawl-like patterns across all other dimensions, on average. By far, the largest changes in our land use metrics occurred in the realm of employment, which became more prevalent per unit of geographic area, but less spatially concentrated and farther from the historical urban core, on average. Our exploratory factor analyses reveal that four factors summarize land use patterns in both years, and remained relatively stable across the two years: intensity, compactness, mixing, and core-dominance. Mean factor scores vary by metropolitan population, water proximity, type, and Census region. Improved measurement of metropolitan land use patterns can facilitate policy and planning decisions intended to minimize the most egregious aspects of urban sprawl.     

Calibration of an urban cellular automaton model by using statistical techniques and a genetic algorithm. Application to a small urban settlement of NW Spain

Cellular automata (CA) stand out among the most commonly used urban models for the simulation and analysis of urban growth because of their ability to reproduce complex dynamics, similar to those found in real cities, from simple rules. However, CA models still have to overcome some shortcomings related to their flexibility and difficult calibration. This study combines various techniques to calibrate an urban CA that is based on one of the most widely used urban CA models. First, the number of calibration parameters is reduced by using various statistical techniques, and, second, the calibration procedure is automated through a genetic algorithm. The resulting model has been assessed by simulating the urban growth of Ribadeo, a small village of NW Spain, characterized by low, slow urban growth, which makes the identification of urban dynamics and consequently the calibration of the model more difficult. Simulation results have shown that, by automating the calibration procedure, the model can be more easily applied and adapted to urban areas with different characteristics and dynamics. In addition, the simulations obtained with the proposed model show better values of cell-to-cell correspondence between simulated and real maps, and the values for most spatial metrics are closer to real ones.     

Configuring the neighbourhood effect in irregular cellular automata based models

Cellular automata (CA) models have been widely employed to simulate urban growth and land use change. In order to represent urban space more realistically, new approaches to CA models have explored the use of vector data instead of traditional regular grids. However, the use of irregular CA-based models brings new challenges as well as opportunities. The most strongly affected factor when using an irregular space is neighbourhood. Although neighbourhood definition in an irregular environment has been reported in the literature, the question of how to model the neighbourhood effect remains largely unexplored. In order to shed light on this question, this paper proposed the use of spatial metrics to characterise and measure the neighbourhood effect in irregular CA-based models. These metrics, originally developed for raster environments, namely the enrichment factor and the neighbourhood index, were adapted and applied in the irregular space employed by the model. Using the results of these metrics, distance-decay functions were calculated to reproduce the push-and-pull effect between the simulated land uses. The outcomes of a total of 55 simulations (5 sets of different distance functions and 11 different neighbourhood definition distances) were compared with observed changes in the study area during the calibration period. Our results demonstrate that the proposed methodology improves the outcomes of the urban growth simulation model tested and could be applied to other irregular CA-based models.     

Identifying determinants of urban growth from a multi-scale perspective: A case study of the urban agglomeration around Hangzhou Bay,China

Analyzing the spatial determinants of urban growth is helpful for urban planning and management. In a case study of urban agglomeration around Hangzhou Bay (China), four landscape metrics (total area, total edge, landscape shape index and aggregation index) were used to describe the landscape characteristics of the urban growth at two block scales (4 km and 7 km) during two temporal intervals (1994–2003 and 2003–2009). Spatial autocorrelation regression was employed to identify the geographic determinants of the urban landscape changes. The results indicated that the urban landscapes became more dominant, unstable, irregular and compact, especially in the centers of cities. These changes exhibited notable spatial variations and spatial autocorrelation at the two block scales. The distances to national and provincial roads influenced the urban pattern changes. The impacts of the urban centers on urban expansion gradually declined with the urbanization progress. The slope factor was the most influential determinant of urban growth. Our study emphasized the importance of considering the autocorrelation and scale effects when analyzing the determinants of urban growth. These findings may help land planners create policies and strategies for future urban development.     

Urbanization of the United States over two centuries: an approach based on a long-term database (1790–2010)

This paper presents an original methodology for the construction of a harmonized database for urban areas in the United States from 1790 to 2010 (a period with a census every 10 years, amounting to 23 dates). Upstream, this method is based on a theoretical choice, the use of a spatio-temporal reference frame to construct urban areas over the long term, defined by a maximum range of one hour’s travel for each period. We used an automatic aggregation from a Reilly model. Urban measures (growth, concentration) derived from this harmonized database are then presented, providing a unified picture of urbanization in the United States over more than two centuries. They are compared with results derived from more classical databases established using the official figures from the Census Bureau, or from other constructions differing from the present system. The results diverge considerably in some instances, which confirms the impact of the method of delineation of geographical areas in measures of growth.     

The Journal of Geography

Abstract

Recent immigration has been an important factor in the growth and distribution of the United States population. The rising number of immigrants has accounted for up to one-fifth of the annual population growth and they have gone to the already populous urban centers and states. Immigrants must be recognized as an increasingly important element of the urban and population geography of the United States.     

Changing Patterns of Population Density in the United States

Changing population density is often ignored in studies of population growth and population transfer in the United States. We show that there is a complex relationship between patterns of population growth and density increase by state. The largest gains in density are in the states of the northeastern megalopolis, California, and Florida. Analysis of the 150 counties with the greatest increases in density between 1980 and 1990 shows that they are well distributed across the United States including the larger metropolitan areas of the “Rustbelt.” In general, the most densely populated states and places are becoming more densely populated, a concept we refer to as densification.     

Changing Patterns of Population Density in the United States

Changing population density is often ignored in studies of population growth and population transfer in the United States. We show that there is a complex relationship between patterns of population growth and density increase by state. The largest gains in density are in the states of the northeastern megalopolis, California, and Florida. Analysis of the 150 counties with the greatest increases in density between 1980 and 1990 shows that they are well distributed across the United States including the larger metropolitan areas of the “Rustbelt.” In general, the most densely populated states and places are becoming more densely populated, a concept we refer to as densification.     

Spatial metrics to study urban patterns in growing and shrinking cities

This article reviews existing literature on spatial metrics, presenting a portfolio of metrics addressing the spatial patterns of growing and shrinking cities and discussing their potential and limitations. A wide and diverse set of spatial metrics was found. While these metrics address most of the identified spatial patterns of urban growth, spatial metrics used in urban shrinkage studies are much scarcer and not nearly sufficient to provide a comprehensive assessment of its spatial patterns. The article concludes that there is great potential for the development of new spatial metrics or mixed indicators, particularly in shrinkage contexts. The article builds on recent literature focusing on reviewing and developing metrics for particular spatial patterns (notably patterns of urban sprawl), while considering a very broad and multidisciplinary set of metrics. It focuses not only on the outcomes of urban growth but also on those of the increasingly common shrinking phenomenon.