Causes of sprawl: A portrait from space

We study the extent to which US urban development is sprawling and consider what determines differences in sprawl across space. Using remote-sensing data to track the evolution of land use on a grid of 8.7 billion 30x30 metre cells, we measure sprawl as the amount of undeveloped land surrounding an average urban dwelling. On this measure, while the extent of sprawl remained roughly unchanged between 1976 and 1992, it varied dramatically across metropolitan areas. Ground water availability, temperate climate, rugged terrain, decentralized employment, early public transport infrastructure, uncertainty about metropolitan growth, and unincorporated land in the urban fringe all increase sprawl.urban sprawl; land development; remote sensing

Identifying urban growth patterns through land-use/land-cover spatio-temporal metrics: Simulation and analysis

[EN] The spatial pattern of urban growth determines how the physical, socio-economic and environmental characteristics of urban areas change over time. Monitoring urban areas for early identification of spatial patterns facilitates assuring their sustainable growth. In this paper, we assess the use of spatio-temporal metrics from land-use/land-cover (LULC) maps to identify growth patterns. We applied LULC change models to simulate different scenarios of urban growth spatial patterns (i.e., expansion, compact, dispersed, road-based and leapfrog) on various baseline urban forms (i.e., monocentric, polycentric, sprawl and linear). Then, we computed the spatio-temporal metrics for the simulated scenarios, selected the most informative metrics by applying discriminant analysis and classified the growth patterns using clustering methods. Two metrics, Weighted mean expansion and Weighted Euclidean distance, which account for the densification, compactness and concentration of urban growth, were the most efficient for classifying the five growth patterns, despite the influence of the baseline urban form. These metrics have the potential to identify growth patterns for monitoring and evaluating the management of developing urban areas.This work was supported by the the Spanish Ministerio de Economia y Competitividad and FEDER [CGL2016-80705-R].Sapena Moll, M.; Ruiz Fernández, LÁ. (2021). Identifying urban growth patterns through land-use/land-cover spatio-temporal metrics: Simulation and analysis. International Journal of Geographical Information Science. 35(2):375-396. https://doi.org/10.1080/13658816.2020.181746337539635

Empirical Analysis of Urban Sprawl in Canadian Census Metropolitan Areas using Satellite Imagery, 1986-2016

Major Canadian cities have experienced rapid sprawl in the last 30 years. This dissertation presents two studies that empirically examine the causes of urban sprawl, merging census socioeconomics data and satellite imageries of 11 major Census Metropolitan Areas (CMAs). The monocentric city model and the Tiebout model are the main traditional theories explaining urban boundary changes and mobility residential. The first study focuses on the cross-sectional comparison among the 11 CMAs in 2016. In the second study, we zoom into the Toronto CMA and examine the longitudinal changes in its urban coverage at the fringe. We detect land cover/use changes of the Toronto CMA in 1986-2016. In both studies, we insert the role of price risk in understanding the timing of urban development. In doing so, both studies aim to contribute to the literature by broadening the traditional theories to include the role of risk in influencing urban development

Urban population density and environmental quality in Port-au-Prince, Haiti: a geo-statistical analysis

This dissertation revolves around three issues on the urban area of Port-au-Prince, Haiti: the population distribution pattern, its estimation from remote sensing images, and its relationship with environmental quality. It follows a three-paper format. Paper 1 examines the population density pattern by the monocentric and polycentric models, based on the 2003 census data. The regression results show a poor fitting power of monocentric functions, and improved but less than satisfactory R2 by polycentric functions. A five-sector conceptual model is proposed to capture the urban structure shaped by the absence or lack of institutional enforcement of land use regulations and urban planning. Paper 2 proposes a population estimation model based on Landsat ETM+ images that are widely available. The subpixel vegetation-impervious surface-soil (VIS) fractions derived from the Landsat multispectral bands (the mean value of houses fraction image, the mean value of vegetation and the standard deviation of vegetation fraction image) are used as predictors for urban population density. The research indicates that the geographically weighted regression (GWR) model, which accounts for spatial non-stationarity, performs much better than its Ordinary Least Square counterpart. Paper 3 uses multiple factors to assess and map the urban environmental quality (UEQ). In addition to parameters typically considered in previous studies, this study includes natural hazards and other parameters unique to Port-au-Prince. Crowdedness, waste, lack of vegetation, presence of slums and water body pollutions are considered as the most critical factors (negatively) affecting the quality of the environment in Port-au-Prince. All are exacerbated by population pressure on the resources, i.e., population density. The scores for corresponding factors are integrated together by weights extracted from a panel of local experts. The overall UEQ results are validated by field surveys. Each paper discusses important implications of major findings for public policy and plannin

Identification of polycentric cities in China based on NPP-VIIRS nighttime light data

Nighttime light data play an important role in the research on cities, while the urban centers over a large spatial scale are still far from clearly understood. Aiming at the current challenges in monitoring the spatial structure of cities using nighttime light data, this paper proposes a new method for identifying urban centers for massive cities at the large spatial scale based on the brightness information captured by the Suomi National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) sensor. Based on the method for extracting the peak point based on digital elevation model (DEM) data in terrain analysis, the maximum neighborhood and di_erence algorithms were applied to the NPP-VIIRS data to extract the pixels with the peak nighttime light intensity to identify the potential locations of urban centers. The results show 7239 urban centers in 2200 cities in China in 2017, with an average of 3.3 urban centers per city. Approximately 68% of the cities had significant polycentric structures. The developed method in this paper is useful for identifying the urban centers and can provide the reference to the city planning and construction

Exploring the Added Value of Population Distribution Indicators for Studies of European Urban Form

In Europe, landscape metrics dominate studies that apply quantitative analyses to urban form. Indicators describing population distribution in more detail than just population density, which in Europe are often neglected because of the difficulty in data acquisition, are likely to be more adequate for describing the socioeconomic perspective on urban form. This study aims to disclose the linkage between landscape metrics and population distribution metrics and to provide a better understanding of population distribution patterns. In our study, we quantified urban form in 35 European cities using the most common indicators from both groups of indicators, including measures for the gradient of population density with distance from the city center or (in-) equality of population distribution. We found that landscape metrics correlate only weakly with population distribution indicators by analyzing the correlation matrix. To obtain more insight into the largely neglected group of population distribution indicators, we also applied a regression analysis to understand their underlying information. The results show that population distribution indicators are related to other basic characteristics of cities, such as planning coordination and latitude. The indicated influence of national planning regime on urban form could stimulate further discussion on the effectiveness of urban planning measures. Our study demonstrates that population distribution indicators provide a different perspective than landscape metrics in describing urban form. We therefore stress that it is essential to include population distribution indicators also for describing European cities when aiming to comprehensively describe urban form

A New Approach to Land-Use Structure. Patch Perimeter Metrics as a Spatial Analysis Tool

This work introduces a new class of landscape metrics characterizing basic features of patch perimeters. Specific computation on patch perimeters was carried out on fine-grained land-use maps with the aim to characterize spatial patterns of neighbor patches, evidencing contact points and perimeter length between two (or more) land-use types. A detailed set of class and landscape metrics were derived from such analysis. This approach is complementary to classical landscape metrics and proved to be particularly useful to characterize complex, fragmented landscapes profiling metropolitan regions based on integrated evaluations of their structural (landscape) and functional (land-use) organization. A multivariate analysis was run to characterize distinctive spatial patterns of the selected metrics in four metropolitan regions of southern Europe reflecting different morphological configurations (Barcelona: compact, polycentric; Lisbon: dispersed, mono-centric; Rome: dispersed, polycentric; and Athens: compact, mono-centric). Perimeter metrics assumed different values for each investigated land-use type, with peculiar characteristics associated to each city. Land-use types assessing residential, discontinuous urban patches were associated to particularly high values of perimeter metrics, possibly indicating patch fragmentation, spatially-associated distribution of land-use types and landscape complexity. Multivariate analysis indicates substantial differences among cities, reflecting the range of morphological configurations described above (from compact mono-centric to dispersed polycentric) and suggesting that urban expansion is accompanied with multiple modifications in the use of the surrounding non-urban land. The computational approach proposed in this study and based on spatially-explicit metrics of landscape configuration and proximity may reflect latent changes in local socio-spatial structures. Our results demonstrate that scattered urban expansion determines a polarization in suburban areas with highly fragmented and more homogeneous landscapes, respectively, associated with mixed cropland and forest systems

Megacities Spatiotemporal Dynamics Monitored with the Global Human Settlement Layer

Megacities are urban agglomerations hosting at least 10 million inhabitants. The rise in number, population size, and spatial extent of megacities are among the most prominent manifestations of the process of urbanisation taking place in the contemporary urban age. Until recently, urban growth has been quantified with data derived from satellites mainly for single megacities or for a limited subset of them. With the current advances in Remote Sensing and data processing, the integration of satellite data with other datasets could become a key contributor to the data revolution and support more complete urban studies and better informed policymaking. Although many remote sensing-derived products exist, few are open and free and possess the adequate resolution, information and contents to monitor the process of urban expansion. This research article builds on the premier open and free geospatial information contained in the Global Human Settlements Layer (GHSL) data package (produced at the European Commission - Joint Research Centre). This research takes advantage of existing GHSL data to identify megacities and to analyse their spatial and demographic change over the last 25 years (between 1990 and 2015). This paper quantifies how much and how fast megacities have expanded in spatial and demographic terms, and we provide graphical examples of the different manifestations of growth across megacities. The main findings of our research reveal an average demographic growth in megacities exceeding 2% a year between 1990 and 2000, and of 1.9% a year between 2000 and 2015. In the first period (1990 to 2000), megacities have expanded faster than the global average and more than the average of other urban centres. In the second period, global urban population increase has been greater than that of megacities. The comparative analysis of megacities however, reveals swift population growth in several cases: in seven cities population more than doubled between 1990 and 2015, and in six the average annual population growth exceeded 4% a year. Spatial expansion of megacities tends to occur at rates slower than that of population. In 27 cities built-up per capita has decreased over 25 years, by more than 10% in 17 cities. Megacities also differ in population density (in 2015), which in five is above 10,000 inhabitants per square kilometre, while in others, especially the ones in high-income countries, density remains around half this figure. Results highlight the value of new remote sensing-based data and methods for mapping and characterizing global urbanisation processes, in a consistent and comparable manner across space and time. The provision of open and free data ensures methods and findings can be audited and analyses extended to other cities, while the temporal dimension enables monitoring urbanisation and intergovernmental policies on sustainable urban development

Urban growth pattern identification : a case study in Siem Reap, Cambodia

The main purpose of this paper is to identify the pattern of urban growth from 1993 to 2011 in Siem reap town, Cambodia. Land use and land cover maps were generated from Landsat TM imageries from different years in order to extract the information related to urban sprawl. The settlement pattern theory, geographic pattern analysis and visualisation interpretation were used to detect the pattern of urban growth in Siem Reap. Result shows that from 1993 to 2011 the urban area grew significantly, about 102.51%. The development of core settlement areas in Siem Reap revealed to be concentrated along main roads and along the river in the past and still keeping the same trend in the present. The current pattern of urban settlement in Siem Reap was classified as clustered and linear, following the roads network