Quantifying urban areas with multi-source data based on percolation theory

Abstract

Quantifying urban areas is crucial for addressing associated urban issues such as environmental and sustainable problems. Remote sensing data, especially the nighttime light images, have been widely used to delineate urbanized areas across the world. Meanwhile, some emerging urban data, such as volunteered geographical information (e.g., OpenStreetMap) and social sensing data (e.g., mobile phone, social media), have also shown great potential in revealing urban boundaries and dynamics. However, consistent and robust methods to quantify urban areas from these multi-source data have remained elusive. Here, we propose a percolation-based method to extract urban areas from these multi-source urban data. We derive the optimal urban/non-urban threshold by considering the critical nature of urban systems with the support of the percolation theory. Furthermore, we apply the method with three open-source datasets -population, road, and nighttime light -to 28 countries. We show that the proposed method captures the similar urban characteristics in terms of urban areas from multi-source data, and Zipf's law holds well in most countries. The derived urban areas by different datasets show good agreement with the Global Human Settlement Layer (GHSL) and can be further improved by data fusion. Our study not only provides an efficient method to quantify urban areas with open-source data, but also deepens the understanding of urban systems and sheds some light on the multi-source data fusion in geographical fields.lacking in many developing countries. Second, the data collection process for MAs is time-consuming and expensive, making it unable to capture the rapid urbanization process in fast-growing regions. Third, the standard to define MAs is different in different countries. There is still a lack of a unified approach to obtain functional urban areas, which can be applicable to all countries.

Multifractal scaling analyses of urban street network structure: The cases of twelve megacities in China

“…Several algorithms have been constructed to delimit urban boundaries [ 48 – 51 ]. Among these algorithms, City Clustering Algorithm (CCA) has attracted great attention for its simplicity and efficiency [ 52 , 53 ]. CCA can be regarded as a method of spatial cluster.…”

iSEAM: Improving the Blooming Effect Adjustment for DMSP-OLS Nighttime Light Images by Considering Spatial Heterogeneity of Blooming Distance

“…mapping urban growth [2][3][4][5][6][7][8][9][10][11], modeling the spatial distribution of population density [12][13][14][15][16], carbon emissions [17][18][19] and economic activities [20][21][22][23][24], etc. Among the NTL data, the annually composite stable NTL product synthesized from NTL images collected by Defense Meteorological Satellite Program-Operational Line-scan System (DMSP-OLS) is the most commonly used because it has the longest historical time series from 1992 to 2013, a period with intensive human activity across the Millennium, while other NTL data do not cover such long period.…”

Development and analysis of land-use/land-cover spatio-temporal metrics in urban environments: Exploring urban growth patterns and linkages to socio-economic factors

“…With the proliferation of sensors in the last decades there is an ever-growing availability of satellite images covering spatially and temporally the Earth, which has subsequently boosted the development of new methods and techniques to characterize urban settings, transforming image data into reliable geo-information (Taubenböck, 2019). For instance, the categorical classification of images into LULC maps is valuable information for urban studies (Donnay et al, 2000;Hermosilla et al, 2012), the characterization of cities into urban structural types and land cover has great potential in its relation with urban functions (Bechtel et al, 2015), and the identification of built-up areas at different scales, from single building to urban settlements provides accurate representations of spatial patterns in urban areas (Hermosilla et al, 2011;Stiller et al, 2019;Cao et al, 2020;Qui et al, 2020).…”

“…A collection of available geospatial datasets at global and semi-global levels managed and maintained by institutions and agencies is summarized in Table 1.1, showing the great efforts that are being made in this regard. However, not only public entities, but also scholars are making available their datasets and codes to the scientific community (e.g., Inglada et al, 2017;Leyk and Uhl, 2018;Demuzere et al, 2019;Cao et al, 2020;Gong et al, 2020;Liu et al, 2020;Qui et al, 2020;Weigand et al, 2020), allowing for the comparison of results in order to evaluate them, detect potential weaknesses and agreements (Uhl et al, 2020), as well as to reproduce results in other areas and to update the datasets whenever new input data are available, improving the cross-comparability and multi-temporality of geospatial datasets. Most significantly, in the last few years there have been numerous attempts to create global maps of annual urban land coverage (Zhou et al, 2018;He et al, 2019;Cao et al, 2020;Gong et al, 2020;Liu et al, 2020;Qui et al, 2020), which are fundamental geo-information for climate change mitigation and monitoring urban expansion to support the SDGs.…”

“…Furthermore, the development of methods and algorithms to automatically classify urban environments across the globe is progressing rapidly (e.g. : Bechtel et al, 2015;Cao et al, 2020;Qui et al, 2020). The global coverage and high spatial and temporal resolutions of EO-derived products combined with the high capacity to automatize processes allows the frequent updating of geospatial datasets.…”