Investigating urbanization and its spatial determinants in the central districts of Guangzhou, China

Chen, Yingbiao; Chang, Kang‐Tsung; Han, Fuzhuang; Karácsonyi, Dávid; Qian, Qinglan

doi:10.1016/j.habitatint.2015.10.013

Cited by 32 publications

(18 citation statements)

References 33 publications

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“…The rapid urbanization processes and associated land-use change in PRD have been reported by previous studies [23,24]. As the largest city in this region, Guangzhou rapidly grew and restructured into a multi-nucleus city during the past thirty years ( Figure 2) [25]. Compared with the other cities in PRD, however, Guangzhou has unique urban growth drivers, such as the constructions of large city projects (e.g., the University Town), hosting international sport events (e.g., the 2010 Asian Games) and the encouragements of real estate development [26][27][28].…”

Section: Study Area and Datasupporting

confidence: 57%

Analyzing Parcel-Level Relationships between Urban Land Expansion and Activity Changes by Integrating Landsat and Nighttime Light Data

Chen

Liu

2017

Remote Sensing

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Urban growth is a process that imposes profound physical and socioeconomic restructuring on cities. Urban land expansion as an immediate physical manifestation of urban growth has been extensively studied using a variety of remote sensing methods. However, little research addresses the interactions between urban land expansion and corresponding activity changes, especially at local scales. We propose an innovative analytical framework that integrates Landsat and nighttime light data to capture the parcel-level relationships between urban land expansion and activity changes. The urban land data are acquired based on the classification of Landsat images, whereas the activity changes are approximated by the nighttime light data. Using the Local Indicator of Spatial Association (LISA) (local Moran's I) approach, four types of local relationships between land expansion and activity changes are defined at the parcel level. The proposed analytical framework is applied in Guangzhou, China, as a case study. The results reveal the mismatched growth between urban land and activity intensity at the parcel level, where the increase in urban land area outpaces the increase of activity intensity. Such results are expected to provide a more comprehensive understanding of urban growth, and can be used to assist urban planning and related decision-making.

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Section: Study Area and Datasupporting

confidence: 57%

Analyzing Parcel-Level Relationships between Urban Land Expansion and Activity Changes by Integrating Landsat and Nighttime Light Data

Chen

Liu

2017

Remote Sensing

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“…The SEM assumes spatial dependence in the error term of OLS and decomposes the error term in Eq 1 into two terms ( and below) (Anselin, 2003;Chen et al, 2016). The general form of this model is: (Ward and Gleditsch, 2018)…”

Section: Spatial Error Model (Sem)mentioning

confidence: 99%

GIS-based spatial modeling of COVID-19 incidence rate in the continental United States

Mollalo

Vahedi

Rivera

2020

Science of The Total Environment

590

584

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Journal Pre-proof J o u r n a l P r e -p r o o f 2 GIS-based Spatial Modeling of COVID-19 Incidence Rate in the Continental United States AbstractDuring the first 90 days of the COVID-19 outbreak in the United States, over 675,000 confirmed cases of the disease have been announced, posing unprecedented socioeconomic burden to the country. Due to inadequate research on geographic modeling of COVID-19, we investigated county-level variations of disease incidence across the continental United States. We compiled a geodatabase of 35 environmental, socioeconomic, topographic, and demographic variables that could explain the spatial variability of disease incidence. Further, we employed spatial lag and spatial error models to investigate spatial dependence and geographically weighted regression (GWR) and multiscale GWR (MGWR) models to locally examine spatial non-stationarity. The results suggested that even though incorporating spatial autocorrelation could significantly improve the performance of the global ordinary least square model; these models still represent a significantly poor performance compared to the local models. Moreover, MGWR could explain the highest variations (adj. R 2 : 68.1%) with the lowest AICc compared to the others. Mapping the effects of significant explanatory variables (i.e., income inequality, median household income, the proportion of black females, and the proportion of nurse practitioners) on spatial variability of COVID-19 incidence rates using MGWR could provide useful insights to policymakers for targeted interventions.

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“…Our analyses depict that the efficiency of spatial regression methods is higher than that of the OLS as a global regression model (Tables II–IV). This is because of considering of the spatial dependence in the spatial regression techniques between the deforestation rate and residential area growth variables, which this priority has reflected by other literatures (Jat et al, ; Wu et al, ; Naibbi & Healey, ; Zhang et al, ; Chen et al, ). Moreover, the GWR model performed better than the SL model in explaining the relationship between forest loss and residential growth in the first and third periods.…”

Section: Resultsmentioning

confidence: 99%

“…Local spatial analysis creates a relationship between the results of spatial techniques and visualization capabilities of Geographial Information Science (GIS) (Fotheringham et al, 2002), whereas the spatial patterns are ignored in correlations of global statistics (Wu et al, 2010). Hence, spatial regression techniques, including the spatial lag (SL) model, spatial error model (Lloyd, 2010;Anselin & Rey, 2014) and geographically weighted regression (GWR; Leung et al, 2000;Fotheringham et al, 2002), have been used to analyse the spatial determinants in deforestation (Wu et al, 2010;Naibbi & Healey, 2014) and residential growth (Jat et al, 2008;Zhang et al, 2015;Chen et al, 2016). GWR is actually a spatial technique that simultaneously applies (X, Y) or (long, lat) locations with attribute fields in regression analysis.…”

Section: Introductionmentioning

confidence: 99%

Analysing Spatial and Statistical Dependencies of Deforestation Affected by Residential Growth: Gorganrood Basin, Northeast Iran

et al. 2017

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This study aimed to examine deforestation and residential growth trends and their spatial dependencies from 1972 to 2010 in Northeast of Iran. First, change rates of forests and residential areas were mapped using Landsat satellite images in 1972–1987, 1987–2000 and 2000–2010. Then, the forest change patterns were interpreted using univariate local Moran's I (local univariate spatial autocorrelation), and the spatial autocorrelation between deforestation and residential growth was tested through bivariate local Moran's I (bivariate local spatial autocorrelation). Furthermore, the spatial relationships between deforestation and residential growth rates were quantified by ordinary least squares, spatial lag (SL) and geographically weighted regression. Results indicated that approximately 25% of forests have been converted to other land‐use types in the span of 38 years, since 1972. Local univariate spatial autocorrelation maps showed that significant values of high–high cluster scattered in all locations in the first span, in the east and south aspects in the second duration, and in the eastern part in the third span. Bivariate local spatial autocorrelation indicated a meaningful Moran's I values of −0·12, −0·26 and −0·20 between deforestation and residential growth, chronologically. Analyses of spatial regression models showed that geographically weighted regression performed better than SL and ordinary least squares in the first (R2 = 0·315, AIC = 6,160) and third periods (R2 = 0·27, AIC = 6,351), whereas, the validity of SL was the highest in the second period (R2 = 0·36, AIC = 6,288). However, the overall trends of deforestation and residential growth have decreased, but the rate of deforestation induced by residential growth is still significant. Spatial exploration of residential growth in deforestation leads to determine its influences in local scale for better conservation of these valuable natural resources. Copyright © 2017 John Wiley & Sons, Ltd.

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Investigating urbanization and its spatial determinants in the central districts of Guangzhou, China

Cited by 32 publications

References 33 publications

Analyzing Parcel-Level Relationships between Urban Land Expansion and Activity Changes by Integrating Landsat and Nighttime Light Data

Analyzing Parcel-Level Relationships between Urban Land Expansion and Activity Changes by Integrating Landsat and Nighttime Light Data

GIS-based spatial modeling of COVID-19 incidence rate in the continental United States

Analysing Spatial and Statistical Dependencies of Deforestation Affected by Residential Growth: Gorganrood Basin, Northeast Iran

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