Normalized Difference Built-up Index Research Articles

Mapping Rural Settlements from Landsat and Sentinel Time Series by Integrating Pixel- and Object-Based Methods

Rural settlements account for 45% of the world’s population and are targeted places for poverty eradication. However, compared to urban footprints, the distribution of rural settlements is not well characterized in most existing land use and land cover maps because of their patchy and scattered organization and relative stability over time. In this study, we proposed a pixel- and object-based method to map rural settlements by employing spectral-texture-temporal information from Landsat and Sentinel time series. Spectral indices (maximum normalized difference vegetation index (NDVI) and minimum normalized difference built-up index (NDBI composite) and texture indices (vertical transmit and vertical receive (VV) polarization of mean synthetic aperture radar (SAR) composite) were calculated from all available Landsat and Sentinel-1A data from 1 January 2016 to 31 December 2018. These features were then stacked for segmentation to extract potential rural settlement objects. To better differentiate settlements from bare soil, the gradient of annual NDVI maximum (namely, gradient of change, use gradient for simplicity) from 1 January 1987 to 31 December 2018 was used. The rural training samples were selected from global urban footprint (GUF) products with a post filtering process to remove sample noise. Scatter plots between pixel- and object-based values per feature were delineated by t-distribution ellipses to determine the thresholds. Finally, pixel- and object-based thresholds were applied to four features (NDVI, NDBI, VV, gradient) in Google Earth Engine (GEE) to obtain the distribution of rural settlements in eight selected Asian regions. The derived maps of rural settlements showed consistent accuracy, with a producer’s accuracy (PA) of 0.87, user’s accuracy (UA) of 0.93 and overall accuracy (OA) reaching 90% in different landscape conditions, which are better than existing land cover products.

Spatially Filtered Multilevel Analysis on Spatial Inequality of Tuberculosis in Gyeongsangbuk-do, Korea

Objectives: This study aims to identify the spatial inequality of tuberculosis (TB) and related socio-environmental factors in Gyeongsangbuk-do, Korea. Methods: This study explains TB incidences in a spatial perspective. A spatial multilevel regression model was applied to account for the spatial inequality of TB and related factors. Applied socio-environmental factors were average age, population density, Normalized Difference Built-up Index (NDBI), particulate matter (PM<sub>10</sub>), medical insurance, financial independence rate, and medical doctors per 1,000 people. Results: This study finds evident spatial inequality of TB in Gyeongsangbuk-do. Most TB hotspots are identified around Yeongyang-gun, the area with the lowest socio-economic status in Korea. The regression result shows that level 1 factors (Average age, NDBI, PM<sub>10</sub>) and level 2 factors (medical insurance, financial independence rate) were statistically significant related to TB incidences. Furthermore, spatial filtering applied to multilevel regression accounts for spatial autocorrelation of TB incidence. Conclusions: Regional socio-economic and demographic conditions in Gyeongsangbuk-do influence TB incidences. Given low expectations on Gyeongsangbuk-do’s socio-economic and demographic growth, reducing the spatial inequality of TB would be challenging.

Anthropogenic drivers and impacts of urban flooding- A case study in Lower Kelani River Basin, Colombo Sri Lanka

Flooding is the most frequent natural disaster among the top ranking disasters causing destructive effects on people and property of Sri Lanka. Colombo, the commercial capital of Sri Lanka and surrounding suburban areas were experiencing a significant flood events during the last couple of decades. The intensified anthropogenic activities have accelerated severity and frequency of recent floods. Therefore this study was attempted to investigate the pressure generated by the population on the land by human activities causing frequent and severe flooding in Kolonnawa DS Division, Sri Lanka. A questionnaire survey and focus group discussions were carried out to collect primary data whilst collecting secondary data such as long term annual flood peak records, flood inundation data from different government agencies. The gridded population data was obtained from Oak Ridge National Laboratory (LandSacn) from 2000 to 2018 to examine the population pressure generated from the commercial capital to the study area. GIS and Remote Sensing based spatial analysis such as Change Detection, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Zonal Statistics and Concentric Circles were used interactively in data analysis. The results reveled that there is an influence of anthropogenic activities of area on recent flood events, severity and damages. Results of NDVI and NDBI analysis depicted a significant decline in green cover. Marshy lands have decreased by 42% from 2005 to 2017. The study further indicated a conversion of paddy and wetlands in to home gardens from 1972 to 2018 due to suburbanization. The decreasing trend of marshy lands, home gardens and paddy lands occurred due to the population pressure generated from the commercial city. Hence, the results of the study would be useful for policy makers and urban planners in developing future flood mitigation plans in study area.

A Soil and Impervious Surface Adjusted Index for Urban Impervious Surface Area Mapping

Index-based methods are widely applied to urban impervious surface area (ISA) mapping, but the confusion between ISA and soil remains unsolved. In this article, the near-infrared (NIR)-blue bands were selected as feature space by analyzing the spectra from the US Geological Survey spectral library, and a simple impervious surface ratio index (ISRI) was developed by shifting the NIR-blue coordinate origin toward the convergence point of the fitting lines of ISA and soil. The ISRI was then validated for threshold simulation, separability, and correlation analysis. Results demonstrated that ISRI had a good performance for ISA mapping in four cities in China with different geographic environments, with all extraction accuracies all above 90%. ISRI had a high separability between ISA and soil and was better than other indices (normalized difference built-up index and biophysical composition index). Further, ISRI has a close relationship with the ISA proportion. Therefore, ISRI would be a simple and reliable index for urban ISA mapping.

Monitoring the Spatial Variation of Aerosol Optical Depth and Its Correlation with Land Use/Land Cover in Wuhan, China: A Perspective of Urban Planning.

Aerosols significantly affect environmental conditions, air quality, and public health locally, regionally, and globally. Examining the impact of land use/land cover (LULC) on aerosol optical depth (AOD) helps to understand how human activities influence air quality and develop suitable solutions. The Landsat 8 image and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products in summer in 2018 were used in LULC classification and AOD retrieval in this study. Spatial statistics and correlation analysis about the relationship between LULC and AOD were performed to examine the impact of LULC on AOD in summer in Wuhan, China. Results indicate that the AOD distribution expressed an obvious “basin effect” in urban development areas: higher AOD values concentrated in water bodies with lower terrain, which were surrounded by the high buildings or mountains with lower AOD values. The AOD values were negatively correlated with the vegetated areas while positively correlated to water bodies and construction lands. The impact of LULC on AOD varied with different contexts in all cases, showing a “context effect”. The regression correlations among the normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference water index (NDWI), and AOD in given landscape contexts were much stronger than those throughout the whole study area. These findings provide sound evidence for urban planning, land use management and air quality improvement.

Appraisal of environment quality in the capital district of Cameroon using Landsat-8 images

The main purpose of this study was to apply remote sensing techniques to properly address the questions of sustainability, environment quality as well as life comfort, in capital district of Cameroon, Yaounde. The effects of urban heat island were highlighted with LST ranging from 14 °C in the vegetated and less developed southern area, to 34 °C in the high built-up and overpopulated northern zone. Results reveal that the LST amplitude is greater than the minimum temperature in the city. LST values are strongly correlated to the normalised difference built-up index and to the normalised difference vegetation index positively (r = +0.8529) and negatively (r = −0.8452) respectively. The derived urban thermal field variance index classifies 50.54 % of the city in the category of worst environment quality. The spatial distribution of the later index is analogic to the global solar radiation in the city, and indicates that populations living in the most part of the study area are likely to be at risk of several health issues. Several suburbs are advised. The Government has to immediately redesign Yaounde as well as other cities, by giving an important part to the urban forest and vegetation cover.

Using Remote Sensing in Monitoring the Urban Green Spaces: A Case Study in Qorveh, Iran

Urban green spaces are important to the physical activity, mental wellbeing, and health of urban residents. In medium-sized cities in Iran, urban green spaces have been ignored during the past decades. In this study, remote sensing and geographic information system was used to investigate the green space distribution in the built-up areas. Accordingly, normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) were used to estimate urban green spaces distribution within the urban landscapes to measure the urban land use effect. The geospatial analysis results presented a lack of green spaces in built-up areas. Also, 16.8% of the total area of Qorveh city is considered as a dysfunctional urban fabric that 39.4% of the city’s population live in this area. This study highlighted the usage of green space index with geospatial analyses in illustrating the urban green spaces’ importance for planning. For the future urban land development scenarios, this approach could be used linking with regional planning approaches.

Impact of Urbanization on Groundwater Levels in Rawalpindi City, Pakistan

Variations in the rate of urbanization directly impact groundwater levels and quality. Therefore, the present study examines the relationship between changes in land-use and land-cover (LULC) and groundwater drawdown in Rawalpindi, Pakistan. Landsat images, i.e., Operational Land Imager (OLI), Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+), were downloaded for the years 1991, 1997, 2007, 2010, and 2017. The study area was classified using the normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) to create three classes, i.e., urban area, vegetation, and barren land. The groundwater level in the study area for the year 2017 was obtained using an electrical resistivity survey (ERS) with a Schlumberger configuration. The data obtained were interpreted using IX1D iteration software. The results of NDBI and NDVI showed that the urban area increased by 37.89% during the period 1991–2017, at the expense of vegetation. Similarly, the groundwater level was found to decrease at a rate of 1.38 m per annum. If the same trend prevails, the groundwater level will decrease to approximately 160 m from the natural ground surface by the end of this century. The results of the present study may be used for the formulation of policy and proper planning prior to any major developmental project to control the impact of LULC changes on water resources in the future.

Proposing an effective and inexpensive tool to detect urban surface temperature changes associated with urbanization processes in small cities

A consequence of urbanization is the loss of vegetation cover, replaced by urban areas. This study investigates the evolution of the built-up areas and its influence on increasing the surface temperature through methodology applicable to small and medium-sized municipalities where urbanization process monitoring is limited. For the case study, the municipality of Penedo, located in the State of Alagoas, was selected. Landsat-5 and Landsat-8 images were used to evaluate urban landscape through normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and the land surface temperature (LST). A simple tool that summarizes NDBI and LST changes was proposed - the Change Detection Index (CDI). It was possible to highlight areas where the increase in temperatures is associated with the built area's intensification. Furthermore, the machine learning k-means clustering tool was applied to create groups and analyze the relationship between urbanization and temperature increase, confirming the CDI values and validating this tool. An evident increase in the built area was highlighted by CDI and cluster analysis, indicating a heat island's potential formation at the implantation area of a housing complex in the municipality. The spatial analysis indicates priority areas for intervention by a simple methodology that facilitated dialogue and understanding between stakeholders, associated with a low-cost tool with the potential for reproduction and adaptation, providing indicators to urban studies and decision-makers and handle the lack of monitoring data in small and medium-sized municipalities.

Application of geospatial indices for mapping land cover/use change detection in a mining area

Mining contributes significantly to the GDP of South Africa but it also comes with some adverse impacts on the environment. In this study, remote sensing was used to quantify land-use/cover changes in the Welkom – Virginia Goldfields. The aim was to analyse Landsat images with a 5-year interval from 1988 to 2018 using geospatial indices: Global Environmental Monitoring Index (GEMI), the Normalized Difference Built-up Index (NDBI), the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Soil Index (NDSI) and the Normalized Difference Water Index (NDWI) to discriminate different land cover types. Supervised classification with the maximum likelihood method was used to classify the images into appropriate classes. Findings revealed different land-use changes with fluctuations in values for each index with an overall accuracy of the classified images ranging from 88% to 96% respectively. Hence these indices are reliable for mapping and monitoring land use/cover changes in mining areas over a large extent.

The localization of urban heat island in the Katowice conurbation (Poland) using the combination of land surface temperature, Normalized Difference Vegetation Index and Normalized Difference Built-up Index

The localization of Surface Urban Heat Island (SUHI) as a potential heat risk for the urban population was evaluated. The paper aimed to propose an approach to quantify and localize (SUHI) based on Landsat series TM, ETM+, OLI satellite imageries from the period 1996-2018 and recognize the Atmospheric Urban Heat Island (AUHI) effects from long term temperature measurements. Using the theoretical relation between the Normalized Difference Built-up Index (NDBI), the Normalized Difference Vegetation Index (NDVI) and the LST (Land Surface Temperature), SUHIintensity and SUHIrisk maps were created from the combination of LST, NDVI, NDBI using threshold values to localize urban heat island in the Katowice conurbation. Negative values of SUHI intensity characterize areas where there is no vegetation, highly built-up areas, and areas with high surface temperatures. The urban grow – revealed from SUHI – and global climate change are acting together to strengthen the global AUHI effect in the region as the temperature measurements were indicated.

Analysis of landslide potential due to changes of land use/land cover at the Kuranji watershed, Padang using normalized difference built-up index (NDBI)

The earthquake and tsunami predictions in the city of Padang have caused very rapid land-use changes, especially in the Kuranji watershed, where people tend to seek locations that are safe from tsunamis and liquefaction. Changes in environmental characteristics such as slope geometry conditions, vegetation density, and changes in land use will affect runoff coefficient and rainwater filtration, triggering a potential for landslides. This study aims to analyze the potential for landslides due to changes in land use in the Kuranji Watershed. The identification of land-use change is carried out using a remote sensing approach, namely the Normalized Difference Built-Up Index (NDBI). Landslide potential is determined based on the relationship between land use and runoff coefficient from 2007 to 2019. The results showed there had been an increase in the built-up area in the Kuranji watershed from 1602.212 ha (2007) to 2897.513 ha (2019). In contrast, the vegetation area has decreased. An increase in the runoff coefficient of 3.9% from 2007 to 2019. The final results of this study are thematic geospatial information obtained in the form of the relationship between changes in land use and the potential for landslides that occurred in the Kuranji watershed during the period 2007 to 2019.

Balancing Land Surface’s Brightness-Shadowing and Spectral Reflectance to Enhance the Discrimination of Built-up Footprint from Surrounding Noise

Recent evolutions of the geospatial technologies are more accurate in mapping and monitoring land use land cover, LULC, in different environments and at different spatial scales. However, some urban applications keep facing issues such as misclassification and other noise in unplanned cities with disorganized built-up and mixed housing material, and surrounded by a composed biophysical environment. This paper reports the processing leading to a new spectral index, that balances the land surface brightness temperature and spectral reflectance to accurately extract the built-up. The namely Brightness Adjusted Built-up Index, BABI, is proposed as a weighted ratio of Landsat OLI-TIRS bands. The methodology is based on a multi-perceptron layers, MLP, regression between a classified image and individually classified red, SWIR1, SWIR2 and TIR bands reclassified “1 = built-up; 0 = Non-Built-up”, with an average r2=0.78. The same way, a linear regression of popular built-up spectral indices such as Normalized Difference Built-up Index, NDBI, and Urban Index, UI, or recently proposed Modified New Built-up Index, MNBI, and Normalized Difference Built-up and Surroundings Unmixing Index, NDBSUI, on one hand, by light-dark spectral indices such as, Normalized Difference Soil Index, NDSI, Bare Soil Index, BSI, and Shadow index on the other hand, stands for the natural environment noise assessment in and around the built-up, with an r2=0.75. The MLP r2 standing for the built-up information, is rounded to 0.8 and according to their rank in the process, the weights allotted are 0.2, 0.4 and 0.8 in the numerator, and inversely 0.8, 0.6 and 0.2 in the denominator, to the red, SWIR1 and SWIR2 bands respectively. Whereas, the simple linear regression r2 standing for the noise is used to weigh the brightness temperature, TB in the numerator and subtracted from the previous group. The value 0.001 multiplies the whole ratio to lower the decimals of the outputs for an easy interpretation. As results, on the floating images scaled [0-1], built-up values are ≥0.1 in Yaounde (Cameroon) and ≥0.07 in Bangui (Central African Republic). The overall accuracies are 96% in Yaounde and 98.5% in Bangui, with corresponding kappa coefficients of 0.94 and 0.97. These scores are better than those of the NDBI, UI, MNBI and NDBSUI.

A long-term monthly analytical study on the relationship of LST with normalized difference spectral indices

ABSTRACT This study analyzes the long-term monthly variation of land surface temperature (LST) and its relationship with normalized difference vegetation index (NDVI), normalized difference water index (NDWI), normalized difference built-up index (NDBI), and normalized difference bareness index (NDBaI) in the Raipur City of India using one hundred and twenty-three Landsat images from 1988-2020. In terms of LST, the warmest month is April (38.49oC) and the coldest month is January (23.04oC). The standard deviation in LST is noticed as 1.1022oC throughout the period. The growth pattern of LST is increasing in the earlier stage while it is steady and decreasing in the later stage. The mean linear regression coefficients for LST-NDVI is -0.42, LST-NDBI are 0.68, LST-NDWI is 0.27, and LST-NDBaI is 0.32. It indicates that the high ratio of green vegetation and water bodies resist the raise of LST, whereas the bare rock surface and built-up land accelerate the LST. The value of the spectral indices and LST varies with the change of month due to the physical change of the land surface materials. Hence, the study will be an effective one for the town and country planners for their future estimation of land conversion.

Mapping Impervious Surface Distribution and Dynamics in an Arid/Semiarid Area-A Case Study in Ordos, China

The impervious surface area (ISA) has become an important parameter in the areas such as surface runoff, climate change, and socioeconomic analysis. However, using remote sensing data to accurately map ISA is still difficult due to its complexity in the arid and semi-arid areas with large amounts of bare soil, sparse vegetation, and a small ISA. In this study, we have selected Ordos city as study area and examined the spatial pattern and dynamics in the years of 1990, 1995, 2000, 2005, 2010, 2015, and 2019 by using multiple data sources with a combination method. The results indicate that the ISAs in 2010, 2015, and 2019 were extracted with overall accuracies of 90.6%, 89.2%, and 91.8% with kappa coefficients of 0.79, 0.76, and 0.82, respectively. The ISA extraction results of the Urban Index (UI), Normalized Difference Built-Up Index (NDBI), Index-based Built-Up Index (IBI), and Impervious Built-up Index from optical sensors (IBUIopt) were used for comparison. Meanwhile, a correlation analysis between socioeconomic factors and the ISA of the typical resource-based city of Ordos was conducted. The spatial pattern and dynamics of the ISA data in Ordos city are of great significance to the study of socioeconomic and environmental changes and provide a reference to examine the effect of ISAs in other resource-based cities.

Spatial Downscaling of MODIS Land Surface Temperature Based on a Geographically and Temporally Weighted Autoregressive Model

Land surface temperature (LST) is a key parameter in numerous environmental studies. However, currently, there is no satellite sensor that can completely provide LST data with both high spatial and high temporal resolutions simultaneously. LST downscaling is regarded as an effective remedy for improving the temporal and spatial resolutions of LST data. In this article, a geographically and temporally weighted autoregressive (GTWAR) model of LST downscaling is that comprehensively considers the spatial heterogeneity, spatial autoregression, and temporality of LST is newly proposed. The normalized difference water index, the normalized difference built-up index, and the normalized difference vegetation index were selected as explanatory variables to downscale the moderate resolution imaging spectroradiometer (MODIS) LST from 1000 to 100 m, while the Landsat 8 LST was selected as the reference data. Compared with the thermal data sharpening (TsHARP), the geographically weighted regression (GWR), the geographically weighted autoregressive (GWAR) and the geographically and temporally weighted regression (GTWR) downscaling methods, the proposed method was superior based on quantitative indices, with the lowest root mean square error (Zhangye: 1.57 °C, Beijing: 1.22 °C) and mean absolute error (Zhangye: 1.06 °C, Beijing: 0.85 °C). The downscaling model of GTWAR will facilitate improvements in the accuracy of downscaling for temporal series of LST data.

Persebaran Fenomena Suhu Tinggi melalui Kerapatan Vegetasi dan Pertumbuhan Bangunan serta Distribusi Suhu Permukaan

Urban heat island is one of the events where the temperature in the metropolitan area is warmer than the surrounding area. Indonesia is located in a tropical climate making several cities potentially increase in temperature during the dry season. As a representative, the City of Pekanbaru is one of the cities with infrastructure growth that is fast including on the island of Sumatra. Population growth and high enthusiastic of residents to find work in the city made land on the edge of the city built up. This makes the green space less and less with the uneven distribution of green space in the city of Pekanbaru. The number of buildings that exist makes the temperature rise in urban areas with an initial temperature of 29oC to 36oC. temperature increases occurred throughout the year with the hottest peaks of temperatures reaching 37oC on April 15, 2013. Whereas in 2009 there was a decrease in temperature with levels ranging from 27oC. This makes the city of Pekanbaru one of the hottest cities in Indonesia. This study uses Landsat 5 imagery for 2000 and 2009, and Landsat 8 for 2018 with the Land Surface Temperature (LST) method and Normalized Difference Built-up Index (NDBI) which are useful to explain the urban heat island distribution (UHI) in Pekanbaru City

Simulation of future land surface temperature distribution and evaluating surface urban heat island based on impervious surface area

This study aims to simulate the future form of Land Surface Temperature (LST) distribution and Surface Urban Heat Island (SUHI) evaluation based on the impervious surface area. For this purpose, Landsat-5 and Landsat-8 satellite data acquired in 2006, 2011 and 2016, respectively, were utilized, and Zonguldak, a province of Turkey, was selected as a test site. In the study, LST image for 2021 was projected using spectral indices, namely Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI). Actually, the correlation analysis between four spectral indices (NDVI, SAVI, MNDWI and NDBI) and LST was conducted. However, NDBI and NDVI were determined as highly correlated indices with LST having correlation coefficient 0.89 and −0.90, respectively, thus MNDWI and SAVI were not considered for the further steps. A Multiple Linear Regression (MLR) analysis was carried out between these two indices (NDBI and NDVI) and LST to extract a mathematical model for LST retrieval. In order to obtain the future form of LST image (2021), these indices were simulated for 2021 using Markov Chain method and then regression model was applied. Before generating LST image for 2021, LST for 2016 image was simulated and compared with the existing 2016 LST image using Kappa index of agreement technique to evaluate the performance. The Kappa value was obtained as 0.73 which is reasonable for future simulation. SUHI evaluation was conducted based on the observed (2016) and simulated (2021) impervious area and normalized LST images. The obtained results showed that the distribution of SUHI effect was highly related to impervious area and if the urbanization rate in the study area continues as it is, this expansion will cause to a dramatic increase in SUHI distribution unless any proper urban planning model is considered by decision makers.

Analysis on Thermal Islands Effectors in Ramadi City, Iraq Using Multi-temporal Landsat Images

This paper analyzes the effects of urban density, vegetation cover, and water body on thermal islands measured by land surface temperature in Al Anbar province, Iraq using multi-temporal Landsat images. Images from Landsat 7 ETM and Landsat 8 OLI for the years 2000, 2014, and 2018 were collected, pre-processed, and anal yzed. The results suggested that the strongest correlation was found between the Normalized Difference Built-up Index (NDBI) and the surface temperature. The correlation between the Normalized Difference Vegetation Index (NDVI) and the surface temperature was slightly weaker compared to that of NDBI. However, the weakest correlation was found between the Normalized Difference Water Index (NDWI) and the temperature. The results obtained in this research may help the decision makers to take actions to reduce the effects of thermal islands by looking at the details in the produced maps and the analyzed values of these spectral indices.

Exploring the Dynamics of Urban Greenness Space and Their Driving Factors Using Geographically Weighted Regression: A Case Study in Wuhan Metropolis, China

Urban greenness plays a vital role in supporting the ecosystem services of a city. Exploring the dynamics of urban greenness space and their driving forces can provide valuable information for making solid urban planning policies. This study aims to investigate the dynamics of urban greenness space patterns through landscape indices and to apply geographically weighted regression (GWR) to map the spatially varied impact on the indices from economic and environmental factors. Two typical landscape indices, i.e., percentage of landscape (PLAND) and aggregation index (AI), which measure the abundance and fragmentation of urban greenness coverage, respectively, were taken to map the changes in urban greenness. As a case study, the metropolis of Wuhan, China was selected, where time-series of urban greenness space were extracted at an annual step from the Landsat collections from Google Earth Engine during 2000–2018. The study shows that the urban greenness space not only decreased significantly, but also tended to be more fragmented over the years. Road network density, normalized difference built-up index (NDBI), terrain elevation and slope, and precipitation were found to significantly correlate to the landscape indices. GWR modeling successfully captures the spatially varied impact from the considered factors and the results from GWR modeling provide a critical reference for making location-specific urban planning.