scholarly journals Exploring multi-scale spatial relationship between built environment and public bicycle ridership: A case study in Nanjing

2020 ◽  
Vol 13 (1) ◽  
pp. 447-467
Author(s):  
Cheng Lyu ◽  
Xinhua Wu ◽  
Yang Liu ◽  
Zhiyuan Liu ◽  
Xun Yang

A public bicycle system (PBS) is a promising countermeasure for the traffic issues induced by rapid urbanization, and it is widely acknowledged that the built environment has a significant impact on the use of a PBS. However, as the urban built-up area expands, different regions within a city can exhibit diverse characteristics. The spatial effects and differences among regions have been neglected by existing studies. To better understand how the urban built environment affects PBS ridership, this study conducts a quantitative analysis of the spatial relationship. It introduces a multi-scale geographically weighted regression (MGWR) to accomplish this task and conducts and evaluates a case study of the PBS in Nanjing, China. Six types of “D” variables (density, diversity, design, destination accessibility, distance to transit, and demand management) are involved in the analysis. The proposed method outperforms linear regression and standard geographically weighted regression (GWR) in terms of explanatory power. The modeling results demonstrate different influencing patterns between traditional downtown areas and newly built-up areas, especially for the density of population, road network, parking space, and various points of interest.

Land ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 500
Author(s):  
Chengjie Yang ◽  
Ruren Li ◽  
Zongyao Sha

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.


2019 ◽  
Vol 8 (10) ◽  
pp. 431 ◽  
Author(s):  
Shiwei Zhang ◽  
Lin Wang ◽  
Feng Lu

In China, the housing rent can clearly reveal the actual utility value of a house due to its low capital premium. However, few studies have examined the spatial variability of housing rent. Accordingly, this study attempted to determine the utility value of houses based on housing rent data. In this study, we applied mixed geographically weighted regression (MGWR) to explore the residential rent in Nanjing, the largest city in Jiangsu Province. The results show that the distribution of residential rent has a multi-center group pattern. Commercial centers, primary and middle schools, campuses, subways, expressways, and railways are the most significant influencing factors of residential rent in Nanjing, and each factor has its own unique characteristics of spatial differentiation. In addition, the MGWR has a better fit with housing rent than geographically weighted regression (GWR). These research results provide a scientific basis for local real estate management and urban planning departments.


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