Determination of the Peak Hour Ridership of Metro Stations in Xi’an, China Using Geographically-Weighted Regression

The ridership of a metro station during a city’s peak hour is not always the same as that during the station’s own peak hour. To investigate this inconsistency, this study introduces the peak deviation coefficient to describe this phenomenon. Data from 88 metro stations in Xi’an, China, are used to analyze the peak deviation coefficient based on the geographically weighted regression model. The results demonstrate that when the land around a metro station is mainly land for work, primary and middle schools, and residences, its station’s peak hour is consistent with the city’s peak hour. Additionally, the station’s peak hour is more likely to deviate from the city’s peak hour for suburban stations. There are two ridership options when designing stations, namely the extra peak hour ridership during a city’s peak hour and that during a station’s peak hour, and the larger of the two is used to design metro stations. The mixed land use ratio must be considered in urban land use planning, because although non-commuting land can mitigate the traffic pressure of a city’s peak hour, it may cause the deviation of the station’s peak hours from that of the city.

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Application of geographically weighted regression model to analysis of spatiotemporal varying relationships between groundwater quantity and land use changes (case study: Khanmirza Plain, Iran)

Environmental Monitoring and Assessment ◽

10.1007/s10661-013-3605-5 ◽

2014 ◽

Vol 186 (5) ◽

pp. 3123-3138 ◽

Cited By ~ 22

Author(s):

Shahabeddin Taghipour Javi ◽

Bahram Malekmohammadi ◽

Hadi Mokhtari

Keyword(s):

Land Use ◽

Regression Model ◽

Geographically Weighted Regression ◽

Land Use Changes ◽

Weighted Regression ◽

Geographically Weighted Regression Model ◽

Groundwater Quantity

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Examining Hotspots of Traffic Collisions and their Spatial Relationships with Land Use: A GIS-Based Geographically Weighted Regression Approach for Dammam, Saudi Arabia

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9090540 ◽

2020 ◽

Vol 9 (9) ◽

pp. 540 ◽

Cited By ~ 2

Author(s):

Muhammad Tauhidur Rahman ◽

Arshad Jamal ◽

Hassan M. Al-Ahmadi

Keyword(s):

Land Use ◽

Saudi Arabia ◽

Traffic Safety ◽

Geographically Weighted Regression ◽

Significant Proportion ◽

Urban Land Use ◽

Weighted Regression ◽

Spatial Relationships ◽

Population Densities ◽

Crash Types

Examining the relationships between vehicle crash patterns and urban land use is fundamental to improving crash predictions, creating guidance, and comprehensive policy recommendations to avoid crash occurrences and mitigate their severities. In the existing literature, statistical models are frequently used to quantify the association between crash outcomes and available explanatory variables. However, they are unable to capture the latent spatial heterogeneity accurately. Further, the vast majority of previous studies have focused on detailed spatial analysis of crashes from an aggregated viewpoint without considering the attributes of the built environment and land use. This study first uses geographic information systems (GIS) to examine crash hotspots based on two severity groups, seven prevailing crash causes, and three predominant crash types in the City of Dammam, Kingdom of Saudi Arabia (KSA). GIS-based geographically weighted regression (GWR) analysis technique was then utilized to uncover the spatial relationships of traffic collisions with population densities and relate it to the land use of each neighborhood. Results showed that Fatal and Injury (FI) crashes were mostly located in residential neighborhoods and near public facilities having low to medium population densities on highways with relatively higher speed limits. Distribution of hotspots and GWR-based analysis for crash causes showed that crashes due to “sudden lane deviation” accounted for the highest proportion of crashes that were concentrated mainly in the Central Business District (CBD) of the study area. Similarly, hotspots and GWR analysis for crash types revealed that “collisions between motor vehicles” constitute a significant proportion of the total crashes, with epicenters mostly stationed in high-density residential neighborhoods. The outcomes of this study could provide analysts and practitioners with crucial insights to understand the complex inter-relationships between traffic safety and land use. It can provide useful guidance to policymakers for better planning and effective management strategies to enhance safety at zonal levels.

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Analysis of Beijing’s Working Population Based on Geographically Weighted Regression Model

Sustainability ◽

10.3390/su12125018 ◽

2020 ◽

Vol 12 (12) ◽

pp. 5018

Author(s):

Yanyan Chen ◽

Hanqiang Qian ◽

Yang Wang

Keyword(s):

Land Use ◽

Population Density ◽

Regression Model ◽

Mobile Phone ◽

Geographically Weighted Regression ◽

Mobile Phone Data ◽

Weighted Regression ◽

Working Population ◽

Development Status ◽

Geographically Weighted Regression Model

Evaluation of urban planning and development is becoming more and more important due to the large-scale urbanization of the world. With the application of mobile phone data, people can analyze the development status of cities from more perspectives. By using the mobile phone data of Beijing, the working population density in different regions was identified. Taking the working population density in Beijing as the research object and combining the land use of the city, the development status of Beijing was evaluated. A geographically weighted regression model (GWR) was used to analyze the difference in the impact of land use on the working population between different regions. By establishing a correlation model between the working population and land use, not only can the city’s development status be evaluated, but it can also help city managers and planners to make decisions to promote better development of Beijing.

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A geographically weighted regression model to examine the spatial variation of the socioeconomic and land-use factors associated with Strava bike activity in Austin, Texas

Journal of Transport Geography ◽

10.1016/j.jtrangeo.2020.102865 ◽

2020 ◽

Vol 88 ◽

pp. 102865

Author(s):

Sirajum Munira ◽

Ipek N. Sener

Keyword(s):

Land Use ◽

Regression Model ◽

Spatial Variation ◽

Geographically Weighted Regression ◽

Weighted Regression ◽

Factors Associated ◽

Geographically Weighted Regression Model ◽

Use Factors

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Understanding City-Wide Ride-Sourcing Travel Flow: A Geographically Weighted Regression Approach

Journal of Advanced Transportation ◽

10.1155/2021/9929622 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Chuqiao Chen ◽

Simon Hu ◽

Washington Y. Ochieng ◽

Na Xie ◽

Xiqun (Michael) Chen

Keyword(s):

Land Use ◽

Geographically Weighted Regression ◽

Transportation Planning ◽

Goodness Of Fit ◽

Urban Land ◽

Residential Area ◽

Urban Land Use ◽

Weighted Regression ◽

Bus Station ◽

The Impact

The emerging ride-sourcing service has become an important element of urban mobility. A challenging question underlying the provision of such service is how and to what extent the built environment affects origin-destination (OD) travel flows. This paper employs the geographically weighted regression (GWR) model to analyze the OD-based ride-sourcing travel flow. It makes a comparison with the existing ordinary least square (OLS) model and spatial autocorrelation model (SAM). We have collected ride-sourcing order data in Hangzhou, China, to provide an accurate source for acquiring ride-sourcing travel flow. We investigate the effects of the residential area, points of interest (POIs), and transit stations on ride-sourcing travel flow among traffic analysis zones (TAZs). The results show the following: (a) GWR has better goodness-of-fit than SAM and OLS. (b) Residential area, enterprise, and bus stations have positive correlations with ride-sourcing OD flows, but education and subway stations have negative correlations. We have further investigated the issue and found that it is not a causal relationship between the bus station and OD flow, due to collinearity between the two variables. The bus station builds on locations with high demand, but its capacity is not large enough to reduce the ride-sourcing flow to a low level, which results in a positive coefficient. (c) Based on the estimated coefficients, the prediction of ride-sourcing flows is feasible, supporting the impact analysis for urban land use and transportation planning. This paper contributes to understanding OD-based ride-sourcing travel flow distributions and provides a framework of long-term OD flow prediction for urban land use and transportation planning.

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IMPACTS OF URBAN LAND USE ON CRIME PATTERNS THROUGH GIS APPLICATION

PLANNING MALAYSIA JOURNAL ◽

10.21837/pmjournal.v11.i2.113 ◽

2013 ◽

Vol 11 (2) ◽

Cited By ~ 1

Author(s):

Ahmad Nazri Muhamad Ludin ◽

Norsiah Abd. Aziz ◽

Nooraini Hj Yusoff ◽

Wan Juliyana Wan Abd Razak

Keyword(s):

Land Use ◽

Drug Abuse ◽

Land Use Planning ◽

Urban Areas ◽

Spatial Relationship ◽

Police Department ◽

Strong Relationship ◽

Urban Land ◽

Urban Land Use ◽

Land Use Patterns

Land use planning plays a crucial role in creating a balance between the needs of society, physical development and the ecosystem. However, most often poor planning and displacement of land uses particularly in urban areas contribute to social ills such as drug abuse and criminal activities. This research explains the spatial relationship of drug abuse and other criminal activities on urban land use planning and their implications on the society at large. Spatial statistics was used to show patterns, trends and spatial relationships of crimes and land use planning. Data on crime incidents were obtained from the Royal Malaysia Police Department whilst cases of drug abuse were collected from the National Anti-Drug Agency (AADK). Analysis of the data together with digital land use maps produced by Arnpang Jaya Municipal Council, showed the distribution of crime incidents and drug abuse in the area. Findings of the study also indicated that, there was a strong relationship between petty crimes, drng abuse and land use patterns. These criminal activities tend to concentrate in residential and commercial areas of the study area.

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Spatiotemporal Changes in Vegetation Cover and Its Influencing Factors in the Loess Plateau of China Based on the Geographically Weighted Regression Model

Forests ◽

10.3390/f12060673 ◽

2021 ◽

Vol 12 (6) ◽

pp. 673

Author(s):

Chen Yang ◽

Meichen Fu ◽

Dingrao Feng ◽

Yiyu Sun ◽

Guohui Zhai

Keyword(s):

Regression Model ◽

Influencing Factors ◽

Loess Plateau ◽

Geographically Weighted Regression ◽

Vegetation Cover ◽

Weighted Regression ◽

Vegetation Coverage ◽

The Loess Plateau ◽

Geographically Weighted Regression Model ◽

Spatiotemporal Changes

Vegetation plays a key role in ecosystem regulation and influences our capacity for sustainable development. Global vegetation cover has changed dramatically over the past decades in response to both natural and anthropogenic factors; therefore, it is necessary to analyze the spatiotemporal changes in vegetation cover and its influencing factors. Moreover, ecological engineering projects, such as the “Grain for Green” project implemented in 1999, have been introduced to improve the ecological environment by enhancing forest coverage. In our study, we analyzed the changes in vegetation cover across the Loess Plateau of China and the impacts of influencing factors. First, we analyzed the latitudinal and longitudinal changes in vegetation coverage. Second, we displayed the spatiotemporal changes in vegetation cover based on Theil-Sen slope analysis and the Mann-Kendall test. Third, the Hurst exponent was used to predict future changes in vegetation coverage. Fourth, we assessed the relationship between vegetation cover and the influence of individual factors. Finally, ordinary least squares regression and the geographically weighted regression model were used to investigate the influence of various factors on vegetation cover. We found that the Loess Plateau showed large-scale greening from 2000 to 2015, though some regions showed decreasing vegetation cover. Latitudinal and longitudinal changes in vegetation coverage presented a net increase. Moreover, some areas of the Loess Plateau are at risk of degradation in the future, but most areas showed a sustainable increase in vegetation cover. Temperature, precipitation, gross domestic product (GDP), slope, cropland percentage, forest percentage, and built-up land percentage displayed different relationships with vegetation cover. Geographically weighted regression model revealed that GDP, temperature, precipitation, forest percentage, cropland percentage, built-up land percentage, and slope significantly influenced (p < 0.05) vegetation cover in 2000. In comparison, precipitation, forest percentage, cropland percentage, and built-up land percentage significantly affected (p < 0.05) vegetation cover in 2015. Our results enhance our understanding of the ecological and environmental changes in the Loess Plateau.

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