scholarly journals Modeling the Factors Influencing the Activity Spaces of Bikeshare around Metro Stations: A Spatial Regression Model

2018 ◽  
Vol 10 (11) ◽  
pp. 3949 ◽  
Author(s):  
Xinwei Ma ◽  
Yanjie Ji ◽  
Yuchuan Jin ◽  
Jianbiao Wang ◽  
Mingjia He
Keyword(s):  
Spatial Regression ◽  
Central Business District ◽  
Ordinary Least Squares ◽  
Model Fit ◽  
Activity Space ◽  
Spatial Error ◽  
Activity Spaces ◽  
Local Residents ◽  
Business District ◽  
Social Demographic

Metro-bikeshare integration is considered a green and efficient travel model. To better understand bikeshare as a feeder mode to the metro, this study explored the factors that influence the activity spaces of bikeshare around metro stations. First, metro-bikeshare transfer trips were recognized by matching bikeshare smartcard data and metro smartcard data. Then, standard deviation ellipse (SDE) was used for the calculation of the metro-bikeshare activity spaces. Moreover, an ordinary least squares (OLS) regression and a spatial error model (SEM) were established to reveal the effects of social-demographic, travel-related, and built environment factors on the activity spaces of bikeshare around metro stations, and the SEM outperformed OLS significantly in terms of model fit. Results show that the average metro-bikeshare activity space on weekdays is larger than that on weekends. The proportion of local residents promotes the increase in activity space on weekends, while a high density of road and metro impedes the activity space on weekdays. Additionally, with increased job density, the activity space becomes smaller significantly throughout the week. Also, both on weekdays and weekends, the closer to the central business district (CBD), the smaller the activity space. This study can offer meaningful guidance to policymakers and city planners aiming to make the bikeshare distribution more reasonable.

scholarly journals The Effect of Land Use/Cover Change on Soil Erosion Change by Spatial Regression in Changwu County on the Loess Plateau in China

Forests ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1209
Author(s):  
Shichuan Yu ◽  
Fei Wang ◽  
Mei Qu ◽  
Binhou Yu ◽  
Zhong Zhao
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Spatial Regression ◽  
Ordinary Least Squares ◽  
Spatial Error Model ◽  
The Loess Plateau ◽  
Spatial Error ◽  
Spatial Lag Model ◽  
Geographically Weighted Regression Model ◽  
Lag Model

Changwu County is a typical soil and water loss area on the Loess Plateau. Soil erosion is an important ecological process, and the impact of land use/cover change on soil erosion has received much attention. The present study used remote sensing images of the study area in 1987, 1997, 2007, and 2017 to analyze the land use/cover change (LULCC), and the RUSLE model was applied to estimate the soil erosion in different times. We exploited the Sankey diagram to visualize the spatiotemporal changes in land use/cover and soil erosion. We planned to obtain the most suitable model by comparing the application of different spatial regression models (Geographically weighted regression model, Spatial lag model, Spatial error model) and Ordinary least squares in LULCC and soil erosion changes. The results revealed that land use/cover has significantly changed in the last 30 years. From 1987 to 1997, cropland expansion came mainly from planted land and orchards, which transformed 68.99 km2 and 64.93 km2, respectively. In 1997–2007, the planted land increase was mainly through the conversion of cropland. In 2007–2017, the increase in orchard area came mainly from cropland. The forest land increase was mainly from the planted land. Soil erosion in Changwu County was dominated by slight erosion and light erosion, although the area of slight erosion and light erosion continued to decrease. The annual average soil erosion increased, which was estimated at 977.84 ton km−2 year−1, 1305.17 ton km−2 year−1, 1310.60 ton km−2 year−1, and 1891.46 ton km−2 year−1 in 1987, 1997, 2007, and 2017, respectively. These amounts of transformation mainly occurred when slight erosion was converted to light erosion, light erosion was converted to moderate erosion, and moderate erosion was converted to light and severe erosion. The Spatial lag model and Spatial error model have higher accuracy than the Geographically weighted regression model and Ordinary least squares when fitting the effect of LULCC and soil erosion change, where the accuracy exceeded 0.62 in different periods.

scholarly journals GIS-based spatial modeling to identify factors affecting COVID-19 incidence rates in Bangladesh

2020 ◽  
Author(s):  
Md. Hamidu Rahman ◽  
Niaz Mahmud Zafri ◽  
Fajle Rabbi Ashik ◽  
Md Waliullah
Keyword(s):  
Health Workers ◽  
Disease Incidence ◽  
Spatial Regression ◽  
Ordinary Least Squares ◽  
Incidence Rates ◽  
Related Factors ◽  
Spatial Error ◽  
Factors Affecting ◽  
Spatial Lag Model ◽  
Lag Model

The outbreak of the COVID-19 pandemic is an unprecedented shock throughout the world which leads to generate a massive social, human, and economic crisis. However, there is a lack of research on geographic modeling of COVID-19 as well as identification of contributory factors affecting the COVID-19 in the context of developing countries. To fulfill the gap, this study aimed to identify the potential factors affecting the COVID-19 incidence rates at the district-level in Bangladesh using spatial regression model (SRM). Therefore, data related to 32 demographic, economic, weather, built environment, health, and facilities related factors were collected and analyzed to explain the spatial variability of this disease incidence. Three global (Ordinary least squares (OLS), spatial lag model (SLM) and spatial error model (SEM)) and one local (geographically weighted regression (GWR)) SRMs were developed in this study. The results of the models showed that four factors significantly affected the COVID-19 incidence rates in Bangladesh. Those four factors are urban population percentage, monthly consumption, number of health workers, and distance from the capital. Among the four developed models, the GWR model performed the best in explaining the variation of COVID-19 incidence rates across Bangladesh with a R square value of 78.6%. Findings from this research offer a better insight into the COVID-19 situation and would help to develop policies aimed to prevent the future epidemic crisis.

scholarly journals International Real Estate Review

2005 ◽  
Vol 8 (1) ◽  
pp. 1-26 ◽  
Author(s):  
Jin Kim ◽  
◽  
Ming Zhang ◽  
Keyword(s):  
Goodness Of Fit ◽  
Regression Coefficient ◽  
Central Business District ◽  
Ordinary Least Squares ◽  
Economic Benefits ◽  
Land Values ◽  
Rail Transit ◽  
Regression Equations ◽  
Subway Stations ◽  
Business District

Literature regarding transit’s impact on land values reports mixed results concerning the economic benefits of accessibility to subway stations, specifically regarding commercial properties. After examining 731 commercial land values in Seoul, Korea, this study suggests a possible explanation for the mixed results: transit’s discrimination impact on land values by location in a built-up urban area. The regression coefficient for distance to station in the central business district is the highest, the subcenters are next, and other areas are lowest – apparently a strong correlation with higher centrality and development densities of submarkets. Also, the inclusion of spatial lag and error term variables greatly improves the goodness of fit of the regression equations lowering the spatial autocorrelation in the ordinary least squares residuals as well as reduces overestimation of value premiums in association with rail transit stations, which enables a regression model to produce a more accurate and efficient estimator for transit’s impact on commercial land values.

Operationalizing Neighborhood Definitions in Health Research

2018 ◽  
Author(s):  
Dustin T. Duncan ◽  
Seann D. Regan ◽  
Basile Chaix
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Global Positioning System ◽  
Health Research ◽  
Self Report ◽  
Activity Space ◽  
Positioning System ◽  
Activity Spaces ◽  
Global Positioning ◽  
Neighborhood Boundaries

Defining neighborhoods for health research continues to be challenging. This chapter discusses different methods to operationalize neighborhood boundaries, including self-report, administrative definitions, geographic information system buffers and activity spaces, including global positioning system (GPS)–defined activity spaces. It discusses the strengths and limitations of each method of examining neighborhood boundaries (e.g., spatial misclassification, technical difficulties, assumptions). Readers are provided with examples of neighborhood definitions frequently applied in the epidemiology and population health literature. In addition, the chapter provides a rigorous overview of theories for selecting neighborhood definitions, including spatial polygamy theory for GPS-defined activity space neighborhoods.

scholarly journals Analysis of Urban Effects in Oklahoma City using a Dense Surface Observing Network

2016 ◽  
Vol 55 (3) ◽  
pp. 723-741 ◽  
Author(s):  
Xiao-Ming Hu ◽  
Ming Xue ◽  
Petra M. Klein ◽  
Bradley G. Illston ◽  
Sheng Chen
Keyword(s):  
Metropolitan Area ◽  
Land Surface ◽  
Critical Role ◽  
Central Business District ◽  
Temperature Inversion ◽  
Oklahoma City ◽  
Near Surface ◽  
Open Questions ◽  
The Stability ◽  
Business District

AbstractMany studies have investigated urban heat island (UHI) intensity for cities around the world, which is normally quantified as the temperature difference between urban location(s) and rural location(s). A few open questions still remain regarding the UHI, such as the spatial distribution of UHI intensity, temporal (including diurnal and seasonal) variation of UHI intensity, and the UHI formation mechanism. A dense network of atmospheric monitoring sites, known as the Oklahoma City (OKC) Micronet (OKCNET), was deployed in 2008 across the OKC metropolitan area. This study analyzes data from OKCNET in 2009 and 2010 to investigate OKC UHI at a subcity spatial scale for the first time. The UHI intensity exhibited large spatial variations over OKC. During both daytime and nighttime, the strongest UHI intensity is mostly confined around the central business district where land surface roughness is the highest in the OKC metropolitan area. These results do not support the roughness warming theory to explain the air temperature UHI in OKC. The UHI intensity of OKC increased prominently around the early evening transition (EET) and stayed at a fairly constant level throughout the night. The physical processes during the EET play a critical role in determining the nocturnal UHI intensity. The near-surface rural temperature inversion strength was a good indicator for nocturnal UHI intensity. As a consequence of the relatively weak near-surface rural inversion, the strongest nocturnal UHI in OKC was less likely to occur in summer. Other meteorological factors (e.g., wind speed and cloud) can affect the stability/depth of the nighttime boundary layer and can thus modulate nocturnal UHI intensity.

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