Spatiotemporal Influence of Urban Environment on Taxi Ridership Using Geographically and Temporally Weighted Regression

Taxicabs play an important role in urban transit systems, and their ridership is significantly influenced by the urban built environment. The intricate relationship between taxi ridership and the urban environment has been explored using either conventional ordinary least squares (OLS) regression or geographically weighted regression (GWR). However, time constitutes a significant dimension, particularly when analyzing spatiotemporal hourly taxi ridership, which is not effectively incorporated into conventional models. In this study, the geographically and temporally weighted regression (GTWR) model was applied to model the spatiotemporal heterogeneity of hourly taxi ridership, and visualize the spatial and temporal coefficient variations. To test the performance of the GTWR model, an empirical study was implemented for Xiamen city in China using a set of weekday taxi pickup point data. Using point-of-interest (POI) data, hourly taxi ridership was analyzed by incorporating it to various spatially urban environment variables based on a 500 × 500 m grid unit. Compared to the OLS and GWR, the GTWR model obtained the best performance, both in terms of model fit and explanatory accuracy. Moreover, the urban environment was revealed to have a significant impact on taxi ridership. Road density was found to decrease the number of taxi trips in particular places, and the density of bus stops competed with taxi ridership over time. The GTWR modelling provides valuable insights for investigating taxi ridership variation as a function of spatiotemporal urban environment variables, thereby facilitating an optimal allocation of taxi resources and transportation planning.

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Modeling spatial determinates of teenage pregnancy in Ethiopia; geographically weighted regression

BMC Women s Health ◽

10.1186/s12905-021-01400-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Seblewongel Tigabu ◽

Alemneh Mekuriaw Liyew ◽

Bisrat Misganaw Geremew

Keyword(s):

Spatial Distribution ◽

Geographically Weighted Regression ◽

Teenage Pregnancy ◽

Wealth Quintile ◽

Ordinary Least Squares ◽

Weighted Regression ◽

Contraceptive Methods ◽

Hotspot Analysis ◽

Increased Risk ◽

Risk Areas

Abstract Background In developing countries, 20,000 under 18 children give birth every day. In Ethiopia, teenage pregnancy is high with Afar and Somalia regions having the largest share. Even though teenage pregnancy has bad maternal and child health consequences, to date there is limited evidence on its spatial distribution and driving factors. Therefore, this study is aimed to assess the spatial distribution and spatial determinates of teenage pregnancy in Ethiopia. Methods A secondary data analysis was conducted using 2016 EDHS data. A total weighted sample of 3381 teenagers was included. The spatial clustering of teenage pregnancy was priorly explored by using hotspot analysis and spatial scanning statistics to indicate geographical risk areas of teenage pregnancy. Besides spatial modeling was conducted by applying Ordinary least squares regression and geographically weighted regression to determine factors explaining the geographic variation of teenage pregnancy. Result Based on the findings of exploratory analysis the high-risk areas of teenage pregnancy were observed in the Somali, Afar, Oromia, and Hareri regions. Women with primary education, being in the household with a poorer wealth quintile using none of the contraceptive methods and using traditional contraceptive methods were significant spatial determinates of the spatial variation of teenage pregnancy in Ethiopia. Conclusion geographic areas where a high proportion of women didn’t use any type of contraceptive methods, use traditional contraceptive methods, and from households with poor wealth quintile had increased risk of teenage pregnancy. Whereas, those areas with a higher proportion of women with secondary education had a decreased risk of teenage pregnancy. The detailed maps of hotspots of teenage pregnancy and its predictors had supreme importance to policymakers for the design and implementation of adolescent targeted programs.

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Global perspective of local meteoric water lines based on daily and monthly data: a consideration of climate types

10.22541/au.161606496.64265677/v1 ◽

2021 ◽

Author(s):

Yang Song ◽

Shengjie Wang ◽

Athanassios Argiriou ◽

Mingjun Zhang ◽

Yudong Shi

Keyword(s):

Least Squares ◽

Meteoric Water ◽

Major Axis ◽

Ordinary Least Squares ◽

Weighted Regression ◽

Precipitation Data ◽

Monthly Data ◽

Water Line ◽

Meteoric Water Line ◽

Event Based

The stable hydrogen and oxygen isotopes as well as their correlation in precipitation have been widely investigated for the understanding of various hydrological processes. Monthly precipitation data were usually recommended in order to establish a linear relationship between the stable hydrogen and oxygen isotope ratios (also known as local meteoric water lines or LMWL for a specific location); however, the LMWL based on daily (or event-based) precipitation data is usually different from that using monthly data. Based on 83 sampling stations across the world from 2000 to 2017, local meteoric water lines were calculated using daily (or event-based) precipitation data (n=9354) and corresponding monthly data (n=1895), respectively; multiple regression methods were used, including ordinary least squares, reduced major axis and major axis regressions as well as their precipitation-weighted counterparts. The global meteoric water line from daily data is δ2H = (7.72 ± 0.02) δ18O + (6.84 ± 0.15) (n=9354, r2=0.96) and from monthly data is δ2H = (7.81 ± 0.04) δ18O+(7.61 ± 0.32) (n=1895, r2=0.96). The stations used in this study were grouped into five climate types, according to the Köppen Climate classification. The precipitation-weighted regression may increase the long-term receptiveness of LMWL using daily-based (or event-based) samples, not only for arid regions, but also for cold regions. When only relatively short-term isotopic records in event-based precipitation samples are available, which is usual in modern hydrological studies, the weighted regression (especially precipitation weighted ordinary least squares regression, PWLSR) is helpful to create a respective local meteoric water line.

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Geographically weighted regression modelling of the spatial association between malaria cases and environmental factors in Cameroon.

10.21203/rs.2.13021/v3 ◽

2020 ◽

Author(s):

Marlvin Anemey Tewara ◽

Liu Yunxia ◽

Weiqiang Ling ◽

Binang Helen Barong ◽

Prisca Ngetemalah Mbah-Fongkimeh ◽

...

Keyword(s):

Environmental Factors ◽

Spatial Variability ◽

Geographically Weighted Regression ◽

Environmental Variables ◽

Vegetation Index ◽

Spatial Association ◽

Ordinary Least Squares ◽

Variable Coefficients ◽

Weighted Regression ◽

Gwr Model

Abstract Background: Studies have illustrated the association of malaria cases with environmental factors in Cameroon but limited in addressing how these factors vary in space for timely public health interventions. Thus, we want to find the spatial variability between malaria hotspot cases and environmental predictors using Geographically weighted regression (GWR) spatial modelling technique.Methods: The global Ordinary least squares (OLS) in the modelling spatial relationships tool in ArcGIS 10.3. was used to select candidate explanatory environmental variables for a properly specified GWR model. The local GWR model used the global OLS candidate variables to examine, predict and explore the spatial variability between environmental factors and malaria hotspot cases generated from Getis-Ord Gi* statistical analysis. Results: The OLS candidate environmental variable coefficients were statistically significant (adjusted R2 = 22.3% and p < 0.01) for a properly specified GWR model. The GWR model identified a strong spatial association between malaria cases and rainfall, vegetation index, population density, and drought episodes in most hotspot areas and a weak correlation with aridity and proximity to water with an overall model performance of 0.243 (adjusted R2= 24.3%).Conclusion: The generated GWR maps suggest that for policymakers to eliminate malaria in Cameroon, there should be the creation of malaria outreach programs and further investigations in areas where the environmental variables showed strong spatial associations with malaria hotspot cases.

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Modelling Housing Rents Using Spatial Autoregressive Geographically Weighted Regression: A Case Study in Cracow, Poland

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9060346 ◽

2020 ◽

Vol 9 (6) ◽

pp. 346

Author(s):

Mateusz Tomal

Keyword(s):

Geographically Weighted Regression ◽

Rental Housing ◽

Ordinary Least Squares ◽

Weighted Regression ◽

Spatial Econometric ◽

Econometric Methods ◽

Depth Analysis ◽

Sar Model ◽

Internet Platform ◽

Spatial Autoregressive

The proportion of tenants will undoubtedly rise in Poland, where at present, the ownership housing model is very dominant. As a result, the rental housing market in Poland is currently under-researched in comparison with owner-occupancy. In order to narrow this research gap, this study attempts to identify the determinants affecting rental prices in Cracow. The latter were obtained from the internet platform otodom.pl using the web scraping technique. To identify rent determinants, ordinary least squares (OLS) regression and spatial econometric methods were used. In particular, traditional spatial autoregressive model (SAR) and spatial autoregressive geographically weighted regression (GWR-SAR) were employed, which made it possible to take into account the spatial heterogeneity of the parameters of determinants and the spatially changing spatial autocorrelation of housing rents. In-depth analysis of rent determinants using the GWR-SAR model exposed the complexity of the rental market in Cracow. Estimates of the above model revealed that many local markets can be identified in Cracow, with different factors shaping housing rents. However, one can identify some determinants that are ubiquitous for almost the entire city. This concerns mainly the variables describing the area of the flat and the age of the building. Moreover, the Monte Carlo test indicated that the spatial autoregressive parameter also changes significantly over space.

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Valuing Environmental Amenities across Space: A Geographically Weighted Regression of Housing Preferences in Greenville County, SC

Land ◽

10.3390/land8100147 ◽

2019 ◽

Vol 8 (10) ◽

pp. 147

Author(s):

Hiebert ◽

Allen

Keyword(s):

Geographically Weighted Regression ◽

City Planning ◽

Spatial Dynamics ◽

Ordinary Least Squares ◽

Hedonic Model ◽

Weighted Regression ◽

Hedonic Price ◽

Environmental Amenities ◽

Forested Areas ◽

The Impact

As global consumption and development rates continue to grow, there will be persistent stress placed on public goods, namely environmental amenities. Urban sprawl and development places pressure on forested areas, as they are often displaced or degraded in the name of economic development. This is problematic because environmental amenities are valued by the public, but traditional market analysis typically obscures the value of these goods and services that are not explicitly traded in a market setting. This research examines the non-market value of environmental amenities in Greenville County, SC, by utilizing a hedonic price model of home sale data in 2011. We overlaid home sale data with 2011 National Land Cover Data to estimate the value of a forest view, proximity to a forest, and proximity to agriculture on the value of homes. We then ran two regression models, an ordinary least squares (OLS) and a geographically weighted regression to compare the impact of space on the hedonic model variables. Results show that citizens in Greenville County are willing to pay for environmental amenities, particularly views of a forest and proximity to forested and agricultural areas. However, the impact and directionality of these variables differ greatly across space. These findings suggest the need for an integration of spatial dynamics into environmental valuation estimates to inform conservation policy and intentional city planning.

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Exploring the Correlates of Perceived Job Dangerousness Among Correctional Staff at a Maximum Security Prison

Criminal Justice Policy Review ◽

10.1177/0887403415623618 ◽

2016 ◽

Vol 29 (3) ◽

pp. 215-239 ◽

Cited By ~ 5

Author(s):

Eric G. Lambert ◽

Kevin I. Minor ◽

Jill Gordon ◽

James B. Wells ◽

Nancy L. Hogan

Keyword(s):

Work Environment ◽

Perceived Risk ◽

Ordinary Least Squares ◽

Correctional Staff ◽

Maximum Security ◽

Personal Variables ◽

Maximum Security Prison ◽

Environment Variables ◽

Survey Results ◽

Perceived Danger

In literature on correctional staff, one poorly understood antecedent of job stress and other negative outcomes is perceived danger from the job. Survey results from 272 staff at a state-run Midwestern maximum security prison were analyzed with Ordinary Least Squares (OLS) Regression to determine the relationships between personal/work environment variables and perceptions of job danger. Analyses revealed the effects of the personal variables were conditional on staff position (custody vs. non-custody). Irrespective of position, two of seven work environment variables studied (less input into decision making and more daily contact with prisoners) were related to greater perceived risk of harm from the job. Also, greater organizational formalization was related to greater perceived risk among custodial staff. Perceived danger from the job is a real issue, and the current results indicate workplace factors play a role.

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Mass Appraisal Modeling of Real Estate in Urban Centers by Geographically and Temporally Weighted Regression: A Case Study of Beijing’s Core Area

Land ◽

10.3390/land9050143 ◽

2020 ◽

Vol 9 (5) ◽

pp. 143 ◽

Cited By ~ 1

Author(s):

Daikun Wang ◽

Victor Jing Li ◽

Huayi Yu

Keyword(s):

Real Estate ◽

Large Data ◽

Ordinary Least Squares ◽

Core Area ◽

Weighted Regression ◽

Data Sets ◽

Residential Areas ◽

Urban Centers ◽

Policy Makers ◽

Evaluation Approach

The traditional linear regression model of mass appraisal is increasingly unable to satisfy the standard of mass appraisal with large data volumes, complex housing characteristics and high accuracy requirements. Therefore, it is essential to utilize the inherent spatial-temporal characteristics of properties to build a more effective and accurate model. In this research, we take Beijing’s core area, a typical urban center, as the study area of modeling for the first time. Thousands of real transaction data sets with a time span of 2014, 2016 and 2018 are conducted at the community level (community annual average price). Three different models, including multiple regression analysis (MRA) with ordinary least squares (OLS), geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR), are adopted for comparative analysis. The result indicates that the GTWR model, with an adjusted R2 of 0.8192, performs better in the mass appraisal modeling of real estate. The comparison of different models provides a useful benchmark for policy makers regarding the mass appraisal process of urban centers. The finding also highlights the spatial characteristics of price-related parameters in high-density residential areas, providing an efficient evaluation approach for planning, land management, taxation, insurance, finance and other related fields.

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Spatial Variations in Fertility of South Korea: A Geographically Weighted Regression Approach

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8060262 ◽

2019 ◽

Vol 8 (6) ◽

pp. 262 ◽

Cited By ~ 2

Author(s):

Myunggu Jung ◽

Woorim Ko ◽

Yeohee Choi ◽

Youngtae Cho

Keyword(s):

South Korea ◽

Geographically Weighted Regression ◽

Population Policy ◽

Local Population ◽

Sociodemographic Factors ◽

Ordinary Least Squares ◽

Spatial Variations ◽

Low Fertility ◽

Weighted Regression

South Korea has witnessed a remarkable decline in birth rates in the last few decades. Although there has been a large volume of literature exploring the determinants of low fertility in South Korea, studies on spatial variations in fertility are scarce. This study compares the Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models to investigate the potential role of the spatially heterogeneous response of the total fertility rate (TFR) to sociodemographic factors. The study finds that the relationships between sociodemographic factors and TFRs in South Korea vary across 252 sub-administrative areas in terms of both magnitude and direction. This study therefore demonstrates the value of using spatial analysis for providing evidence-based local-population policy options in pursuit of a fertility rebound in South Korea.

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Effects of Smoking, Drinking, and Urban Environment on Obesity in Seoul, Korea

Iranian Journal of Public Health ◽

10.18502/ijph.v49i2.3085 ◽

2020 ◽

Author(s):

Haejung CHUN

Keyword(s):

Urban Environment ◽

Statistical Significance ◽

Ordinary Least Squares ◽

Star Model ◽

Contrast Effects ◽

Positive Effects ◽

Sar Model ◽

Spatial Autoregressive ◽

The Individual ◽

Space Effects

Background: The level of obesity is related to spatial characteristics around the individual. The objective of this study was to empirically analyze the effect of smoking, drinking, and urban environment on obesity in community residents. Methods: This study was conducted for empirical analysis using Ordinary Least Squares (OLS) model not considering time or space-effects, Temporal Autoregressive (TAR) model considering time-effect only, Spatial Autoregressive (SAR) model considering space-model only, and STAR model considering both time and space effects. This study covered 25 autonomous districts in Seoul City, South Korea in terms of space and from 2009 to 2014 in terms of time. Results: The STAR model yielded an adjusted R square higher than that from OLS, TAR, or SAR model. Empirical results from the STAR model showed significantly positive (+) effects of the ratio of dependent elders, ratio of smokers, ratio of drinkers, and areas of retail floor space on obesity. In contrast, effects of length of bicycle road and the amount of collected local tax on obesity were negative (-) with statistical significance. Conclusion: Smoking and drinking rate and the length of bicycle road can contribute to personal obesity.

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Spatial Heterogeneity, Broadband, and New Firm Formation

Quality Innovation Prosperity ◽

10.12776/qip.v20i3.791 ◽

2017 ◽

Vol 21 (1) ◽

pp. 165

Author(s):

Jitendra Parajuli ◽

Kingsley Haynes

Keyword(s):

Spatial Heterogeneity ◽

Least Squares ◽

Geographically Weighted Regression ◽

Ordinary Least Squares ◽

Weighted Regression ◽

Least Squares Regression ◽

Firm Formation ◽

Broadband Internet ◽

New Firm Formation ◽

New Firm

Purpose: This paper examines the spatial heterogeneity associated with broadband Internet and new firm formation in a number of U.S. states.Methodology/Approach: Both ordinary least-squares regression and Geographically Weighted Regression are used for the estimation purpose.Findings: The global coefficient estimates of ordinary least-squares regression account for the marginal change in a phenomenon, but such a global measure cannot reveal the locally-varying dynamics. Using Geographically Weighted Regression, it was found that at the aggregate and economic sector levels, the association between single-unit firm births and the provision of broadband Internet varies across counties in Florida and Ohio.Originality/Value of paper: There are numerous studies on broadband Internet in the U.S., but this is the first that explicitly examines broadband provision and new firm formation by taking into account spatial heterogeneity across countries.

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