scholarly journals Pedestrian Crash Exposure Analysis Using Alternative Geographically Weighted Regression Models

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Seyed Ahmad Almasi ◽  
Hamid Reza Behnood ◽  
Ramin Arvin
Keyword(s):  
Traffic Control ◽  
Geographically Weighted Regression ◽  
Spatial Models ◽  
Spatial Distance ◽  
Weighted Regression ◽  
Crash Data ◽  
Pedestrian Crashes ◽  
Gaussian Regression ◽  
Crash Exposure ◽  
Surrogate Measures

In order to develop a sustainable, safe, and dynamic transportation system, proper attention must be paid to the safety of pedestrians. The purpose of this study is to analyze the surrogate measures related to pedestrian crash exposure in urban roads, including the use of sociodemographic characteristics, land use, and geometric characteristics of the network. This study develops pedestrian exposure models using geographical spatial models including geographically weighted regression (GWR), geographically weighted Poisson regression (GWPR), and geographically weighted Gaussian regression (GWGR). In general, the results of the GWPR model show that the presence of a bus station, population density, type of residential use, average number of lanes, number of traffic control cameras, and sidewalk width are negatively associated with increasing the number of crashes. In this study, in order to identify traffic analysis zones (TAZ) based on the observed and predicted crash data, spatial distance-based methods using GWPR outputs have been used. This study shows the dispersion and density of pedestrian crashes without possessing the volume of pedestrians. Comparison of the performance of GWPR and Poisson models shows a significant spatial heterogeneity in the analysis.

Multiple Correspondence Analysis of Pedestrian Crashes in Rural Illinois

2018 ◽  
Vol 2672 (38) ◽  
pp. 116-127 ◽  
Author(s):  
Raghunandan Baireddy ◽  
Huaguo Zhou ◽  
Mohammad Jalayer
Keyword(s):  
Correspondence Analysis ◽  
Traffic Control ◽  
Rural Areas ◽  
Multiple Correspondence Analysis ◽  
Weather Conditions ◽  
Functional Class ◽  
Crash Data ◽  
Lighting Conditions ◽  
Random Events ◽  
Pedestrian Crashes

During the five-year period between 2010 and 2014, there were 24,178 pedestrian crashes in Illinois. Approximately 4.39% of these pedestrian crashes occurred in rural areas; and approximately 40% of the crashes resulted in a severe injury or a fatality. Thus, pedestrian safety problems exist in rural locales, and the factors contributing to these problems need to be investigated. The goal of this study is to answer the question: “Which variable categories, when acting together, contribute more to the occurrence of pedestrian crashes in rural areas?” Crashes are random events stemming from the convergence of various factors. However, traditional statistical tools can only make pairwise comparisons of dependent and independent variables. Therefore, it is necessary to apply an analytical tool that can identify complex underlying structures in crash data and spot associations among variable categories that contribute to crash occurrence. The multiple correspondence analysis (MCA) method, which is used in this study, can do just that. According to the obtained results, categories of variables such as roadway functional class, the number of lanes, lighting conditions, weather conditions, traffic control devices, driver condition, and pedestrian condition were proved to contribute to pedestrian crashes in rural Illinois.

scholarly journals Estimation of grass yield in large region on geographically weighted regression model

2015 ◽  
Vol XL-7/W3 ◽  
pp. 9-13
Author(s):  
L. Chengfeng ◽  
Y. Xiujuan ◽  
L. Caijuan ◽  
D. Yingkun
Keyword(s):  
Regression Model ◽  
Geographically Weighted Regression ◽  
Production Management ◽  
Model Fitting ◽  
Multiple Linear Regression Model ◽  
Animal Husbandry ◽  
Spatial Distance ◽  
Tibet Plateau ◽  
Weighted Regression ◽  
Qinghai Province

The grass yield embodies its productivity，and also is ground for developing animal husbandry production management. Now the remote sensing technology has been becoming an efficient and feasible mean to estimate the grass yield. In the study, the thought about Geographically Weighted Regression (GWR) was involved in estimating the grass yield. The special characteristics of samples measured on field were considered, and then each sample has a local function covering area around. And the parameters for the function are decided by the weighted function which is associated with the spatial distance between the sample and others around. GWR is a good solution to the model without spatial stationarity, as a consequence a significant model-fitting degree comes out. Based on GWR model an ideal production of grassland can be estimated. In this study, Qinghai province, about 0.72 million square kilometres, was taken as an example. The province is an important one on the Qinghai Tibet Plateau. Here the grassland not only closely relates with the local animal husbandry economy, but also directly affects the regional ecosystem security. Landsat TM data in 2013 and samples on field were used to estimate the production. As input parameters, OSAVI and FVC have high correlation coefficient more than 97% with grass yield. There were 201 samples involved in modelling, and the accuracy is 87.27%, above about 47% than that of multiple linear regression model, a widely used traditional statistic model. Another 220 samples were used to verify the results, and here the accuracy can reach 81.3%. Out results indicated that in 2013 the yield of grass in Qinghai province is 1.018*108 ton. The difference between our data and that from professional sector is less than 10%.

scholarly journals Exploratory Analysis of Pedestrian Road Trauma in Finland

2021 ◽  
Vol 13 (12) ◽  
pp. 6715
Author(s):  
Steve O’Hern ◽  
Roni Utriainen ◽  
Hanne Tiikkaja ◽  
Markus Pöllänen ◽  
Niina Sihvola
Keyword(s):  
Cluster Analysis ◽  
Motor Vehicle ◽  
Seasonal Effects ◽  
Unique Element ◽  
Driver Behaviour ◽  
Crash Data ◽  
Safety Issues ◽  
Key Issues ◽  
Development Goals ◽  
Pedestrian Crashes

In Finland, all fatal on-road and off-road motor vehicle crashes are subject to an in-depth investigation coordinated by the Finnish Crash Data Institute (OTI). This study presents an exploratory and two-step cluster analysis of fatal pedestrian crashes between 2010 and 2019 that were subject to in-depth investigations. In total, 281 investigations occurred across Finland between 2010 and 2019. The highest number of cases were recorded in the Uusimaa region, including Helsinki, representing 26.4% of cases. Females (48.0%) were involved in fewer cases than males; however, older females represented the most commonly injured demographic. A unique element to the patterns of injury in this study is the seasonal effects, with the highest proportion of crashes investigated in winter and autumn. Cluster analysis identified four unique clusters. Clusters were characterised by crashes involving older pedestrians crossing in low-speed environments, crashes in higher speed environments away from pedestrian crossings, crashes on private roads or in parking facilities, and crashes involving intoxicated pedestrians. The most common recommendations from the investigation teams to improve safety were signalisation and infrastructure upgrades of pedestrian crossings, improvements to street lighting, advanced driver assistance (ADAS) technologies, and increased emphasis on driver behaviour and training. The findings highlight road safety issues that need to be addressed to reduce pedestrian trauma in Finland, including provision of safer crossing facilities for elderly pedestrians, improvements to parking and shared facilities, and addressing issues of intoxicated pedestrians. Efforts to remedy these key issues will further Finland’s progression towards meeting Vision Zero targets while creating a safer and sustainable urban environment in line with the United Nations sustainable development goals.

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

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.

scholarly journals Effects of COVID-19 on Urban Population Flow in China

2021 ◽  
Vol 18 (4) ◽  
pp. 1617 ◽  
Author(s):  
Xiaorong Jiang ◽  
Wei Wei ◽  
Shenglan Wang ◽  
Tao Zhang ◽  
Chengpeng Lu
Keyword(s):  
Network Topology ◽  
Traffic Control ◽  
Urban Population ◽  
Deep Understanding ◽  
Clustering Coefficient ◽  
Spatial Distance ◽  
Structure Evolution ◽  
Effective Distance ◽  
Urban Hierarchy ◽  
The City

The COVID-19 epidemic has become a Public Health Emergency of International Concern. Thus, this sudden health incident has brought great risk and pressure to the city with dense population flow. A deep understanding of the migration characteristics and laws of the urban population in China will play a very positive role in the prevention and control of the epidemic situation. Based on Baidu location-based service (LBS) big data, using complex networks method and geographic visualization tools, this paper explores the spatial structure evolution of population flow network (PFN) in 368 cities of China under different traffic control situations. Effective distance models and linear regression models were established to analyze how the population flow across cities affects the spread of the epidemic. Our findings show that: (1) the scope of population flow is closely related to the administrative level of the city and the traffic control policies in various cities which adjust with the epidemic situation; The PFN mainly presents the hierarchical structure dominated by the urban hierarchy and the regional isolation structure adjacent to the geographical location.(2) through the analysis network topology structure of PFN, it is found that only the first stage has a large clustering coefficient and a relatively short average path length, which conforms to the characteristics of small world network. The epidemic situation has a great impact on the network topology in other stages, and the network structure tends to be centralized. (3) The overall migration scale of the whole country decreased by 36.85% compared with the same period of last year’s lunar calendar, and a further reduction of 78.52% in the nationwide traffic control stage after the festival. (4) Finally, based on the comparison of the effective distance and the spatial distance from the Wuhan to other destination cities, it is demonstrated that there is a higher correlation between the effective distance and the epidemic spread both in Hubei province and the whole country.

scholarly journals Spatiotemporal Distribution Characteristics and Driving Forces of PM2.5 in Three Urban Agglomerations of the Yangtze River Economic Belt

2021 ◽  
Vol 18 (5) ◽  
pp. 2222
Author(s):  
Jin-Wei Yan ◽  
Fei Tao ◽  
Shuai-Qian Zhang ◽  
Shuang Lin ◽  
Tong Zhou
Keyword(s):  
Geographically Weighted Regression ◽  
Yangtze River ◽  
National Level ◽  
National Development ◽  
Urban Agglomeration ◽  
Spatiotemporal Distribution ◽  
Weighted Regression ◽  
Series Data ◽  
The Yangtze River ◽  
Urban Agglomerations

As part of one of the five major national development strategies, the Yangtze River Economic Belt (YREB), including the three national-level urban agglomerations (the Cheng-Yu urban agglomeration (CY-UA), the Yangtze River Middle-Reach urban agglomeration (YRMR-UA), and the Yangtze River Delta urban agglomeration (YRD-UA)), plays an important role in China’s urban development and economic construction. However, the rapid economic growth of the past decades has caused frequent regional air pollution incidents, as indicated by high levels of fine particulate matter (PM2.5). Therefore, a driving force factor analysis based on the PM2.5 of the whole area would provide more information. This paper focuses on the three urban agglomerations in the YREB and uses exploratory data analysis and geostatistics methods to describe the spatiotemporal distribution patterns of air quality based on long-term PM2.5 series data from 2015 to 2018. First, the main driving factor of the spatial stratified heterogeneity of PM2.5 was determined through the Geodetector model, and then the influence mechanism of the factors with strong explanatory power was extrapolated using the Multiscale Geographically Weighted Regression (MGWR) models. The results showed that the number of enterprises, social public vehicles, total precipitation, wind speed, and green coverage in the built-up area had the most significant impacts on the distribution of PM2.5. The regression by MGWR was found to be more efficient than that by traditional Geographically Weighted Regression (GWR), further showing that the main factors varied significantly among the three urban agglomerations in affecting the special and temporal features.

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