Exploratory Spatial Data Analysis of Traffic Forecasting: A Case Study

Transportation planning has historically relied on statistical models to analyze travel patterns across space and time. Recently, an urgency has developed in the United States to address outdated policies and approaches to infrastructure planning, design, and construction. Policymakers at the federal, state, and local levels are expressing greater interest in promoting and funding sustainable transportation infrastructure systems to reduce the damaging effects of pollutive emissions. Consequently, there is a growing trend of local agencies transitioning away from the traditional level-of-service measures to vehicle miles of travel (VMT) measures. However, planners are finding it difficult to leverage their investments in their regional travel demand network models and datasets in the transition. This paper evaluates the applicability of VMT forecasting and impact assessment using the current travel demand model for Dane County, Wisconsin. The main finding is that exploratory spatial data analysis of the derived data uncovered statistically significant spatial relationships and interactions that planners cannot sufficiently visualize using other methods. Planners can apply these techniques to identify places where focused VMT remediation measures for sustainable networks and environments can be most cost-effective.

Environmental Protection, Industrial Structure and Urbanization: Spatiotemporal Evidence from Beijing–Tianjin–Hebei, China

The Beijing–Tianjin–Hebei urban agglomeration (BTH) is striving to realize the transformation process from a low-efficiency to a high-quality development mode; however, it still has problems regarding reducing energy consumption and ecological environment pressure. Based on panel data from 2013 to 2017, this paper proposes an evaluation index system based on BTH’s “environmental protection–industrial structure–urbanization” system. In the course of applying the coupling degree model (CDM) and the coupling coordination degree model (CCDM) with exploratory spatial data analysis (ESDA) methods, this paper discusses the spatiotemporal process, development level, and spatial agglomeration characteristics of the environmental protection–industrial structure–urbanization system in each city of the BTH area. The findings reveal that the coupling degree of the BTH system is gradually increasing, and that the development level of the BTH subsystem is unbalanced: the coupling coordination level of BTH shows a positive evolution process; however, it is in a stage of low-level collaborative development, and there are obvious differences in the level of BTH coupling coordination in space, revealing the convergence of low–high and high–low types. This paper concludes by putting forward the strategy of optimizing the regional spatial pattern of urban agglomeration and implementing integrated development in order to achieve the desired coupling and coordination effects.

Impact of Rural Roads in India: A Case Study Based on Spatial Data Analysis

The Government of India launched its National Rural Roads Program known as Pradhan Mantri Gram Sadak Yojana (PMGSY) to connect the 167 thousand unconnected villages in the country by all-weather roads to improve connectivity there. It is important to study the impact of such intervention on various socio-economic indicators of rural development there. This study assesses the impact of those roads on the different aspects of rural community. The assessment has been completed based on spatial visualisation of the impact created by various facility parameters in rural development using various questionnaires formed and applied on a few selected blocks. Spatial data was collected and integrated using open-source software (QGIS) and statistical analysis has been performed to understand the percentage change in socio-economic indicators related to education, healthcare, agriculture, marketing and employment opportunities which are essential elements of the integrated rural development in India. The analysis appears helpful in estimating the sensitivity of government policies in the context, and thus understanding the requirement of policy changes and implementation in rural India.

Route Optimization via Environment-Aware Deep Network and Reinforcement Learning

Vehicle mobility optimization in urban areas is a long-standing problem in smart city and spatial data analysis. Given the complex urban scenario and unpredictable social events, our work focuses on developing a mobile sequential recommendation system to maximize the profitability of vehicle service providers (e.g., taxi drivers). In particular, we treat the dynamic route optimization problem as a long-term sequential decision-making task. A reinforcement-learning framework is proposed to tackle this problem, by integrating a self-check mechanism and a deep neural network for customer pick-up point monitoring. To account for unexpected situations (e.g., the COVID-19 outbreak), our method is designed to be capable of handling related environment changes with a self-adaptive parameter determination mechanism. Based on the yellow taxi data in New York City and vicinity before and after the COVID-19 outbreak, we have conducted comprehensive experiments to evaluate the effectiveness of our method. The results show consistently excellent performance, from hourly to weekly measures, to support the superiority of our method over the state-of-the-art methods (i.e., with more than 98% improvement in terms of the profitability for taxi drivers).

Centering Racial and Ethnic Equity in Health Care Throughout Spatial Data Analysis

BackgroundThe ongoing COVID-19 pandemic as well as a host of social movements have put a nation-sized spotlight on structural inequality and racial disparities in health throughout America. As health care systems begin to advance health equity by holding plans and payers accounting for racial and socioeconomic disparities in care, quantitative methods are needed that emphasize the distinct linkages between physical locations and racially disparate outcomes.MethodsWe apply a counterfactual model to compare differences in avoidable and potentially avoidable emergency department (ED) admissions among a panel of 8,924 non-Hispanic White, Black, and Hispanic Medicaid participants between 2016 - 2018. The magnitude of disparity estimates is examined in relation to geographic proximity to health care providers, neighborhood socioeconomic contexts, as well as the type of primary care delivery model individuals received. The adjusted rates were assessed by generalized estimating equations (GEE) and average marginal effects models to contrast differences in probability of events in association with race/ethnicity, proximity to care, and treatment through patient-centered medical homes (PCMH). ResultsAttending a patient-centered medical home was associated with a 3.4 percentage point (p <0.001) decrease in Black-White racial disparity and a 1.8 percentage point (p < 0.10) reduction in the overall Black-White disparity for potentially avoidable ED admissions. PCMH attendance was attributed to a 2.6 percentage point (p < 0.10) reduction in Hispanic-White disparities in potentially avoidable admissions, but this difference was not substantial enough to curb the overall Hispanic-White racial disparity in ED admissions. No statistically significant reductions in Black-White or Hispanic-White disparities in avoidable ED admissions were observed. ConclusionMedical homes may be able to curb, but not necessarily eliminate, racial disparities in ED admissions. Counterfactual models of health disparities are in line with recent transitions toward evaluating patient- and value-centered health care reform changes as they are designed to measure health and racial equity. This strategy, or variations of it, are adaptable to other investigations where emphasis on physical locations is considered essential to understanding racial disparities in health outcomes.

Simulation model for assessing the risks of accidental oil spills based on cluster

The paper proposes a new risk-oriented approach to the implementation of control and supervision activities in the field of regional environmental control. The issues of building a simulation model of oil spill risks assessment, implemented in the form of a digital mapof the region based on a combination of clustering methods and spatial data analysis are considered. The analysis is based on data on accidents at field oil pipelines in the license areas of Khanty-Mansi Autonomous Okrug for the period from 2014 to 2020. The result of the analysis is a digital mappublished on the Internet with authorized access and reflecting 5 levels of risk for the districts of the study area.

Latin American Integration Route and the state of Mato Grosso do Sul: productive characterization, threats, and possibilities of promotion

The Latin American Integration Route (RILA) corresponds to the materialization of an old desire to integrate the peoples of South America. This route will connect important municipalities in Brazil, Paraguay, Argentina, and Chile. In the state of Mato Grosso do Sul (MS), it will connect important municipalities, and bring opportunities and threats. The objective of the study was to analyze the productive structure of the municipalities in Mato Grosso do Sul that will be directly affected by the RILA to better understand this process. For this purpose, we used an Exploratory Spatial Data Analysis (Spatial EDA) and the spatial Locational Quotient (sLQ) of the sectorial jobs of the municipalities of the State. The results point out a spatial inequality in productive sectors, delimiting "sectorial islands". In the industrial sector, the northeastern regions and the surroundings of the capital, Campo Grande, stand out. The northeast region of the State also stands out in the Civil Construction sector and, together with the north-central part of the MS, in the agriculture and livestock sector. In the trade sector, the southern region of the MS stands out, with proximity to Paraguay. In the services sector, there is a relative concentration in the capital and the extreme south of the State. In conclusion, we point out the urgent need for public policies to expand opportunities and mitigate the threats of integration managed by the route.

Spatiotemporal Evolution and Influencing Factors of Carbon Sink Dynamics at County Scale: A Case Study of Shaanxi Province, China

To explore the spatiotemporal evolution of carbon sinks in Shaanxi Province, and their impact mechanisms, this study used panel data from 107 counties (districts) in Shaanxi Province from 2000 to 2017. First, we conducted spatial distribution directional analysis and exploratory spatial data analysis (ESDA). Then, we constructed a geographic spatial weight matrix and used the spatial panel Durbin model to analyze the driving factors of carbon sink changes in Shaanxi Province, from the perspective of spatial effects. The results showed that: (1) The temporal evolution of carbon sinks during the study period showed an overall upward trend, but the carbon sinks of counties (districts) differed greatly, and the center of gravity of carbon sinks, as a whole, showed the characteristics of “south to north” migration. (2) The carbon sinks of Shaanxi Province have a significant positive global spatial autocorrelation in geographic space. The local spatial pattern was characterized by low-value agglomeration (low-low cluster) and high-value agglomeration (high-high cluster), supplemented by high-value bulge (high-low outlier) and low-value collapse (low-high outlier). (3) The result of the spatial measurement model proved that the spatial Durbin model, with dual fixed effects of time and space, should be selected. In the model results, factors such as population, per capita gross domestic product (GDP), local government general budget expenditure, and local government general budget revenue all reflect strong spatial spillover effects. Accordingly, in the process of promoting “carbon neutrality”, the government needs to comprehensively consider the existence of spatial spillover effects between neighboring counties (districts), and strengthen the linkage-management and control roles of counties (districts) in increasing carbon sinks.

Ecological Efficiency Evaluation and Spatiotemporal Characteristics Analysis of the Coupling Coordination of the Logistics Industry and Manufacturing Industry

The coupling coordination of the logistics industry and manufacturing industry conducive to the sustainable development of logistics and manufacturing and the stability of sustainable supply chain. The logistics and manufacturing industries are not only the basic industries that support social development, but also the industries with high carbon emissions. Firstly, this paper classifies the carbon emissions from the logistics industry and manufacturing industry as undesirable outputs, evaluates the ecological efficiency of the logistics industry (LEE) and manufacturing industry (MEE) in the Yangtze River Delta from 2006 to 2019 by using the unexpected slacks-based measure (SBM) model. Secondly, the coupling coordination method is used to analyze the coupling coordination scheduling of industrial ecological efficiency. Thirdly, the paper analyzes the spatial differences of the coupling coordination ecological efficiency between logistics industry and manufacturing industry (MLCC) by using the exploratory spatial data analysis method. Finally, the spatial econometric model is used to analyze the driving factors of the MLCC. The results show: The ecological efficiency of the manufacturing industry has steadily improved. The ecological efficiency of the logistics industry presents the rising trend in fluctuation. The level of the coupling coordination development between the logistics and manufacturing industries is high. The results of the spatial heterogeneity analysis show that the spatial differentiation of high-high agglomeration and low-low agglomeration is obvious. The spatial agglomeration characteristics are relatively stable, and the spatial diffusion effect is strong; In space, the MLCC shows a trend of developing from multiple agglomeration areas to one agglomeration area. The results of driving factor analysis show that foreign direct investment(FDI), government intervention(GI) and human capital(HP) have positive effects on the MLCC, while industrial structure(IS), environmental regulation(ER) and energy intensity(EI) have negative effects on the MLCC.

Determination Of Groundwater Recharge Area In Raimanuk And Surroundings, Timor Island

The Raimanuk area in Timor, East Nusa Tenggara, is located in the Aroki Groundwater Basin. The decreasing quality and potential groundwater availability in the Aroki Groundwater Basin is feared due to its widespread use for household needs and agriculture. The lack of the groundwater recharge area map will pose an obstacle in policymaking regarding the management and preparation of spatial conservation areas in the Raimanuk Region. This study aims to determine the zone and classification of groundwater recharge areas in the Raimanuk area based on spatial data analysis. The groundwater recharge area can be determined using slope, river flow patterns, spring emergence, and groundwater table depth. The classification of the recharge area uses a scoring approach with an overlapping analysis of the parameter assessments, which are hydraulic conductivity, precipitation, soil cover, slope, and depth of unconfined groundwater. The result of the study is the groundwater recharge area map of Raimanuk. The groundwater recharge area is located in the Mandeu Hill area, which is the main recharge area. The groundwater discharge area is located in the Aroki plain area that can be the main recharge area.