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Evaluating the Correlation Between Impacting Factors and Land Surface Temperature via spatial regression model and Random Forest

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PROFESSIONAL GEOGRAPHER 2024年第6期76卷 740-755页

作者： Li, Jiongye Natl Univ Singapore Singapore City Singapore

Identifying accurate correlations between land surface temperature (LST) and its contributing factors is crucial for mitigating LST. Current studies, however, lack precise methods to determine key impacting factors for correlation analyses. Additionally, existing dominant analysis methods, which are spatial regression models and random forest (RF), have strengths and weaknesses, with insufficient comparative studies to determine their suitability for LST analysis. This study, informed by comprehensive literature reviews, identifies key factors affecting LST. Using Singapore as the study site, both multiscale geographically weighted regression (MGWR;spatial regression) and RF models were employed to analyze these factors, considering spatial variance and nonlinear correlations. The results demonstrate high accuracy, with R2 values of 89 percent for MGWR and 81 percent for RF. The analyses reveal that, in terms of impact magnitude, normalized difference built-up index (NDBI) and normalized difference vegetation index (NDVI) significantly affect LST. Regarding the direction of the correlations, NDBI is positively correlated with LST in both models, whereas green view index (GVI) consistently shows negative correlations with LST. Other variables show variable correlations depending on location, reflecting context-dependent impacts. Based on these results, to mitigate LST, NDBI should be decreased by converting builtup areas to green spaces where feasible. Increasing GVI is also recommended to increase the proportion of green space from a street view, thereby lowering LST.

关键词： correlation analysis random forest satellite imagery geoprocessing spatial regression model street view imagery segmentation

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The Impact of Green Innovation on CO2 Emissions in China: Evidence from spatial regression model

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INTERNATIONAL ECONOMIC JOURNAL 2024年第3期38卷 446-470页

作者： Liu, Jie Kang, Sung Jin Korea Univ Dept Econ Seoul South Korea

Using data from USPTO, CEADs, the Chinese Statistical Yearbook, and spatial econometric models for the panel of the 30 Chinese provinces for the period 2000 similar to 2019, this study explores and compares the impact of green and non-green innovation on CO2 emissions. The findings reveal a significant spatial correlation in CO2 emissions across provinces, validating the appropriateness of spatial econometric models for this analysis. The results indicate that both green and non-green innovations significantly reduce CO2 emissions, with green innovation having a more pronounced effect despite its lower prevalence. Additionally, patents and their citations notably enhance environmental quality in neighboring provinces through indirect effects. The empirical results highlight that China should focus on: (1) spatial spillover effects of technological, especially green, innovation;(2) removing barriers to green patent invention and citation;(3) transitioning from an energy-driven to a green innovation-driven economy;and (4) enhancing environmental quality by mitigating CO2 emissions.

关键词： Green innovation green patent CO2 emissions spatial regression model

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Regional Factors Influencing Non-Take-Up for Social Support in Korea Using a spatial regression model

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SAGE OPEN 2021年第4期11卷

作者： Park, Gyubeom Yoon, Kichan Lee, Munjae Korea Social Secur Informat Serv Social Secur Informat Inst Seoul South Korea Korea Univ Coll Hlth Sci Dept Hlth Policy & Management Seoul South Korea Ajou Univ Dept Med Humanities & Social Med Sch Med Suwon 16499 South Korea Ajou Univ Ajou Res Inst Innovat Med Med Ctr Suwon South Korea

The purpose of this study is to analyze the influential factors of non-take-up citizens, who do not receive social benefits, to increase their discovery rate. A spatial regression model was used to analyze the variables affecting the discovery rates. As a result of the study, there was a difference in the percentage of welfare blind spots by region. In addition, when the proportion of the elderly population, the number of unemployment benefit recipients, etc. increased, that of welfare blind spots also increased;the lower the population density was, the higher the rate of increase in welfare blind spots became. Accordingly, in order to resolve the welfare blind spots at the local level, it is necessary to reinforce policy support for the elderly population and reduce the unemployment rate. Particularly, the policy will have to be prepared to resolve the welfare blind spots in rural areas with low population density.

关键词： non-take-ups regional variables geographic information system elderly proportion spatial regression model

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Ecosystem health in the Yangtze and Yellow River basins in China: Regional differences and driving forces

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ECOLOGICAL INDICATORS 2024年 166卷

作者： Chen, Wanxu Yuan, Yanghaoyue Liu, Zhiling Zeng, Jie Yang, Liyan Zhong, Yangyi Gu, Tianci Feng, Ziyi China Univ Geosci Sch Geog & Informat Engn Hubei Key Lab Reg Ecol & Environm Change Wuhan 430074 Peoples R China Henan Univ Lab Geospatial Technol Middle & Lower Yellow River Minist Educ Kaifeng 475004 Peoples R China Qinghai Univ Lab Ecol Protect & High Qual Dev Upper Yellow Rive Xining 810016 Peoples R China China Univ Geosci Sch Publ Adm Wuhan 430074 Peoples R China

The Yangtze and Yellow River basins (YYRB) are renowned for possessing the capacity to provide ecological protection. Rapid socioeconomic development poses severe threats to the ecosystem health. Therefore, scientific exploration of the regional differences in the ecosystem health driving factors is important to achieve healthy urbanization, ecological civilization construction, and high-quality regional development in the YYRB. However, existing literatures lacked an in-depth exploration of the differences in the driving factors of ecosystem health in the YYRB. Based on multi-source data, this study measured the spatiotemporal patterns and analyzed the spatiotemporal variation characteristics, and examined regional differences of ecosystem health in the YYRB. The results indicated that during 2000-2020, the overall ecosystem health index (EHI) of the Yangtze River Basin (YTRB) was higher than that of the Yellow River Basin (YLRB). The middle YLRB were considered the main areas for ecosystem health recovery. The EHI of both basins exhibited significant spatial dependence. The spatial imbalance of EHI in YLRB was greater than that in YTRB. The spatial regression models can better explain the spatial relationship between driving factors and EHI, and the geographically weighted regression model demonstrated the spatial heterogeneity of influence factors. This study can provide a scientific decision-making reference for improving the ecosystem protection of the YYRB. It also serves as a basis for formulating macrocontrol strategies and differentiated control measures.

关键词： Ecosystem health Driving forces Regional differences spatial regression model Yangtze and Yellow River basins

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Explaining the role of land use changes on land surface temperature in an arid and semi-arid metropolitan area with multi-scale spatial regression analysis

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2024年第2期196卷 124-124页

作者： Dadashpoor, Hashem Khaleghinia, Ali Shabrang, Amirhosein Tarbiat Modares Univ Fac Arts & Architecture Urban & Reg Planning Dept Tehran Iran

Urban Heat Islands (UHIs), Land Surface Temperature (LST), and Land Use Land Cover (LULC) changes are critical environmental concerns that require continuous monitoring and assessment, especially in cities within arid and semi-arid (ASA) climates. Despite the abundance of research in tropical, Mediterranean, and cold climates, there is a significant knowledge gap for cities in the Middle East with ASA climates. This study aimed to examine the effects of LULC change, population, and wind speed on LST in the Mashhad Metropolis, a city with an ASA climate, over a 30-year period. The research underscores the importance of environmental monitoring and assessment in understanding and mitigating the impacts of urbanization and climate change. Our research combines spatial regression models, multi-scale and fine-scale analyses, seasonal and city outskirts considerations, and long-term change assessments. We used Landsat satellite imagery, a crucial tool for environmental monitoring, to identify LULC changes and their impact on LST at three scales. The relationships were analyzed using Ordinary Least Squares (OLS) and spatial Error model (SEM) regressions, demonstrating the value of these techniques in environmental assessment. Our findings highlight the role of environmental factors in shaping LST. A decrease in vegetation and instability of water bodies significantly increased LST over the study period. Bare lands and rocky terrains had the most substantial effect on LST. At the same time, built-up areas resulted in Urban Cooling Islands (UCIs) due to their lower temperatures compared to surrounding bare lands. The Normalized Difference Vegetation Index (NDVI) and Dry Bare-Soil Index (DBSI) were the most effective indices impacting LST in ASA regions, and the 30x30 m2 micro-scale provides more precise results in regression models, underscoring their importance in environmental monitoring. Our study provided a comprehensive understanding of the relationship between LULC cha

关键词： Land Surface Temperature (LST) Land Use Land Cover (LULC) change Urban Heat Island (UHI) spatial regression model Multi-scale grid analysis Arid and semi-arid (ASA) climate

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spatial regression modeling Approach for Assessing the spatial Variation of Air Pollutants

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ATMOSPHERE 2021年第6期12卷 785-785页

作者： Park, Seunghoon Ko, Dongwon Dankook Univ Sch Urban Planning & Real Estate Studies Yongin 16890 South Korea Dankook Univ Dept Urban Planning Yongin 16890 South Korea

Over the past decades, industrialization has resulted in radical economic development in Korea. The resulting urban sprawl and unsustainable development have led to considerable air pollution. In this study, using spatial regression models, we examine the effects of the physical and socioeconomic characteristics of neighborhoods on particulate matter (PM10, PM2.5), NO2, CO, and SO2 concentrations in the Daegu Metropolitan area. Results reveal the following: (i) the socioeconomic characteristics were not statistically significant regardless of the air pollutant type;(ii) the effects of the built environment characteristics of the neighborhoods were different for each air pollutant. Compared with other pollutants, PM2.5 was affected more by the built environment. Concerning the neighborhoods' main roads, the SO2 concentration was higher, that of PM2.5 was higher in neighborhoods with more bus stops, and those of CO and PM2.5 were possibly higher in the neighborhood of industrial zones. In neighborhoods with parks and green areas, air pollutant concentrations are likely to be lower. When the total used surface of residential buildings was higher, the air pollutant concentrations were lower. Contextually, similar neighborhoods with more single-family houses seemed to have high pollution levels. Overall, this study is expected to guide policymakers and planners in making smart decisions for eco-friendly and healthy cities.

关键词： air pollutants built environment spatial regression model PM10 PM2 5 NO2 CO SO2

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spatial modeling and Monitoring Electricity Consumption using Generalized Likelihood Ratio Control Chart

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INTERNATIONAL JOURNAL OF ENGINEERING 2025年第7期38卷 1433-1446页

作者： Poula, M. Khazaie Farughia, H. Samimib, Y. Univ Kurdistan Fac Engn Dept Ind Engn Sanandaj Iran KN Toosi Univ Technol Dept Ind Engn Tehran Iran

Energy monitoring using statistical process control (SPC) methods makes it more straightforward to identify patterns and trends to decrease energy consumption more effectively. The literature review of energy consumption monitoring with SPC techniques generally focuses on the temporal aspect of variation. However, due to the spatial nature of energy data, enhancing these methods to incorporate temporal and spatial aspects would improve the accuracy of the diagnostic information, underscoring simultaneous detection of the time and location of changes. Thus, the main novelty of this work is the spatial modeling and spatiotemporal monitoring of electricity consumption. For this purpose, the study used actual electricity consumption data from eight western cities of Mazandaran province in the north of Iran for spatial modeling using spatial regression models and a geographically weighted regression (GWR) Amodel. The prediction performance evaluation of spatial models showed GWR as an appropriate model, whose coefficients were monitored through a generalized likelihood ratio (GLR) chart in phase II. The GLR chart detected two changes in consumption, and its performance was confirmed based on the statements from electricity experts relying on meteorological information and floating population data. Furthermore, the performance of the GLR chart was evaluated using out-of-control average run length (ARL1) Aacross three different scenarios. The findings indicate that the GLR chart can effectively detect any sizes of shifts (delta), ranging from 5% to 100% of the model's parameter value. Additionally, with larger values of delta, the ARL1 decreases, resulting in faster detection of changes in the model.

关键词： Statistical Process Control Spatiotemporal Monitoring Household Electricity Consumption Multivariate Control Chart spatial regression model Geographically Weighted regression

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Logistics planning for direct temporary disaster housing assistance under demand uncertainty

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SOCIO-ECONOMIC PLANNING SCIENCES 2024年 96卷

作者： Chen, Sheng-Yin Song, Yongjia Albright, Dustin Pang, Weichiang Clemson Univ Dept Ind Engn Clemson SC 29634 USA Clemson Univ Sch Architecture Clemson SC 29634 USA Clemson Univ Dept Civil Engn Clemson SC 29634 USA

In this paper, we propose and study a framework for disaster housing logistics planning under demand uncertainty. Specifically, we utilize a two-stage chance-constrained stochastic programming model to achieve the balance between logistics operational cost and demand fulfillment especially towards extreme disaster scenarios. To do so, we incorporate two operational modalities, one for the ordinary modality and the other for the emergency modality, and the emergency modality is only allowed to be activated for a certain percentage of scenarios that is specified by the decision maker among all scenarios. The set of scenarios is generated according to a spatial regression model for characterizing the disaster housing demand based on a selected number of independent variables related to both the hazard and socioeconomic factors, which is trained offline from historical data. We conduct a numerical experiment based on Hurricane Ian, and our numerical results show the effectiveness of the proposed approach compared to some standard benchmark approaches. We also highlight the managerial insights for disaster housing logistics planning gained through this numerical experiment.

关键词： Disaster housing recovery Logistics planning Optimization under uncertainty spatial regression model

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modeling Local-Scale Violent Crime Rate: A Comparison of Eigenvector spatial Filtering models and Conventional spatial regression models

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PROFESSIONAL GEOGRAPHER 2021年第2期73卷 312-321页

作者： Sun, Yeran Wang, Shaohua Xie, Jing Hu, Xuke Swansea Univ Coll Sci Dept Geog Swansea SA2 8PP W Glam Wales Univ Illinois CyberGIS Ctr Adv Digital & Spatial Studies Urbana IL 61801 USA Univ Hong Kong Fac Architecture Hong Kong Peoples R China German Aerosp Ctr DLR Data Sci Inst Malzerstr 3 D-07745 Jena Germany

Environmental factors have both direct and indirect impacts on crime behavior decision making. This study aimed to examine to what degree the occurrences of violent crimes can be affected by social and built environment over space. Although a few studies have attempted to model crime rate using spatial regression models, there is a lack of comparison of spatial regression models. Particularly, the eigenvector spatial filtering type of spatial regression models has reportedly been effective in urban and regional studies, but it has not been widely applied to crime data. In this study, we aimed to examine whether the spatial filtering type of spatial regression models outperforms conventional types of spatial regression models in modeling violent crime rates over space. Moreover, we aimed to investigate the impacts of land use mix and street connectivity on the occurrences of violent crimes as the routine activity theory explained. In empirical studies, two types of spatial regression models (i.e., spatial error model and eigenvector spatial filtering model) were selected and estimated successfully to model local-scale violent crime rates across New York City. The eigenvector spatial filtering models outperform the spatial error models as well as the nonspatial models. model estimation results show that occurrences of violent crimes (i.e., assaults and robberies) can be well determined by socioeconomic and built environment factors and thereby environmental factors can affect the occurrences of violent crimes. The contributions of socioeconomic and built environment factors to violent crime can offer insights on urban planning and policymaking toward violent crime prevention. Particularly, this study offers new evidence on the routine activity theory that increasing land use mix and street connectivity can enhance street activity, thereby reducing occurrences of violent crimes. Policymakers and urban planners should continue to enhance street activity through increasi

关键词： eigenvector spatial filtering land use mix spatial regression model street connectivity violent crime

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Key Factors Influencing Vehicle Availability Associated with the User Waiting Time of Demand Responsive Transport (DRT) for People with Disabilities

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KSCE JOURNAL OF CIVIL ENGINEERING 2023年第10期27卷 4485-4493页

作者： Kim, Do-Gyeong Lee, Chaerim Choi, Hosik Univ Seoul Dept Transportat Engn Dept Urban Big Data Convergence Seoul 02504 South Korea Univ Seoul Grad Sch Dept Urban Big Data Convergence Seoul 02504 South Korea Seoul Inst Dept Transportat Syst Res Seoul 06756 South Korea Univ Seoul Dept Urban Big Data Convergence Seoul 02504 South Korea

Demand Responsive Transport (DRT) services for the safe and comfortable travel of people with disabilities have long waiting times and significant variations in time and space. Due to these characteristics, user dissatisfaction with DRT services continues to increase. This study aimed to identify factors that affect vehicle availability, which is closely related to waiting time, and to prepare measures to solve problems in waiting time. The data used for the analysis was composed of spatial units, so Moran's I factor was used to test for spatial dependence. After confirming the presence of spatial dependence, spatial regression models optimized for spatial data analysis were estimated. As a result of predicting the model using five independent variables, four variables, Medical Infrastructure Concentration Index (MICI), Disabled Population Concentration Index (DPCI), Depot Size, and Demand Variability Index (DVI), were found to be statistically significant at a 95% confidence level, except for congestion level. The analytical results indicate that the frequency of vehicle operation is low in areas with a low population of the disabled, little medical infrastructure related to the disabled, and irregular DRT demand, which may affect increasing user waiting time. Therefore, supply-oriented policies to increase the number of available vehicles, such as adding more depots or deploying more vehicles, will contribute significantly to reducing user waiting time in these areas.

关键词： Demand responsive transport (DRT) Vehicle availability spatial dependence Waiting time spatial regression model Location quotient (LQ)

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