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World Scientific Research Journal 2022年第2期8卷 385-393页

作者： Lin Xu

Guangdong has always been a major economic province in China, but with its rapid economic development, there is also the problem of unbalanced regional economic development. This paper introduces spatial statistics to study the regional economic development of Guangdong Province. Firstly, through intuitive analysis and spatial autocorrelation test of Moran's I index of per capita GDP over the years, it is found that there is indeed spatial correlation and spatial aggregation in the regional economic development of Guangdong Province for many years. Then, through the comparative analysis of traditional linear regression model and spatial econometric model, it reveals that spatial factors, industrial structure, the scale of foreign direct investment and the scale of government expenditure are the main factors affecting regional economic development. Finally, according to the analysis results, this paper puts forward some suggestions to promote regional economic development.

关键词： Economic development spatial autocorrelation Moran's I index spatial error model

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Consumer clusters detection with geo-tagged social network data using DBSCAN algorithm: a case study of the Pearl River Delta in China

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GEOJOURNAL 2021年第1期86卷 317-337页

作者： Fan, Tianhui Guo, Naijing Ren, Yujie Grenoble Ecole Management Dept Mkt Grenoble France Nanjing Forestry Univ Coll Landscape Architecture Nanjing Peoples R China Kyushu Univ Grad Sch Human Environm Studies Fukuoka Japan

With the advent of the Big Data era, multi-source geo-tagged data provide a new perspective and data source for urban spatial analysis. In order to accurately identify the location and characteristics of consumer clusters in urban area and explore their formation mechanism, this study collects Sina Weibo check-in data and *** electronic word of mouth (e-WOM) data generated in the catering consumer space in the core region of Pearl River Delta, Guangdong and identifies location and characteristics of clusters with the help of DBSCAN clustering algorithm and CSA indices. In addition, the formation mechanism of these catering space clusters is explained by non-spatial and global spatial regression models. The result revealed that 4 levels of 19 catering space clusters are identified in the study area. The size and heat of consumer clusters are mainly affected by the geometric form, diversity of check-in, population density, distance from the city center and e-WOM corresponding to each cluster. The present study suggests that the new DBSCAN-based clustering method has a high accuracy. Compared with the traditional factors that reflect the objective attributes of cities and non-spatial models, the unstructured information elements contained in e-WOM and spatial error models can better explain the formation mechanism of the consumer clusters.

关键词： Geo-tagged data DBSCAN algorithm Consumer clusters spatial error model

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modeling the impact of agrometeorological variables on soybean yield in the Mato Grosso Do Sul: 2000-2019

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ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY 2021年第4期23卷 5151-5164页

作者： de Oliveira Aparecido, Lucas Eduardo Torsoni, Guilherme Botega da Silva Cabral de Moraes, Jose Reinaldo de Meneses, Kamila Cunha Lorencone, Joao Antonio Lorencone, Pedro Antonio Fed Inst Mato Grosso do Sul IFMS Navirai MS Brazil State Univ Sao Paulo FCAV UNESP Jaboticabal SP Brazil

The study of the soybean yield variability influenced by the climate contributes to the planning of strategies to mitigate its negative effects. Thus, our aim was to calibrate agrometeorological models for soybean yield forecast and identify the weather variables that most influence soybean yield. This study used historical series of climate and soybean yield data from soybean-producing locations in the Mato Grosso do Sul state, Brazil. The historical climate series was 20 years (2000-2019). The soybean production, yield, and planted area data of the localities were in the period from 2009-2018. Multiple linear regression analysis was the statistical tool used for data modeling. The models from the north and central regions forecast of anticipation of 2 months since the final data necessary to apply the model were EXC(JANc)and P-JANc, respectively. The models calibrated for the southern region reported anticipation of one month since the final data necessary to apply the model was EXCFEVc. The calibrated models used to forecast soybean yield as a function of climatic conditions have a high degree of significance (p < 0.05), high accuracy and errors lower. The models for the northern and central regions show a prevision of anticipation of 2 months before soybean harvest, a period that is essential for producers to be able to conduct pre- and post-harvest planning. The climate variable with the greatest negative influence (r = - 0.54) on soybean yield in Mato Grosso do Sul state was water stress in December.

关键词： Crop modeling Climate Yield zoning spatial error model Glycine maxL

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A spatial regression approach to modeling urban land surface temperature

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METHODSX 2023年 10卷 102022页

作者： Ismaila, Abdur-Rahman Belel Muhammed, Ibrahim Adamu, Bashir Modibbo Adama Univ Fac Environm Sci Dept Urban & Reg Planning YolaPMB 2076 Yola Adamawa Nigeria Modibbo Adama Univ Fac Environm Sci Dept Surveying & Geoinformat YolaPMB 2076 Yola Adamawa Nigeria Modibbo Adama Univ Fac Environm Sci Dept Geog YolaPMB 2076 Yola Adamawa Nigeria

Land surface temperature (LST) is the instantaneous radiative skin temperature of land obtained from satellite sensors. Measured by visible, infrared or microwave sensors, the LST is useful in determining thermal comfort for urban planning. It also serves as a precursor to many underly-ing impacts such as health, climate change and the likelihood of rainfall. Due to the paucity of observed data because of cloud cover or rain-bearing clouds in the case of microwave sensors, it is necessary to model LST for the purpose of forecasting. Two spatial regression models were employed: the spatial lag model and the spatial error model. Using Landsat 8 and Shuttle Radar Topography Mission (SRTM), these models can be studied and compared in terms of their ro-bustness in reproducing LST. Whereas LST is to be the independent variable, built-up area, water surface, albedo, elevation, and vegetation are to be considered as dependent variables and their relative contributions to LST *** dot modeling LST based on spatial regression models with calculated LST as independent *** dot Dependent variables to be considered are normalised difference Built-up index (NDBI), nor-malised difference vegetation index (NDVI), modified normalised difference water index (MNDWI), albedo and *** dot The models were validated using k-fold cross validation method, mean square error and stan-dard deviation.

关键词： spatial lag model spatial error model Surface temperature Urban area

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Estimating the UK Index Flood: an Improved spatial Flooding Analysis

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ENVIRONMENTAL modelING & ASSESSMENT 2020年第5期25卷 731-748页

作者： Muhammad, Marinah Lu, Zudi Univ Southampton Southampton Stat Sci Res Inst Southampton SO17 1BJ Hants England Univ Southampton Sch Math Sci Southampton SO17 1BJ Hants England Univ Malaysia Kelantan Fac Earth Sci Jeli Campus Jeli 17600 Kelantan Malaysia

Flooding is one of the major natural hazards in the UK. Accurate flood estimation at ungauged catchment is an important component to understand and mitigate flood hazards, but still a difficult issue. This study therefore attempts to explore and improve an index flood estimation model, known as the FEH-QMED model, popular in the UK. It was developed under the assumption that the index flood of QMED, i.e., the median of the set of annual maximum (AMAX) flood data, standing for a flooding level of 2-year return period, can be explained by catchment descriptors. In this study, two fundamentals are empirically explored, including assessing reliability of the nonlinear functional impacts of the catchment descriptors on the logarithmic transformation of QMED, specified by the FEH-QMED model, and the potential to improve the model for more accurate index flood estimation, based on the flooding data of 586 gauged stations across the UK. Through a spatial additive regression analysis, we empirically find that the nonlinear impacts of the catchment descriptors in an updated FEH-QMED model appear reliable. However, spatial correlation tests including Moran'sIand Lagrange multiplier tests show that strong spatial dependence exists in the residuals of, but was not fully taken into account by, the QMED type models. We have therefore empirically established new spatial index flood estimation models by proposing spatial autoregressive models to model the impacts of the neighboring sites. Cross-validation assessments demonstrate that the suggested spatial error-based index flood model outperforms the updated FEH-QMED model with a significant improvement, which is robust in the sense of different error measures, say by a reduction of 13.8% of the mean squared error of prediction, for the UK index flood estimation.

关键词： Index flood estimation Flood catchment descriptors Flood Estimation Handbook (FEH)-QMED model Nonlinear effect of covariates spatial neighboring effect spatial error model

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Spatiotemporal variation of forest land and its driving factors in the agropastoral ecotone of northern China

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Journal of Arid Land 2022年第1期14卷 1-13页

作者： WANG Shiqing TAO Zefu SUN Piling CHEN Sijia SUN Huiying LI Nan School of Geography and Tourism Qufu Normal UniversityRizhao 276826China Rizhao Key Laboratory of Territory Spatial Planning and Ecological Construction Rizhao 276962China College of Land Science and Technology China Agriculture UniversityBeijing 100193China

As an important natural resource,forest land plays a key role in the maintenance of ecological ***,variations of forest land in the agropastoral ecotone of northern China(AENC)have attracted little *** the AENC as an example and based on remote-sensing images from 2000,2010 to 2020,we explored the spatiotemporal variation of forest land and its driving factors using the land-use transfer matrix,spatial autocorrelation analysis and spatial error *** results showed that from 2000 to 2020,the total area of forest land in the AENC increased from 75,547.52 to 77,359.96 km^(2) and the changes were dominated by the transformations among forest land,grassland and cropland,which occurred mainly in areas with the elevation of 500-2000 m and slope of 15°-25°.There was obvious spatial agglomeration of forest land in the AENC from 2000 to 2020,with hot spots of forest land gathered in the southern marginal areas of the Yanshan Mountains and the low mountainous and hilly areas of the Loess *** sub-hot spots around hot spots moved southward,the sub-cold spots spread to the surrounding areas and the cold spots *** spatiotemporal variation of forest land resulted from the interactions of natural environment,socioeconomic and policy factors from 2000 to *** variables of average annual precipitation,slope,terrain relief,ecological conversion program and afforestation policy for barren mountains affected the spatial pattern of forest land positively,while those of annual average temperature,slope and road network density influenced it negatively.

关键词： forest land spatiotemporal variation driving factors spatial error model agropastoral ecotone northern China

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Assessing the effectiveness of built environment-based safety measures in urban and rural areas for reducing the non-motorist crashes

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HELIYON 2023年第3期9卷 e14076页

作者： Tokey, Ahmad Ilderim Shioma, Shefa Arabia Uddin, Muhammad Salaha Ohio State Univ Dept Geog 281 West Lane Ave Columbus OH 43210 USA Calif Dept Transportat CALTRANS Sacramento CA 94273 USA Univ Texas San Antonio IDSER San Antonio TX 78249 USA

Introduction: Built environment (BE) has a well-documented impact on non-motorist crashes. Interestingly, the urban-rural distinction of the impacts received scant attention in the literature. Moreover, the combined effect of these elements are less studied than their standalone ***: This study explores the combined effectiveness of built environment-based safety measures in urban and rural settings. Data and method: The study uses nine years (2011-2019) of non-motorist (pedestrian and bicyclist) crash data in Florida. It classifies urban and rural areas with the multivariate clustering method and models the crash count with Log-transformed spatial error ***: Findings suggest that urban areas, tracts with low median income, a lower percentage of senior citizens, and a higher percentage of black, white, and Hispanic people are significantly associated with a high number of nonmotorist crashes. The percentage of pedestrian and bicyclist commuters is positively associated with pedestrian and bicycle crash count, respectively. Among BE variables, more crashes are observed in tracts with more commercial land use (LU), less recreational LU, higher LU mix, more traffic, signalized intersection, transit stops, and sidewalks. Having more traffic and fewer transit stops pose lesser risk in urban areas than rural areas. The combined effects suggest that increasing commercial LU where LU entropy is high (or vice-versa) will help to reduce nonmotorist crashes. Also, in high entropy areas, increasing rural traffic is riskier whereas increasing urban traffic is ***: This paper documents the need of considering urban-rural differences and interaction effects among BE elements for nonmotorist safety.

关键词： Pedestrian crash Bicycle crash Built environment Safety spatial error model

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Improvements and spatial Dependencies in Energy Transition Measures

Improvements and Spatial Dependencies in Energy Transition M...

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作者： Kuc-Czarnecka, Marta Ewa Brno University of Technology

This article aims to improve one of the newest energy transition measures-the World Economic Forum WEF Energy Transition Index (ETI) and find its driving forces. This paper proposes a new approach to correct the ETI structure, i.e., sensitivity analysis, which allows assessing the accuracy of variable weights. Moreover, the novelty of the paper is the use the spatial error models to estimate determinants of the energy transition on different continents. The results show that ETI is unbalanced and includes many variables of marginal importance for the shape of the final ranking. The variables with the highest weights in ETI did not turn out to be its most important determinants, which means that they differentiate the analysed countries well; nonetheless, they do not have sufficient properties of approximating the values of the ETI components. The most important components of ETI (with the highest information load) belong to the CO2 emissions per capita, the innovative business environment, household electricity prices, or renewable capacity buildout. Moreover, we identified the clustering of both ETI and its two main pillars in Europe, which is not observed in America and Asia. The identified positive spatial effects showing that European countries need much deeper cooperation to reach a successful energy transition.

关键词： energy transition index energy transition composite indicators sensitivity analysis spatial error model

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Exploration of spatiotemporal heterogeneity and socio-demographic determinants on COVID-19 incidence rates in Sarawak, Malaysia

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BMC PUBLIC HEALTH 2023年第1期23卷 1396-1396页

作者： Phang, Piau Labadin, Jane Suhaila, Jamaludin Aslam, Saira Hazmi, Helmy Univ Malaysia Sarawak Fac Comp Sci & Informat Technol Kota Samarahan 94300 Sarawak Malaysia Univ Teknol Malaysia Fac Sci Dept Math Sci Skudai 81310 Johor Malaysia Univ Malaysia Sarawak Fac Med & Hlth Sci Kota Samarahan 94300 Sarawak Malaysia

BackgroundIn Sarawak, 252 300 coronavirus disease 2019 (COVID-19) cases have been recorded with 1 619 fatalities in 2021, compared to only 1 117 cases in 2020. Since Sarawak is geographically separated from Peninsular Malaysia and half of its population resides in rural districts where medical resources are limited, the analysis of spatiotemporal heterogeneity of disease incidence rates and their relationship with socio-demographic factors are crucial in understanding the spread of the disease in *** spatial dependence of district-wise incidence rates is investigated using spatial autocorrelation analysis with two orders of contiguity weights for various pandemic waves. Nine determinants are chosen from 14 covariates of socio-demographic factors via elastic net regression and recursive partitioning. The relationships between incidence rates and socio-demographic factors are examined using ordinary least squares, spatial lag and spatial error models, and geographically weighted *** the first 8 months of 2021, COVID-19 severely affected Sarawak's central region, which was followed by the southern region in the next 2 months. In the third wave, based on second-order spatial weights, the incidence rate in a district is most strongly influenced by its neighboring districts' rate, although the variance of incidence rates is best explained by local regression coefficient estimates of socio-demographic factors in the first wave. It is discovered that the percentage of households with garbage collection facilities, population density and the proportion of male in the population are positively associated with the increase in COVID-19 incidence *** research provides useful insights for the State Government and public health authorities to critically incorporate socio-demographic characteristics of local communities into evidence-based decision-making for altering disease monitoring and response plans. Policymakers can make well-

关键词： COVID-19 Spatiotemporal heterogeneity Socio-demography spatial lag spatial error model Geographically weighted regression

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Impacts of spatial Configuration of Land Surface Features on Land Surface Temperature across Urban Agglomerations, China

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REMOTE SENSING 2021年第19期13卷 4008-4008页

作者： Zhang, Qiang Wu, Zixuan Singh, Vijay P. Liu, Chunling Beijing Normal Univ Key Lab Environm Change & Nat Disaster Minist Educ Beijing 100875 Peoples R China Beijing Normal Univ Acad Disaster Reduct & Emergency Management Fac Geog Sci Beijing 100875 Peoples R China Beijing Normal Univ State Key Lab Earth Surface Proc & Resources Ecol Beijing 100875 Peoples R China Minist Nat Resources Inst Surveying & Mapping Standardizat Xian 710054 Peoples R China Texas A&M Univ Dept Biol & Agr Engn College Stn TX 77469 USA

Booming urbanization triggers a significant modification of surface landscape configuration and hence complex urban climates. Considerable concerns exist regarding impacts of impervious surface area (ISA) and/or urban green space (UGS) on land surface temperature (LST). However, a knowledge gap still exists concerning the influence of urban landscape components and related spatial configuration on LST. To date, case studies have usually focused on individual cities, while few reports have addressed the impacts of urban surface components and relevant spatial configurations on LST within cities of different sizes, at different latitudes, and with different climatic backgrounds. Considering case studies from different latitudes and various climatic backgrounds can assist in obtaining comprehensive viewpoints about impacts of urban surface features on LST in both space and time. In this paper we analyzed data from three urban agglomerations, Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD) and the Pearl River Delta (PRD), over the period 2000-2015. These three regions are densely populated with the most developed socio-economy across China, and are also dominated by booming urbanization. Based on Landsat remotely sensed data, we included the spatial pattern of surface components and related configuration into our analysis, quantifying impacts of spatial configuration of surface components on LST in both space and time. We found generally rising LST over all cities, which can be attributed to continuous urban expansion-induced decreased UGS. Generally, LST over ISA was 0.96-7.96 degrees C higher than that over UGS. We investigated the impacts of spatial pattern of land surface components on LST and found that the joint effect of the composition and spatial configuration of land surface components had the most significant impact on LST. Specifically, ISA and UGS had higher impact on LST than the impact of geometry of the ISA and UGS on LST. In the future, conti

关键词： land surface components land surface temperature urban agglomerations spatial error model scenarios

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