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Intertemporal Tourism Clusters and Community Resilience

PROFESSIONAL GEOGRAPHER 2021年第3期73卷 567-572页

作者： Lee, Yong-Jin Alex Kim, Jinwon Jang, Seongsoo Community Spatial Lab Gainesville FL 32608 USA Univ Florida Dept Tourism Hospitality & Event Management Gainesville FL 32611 USA Cardiff Univ Mkt Cardiff CF10 3EU Wales

Promoting community resilience is a major question for researchers and policymakers. Although residents' perceptions of tourism impacts are positively correlated with community resilience, no study to date has quantitatively investigated the relationship between intertemporal tourism clusters and community resilience. Moreover, there is little research assessing the spatial variability of community resilience metrics in relation to tourism clusters. Hence, this study addressed these gaps by empirically examining the spatially varying relationships between intertemporal specialization or instability of tourism clusters and community resilience. spatial and aspatial regression models were employed via a case study of sixty-seven counties in Florida. Findings showed that accommodation and food service specialization led to higher levels of community resilience, whereas arts, entertainment, and recreation instability led to lower levels of community resilience. Furthermore, these effects of tourism clusters on community resilience were spatially heterogeneous. Such findings are essential for policymakers to establish the formulation of community-based resilience planning and policy with tourism clusters.

关键词： community-based resilience planning community resilience Florida intertemporal tourism clusters spatial regression model

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Mapping the dynamics of electric vehicle charging demand within Beijing's spatial structure

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SUSTAINABLE CITIES AND SOCIETY 2022年第0期76卷 103507-103507页

作者： Kang, Jing Kong, Hui Lin, Zhongjie Dang, Anrong Hiroshima Univ Grad Sch Adv Sci & Engn Higashihiroshima Japan Univ Minnesota Humphrey Sch Publ Affairs Minneapolis MN USA Univ Penn Dept City & Reg Planning Philadelphia PA 19104 USA Tsinghua Univ Sch Architecture Beijing Peoples R China

Electric vehicles have been proliferating in large cities across the world, and we increasingly face challenges in estimating charging demand and in planning EV infrastructure. Focusing on Beijing as a case study, this research uses a novel data-driven method to measure the Charging Demand Indicators (CDI) derived from location-based service big data. Analyses through kernel density function reveal dynamic relations between the spatial patterns of CDI and the distribution of Public Charging Stations (PCS). spatial match examination is conducted to discover areas of mismatch between charging demand and infrastructure supply. The results expose a CDI pattern which, although largely complies with the city's centripetal structure, demonstrates variations between weekdays and weekends and by EV travel distances. A spatial regression model confirms the influence of urban structure and distribution of amenities on EV charging behavior and suggests that particular land uses and location features have a significant association with EV charging demand. These findings shed light on the understanding of the spatial disparity between the CDI pattern and the current PCS distribution, which could inform future urban policies and planning of EV infrastructure with an emphasis on its coordination with land use, physical layout, and transit.

关键词： Electric vehicle Public charging station Charging demand indicator Geospatial big data spatial regression model

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spatial regression modeling of soils with high cadmium content in a cocoa producing area of Central Colombia

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GEODERMA REGIONAL 2019年 16卷

作者： Rodriguez Albarrcin, Heidy Soledad Darghan Contreras, Aquiles Enrique Cecilia Henao, Martha Univ Nacl Colombia Fac Ciencias Agr Bogota Colombia

Cadmium (Cd) is a highly toxic heavy metal that can cause harmful effects in soils, plants, micro-organisms and human beings. Plants absorb Cd from available forms in the soil. In cocoa crops of Latin America, higher levels of Cd than those allowed by international regulations on processed foods frequently have been reported. Consequently, close monitoring of such crops is required in order to guarantee the innocuousness of cocoa products. Using data from an existing study done in a cocoa producing area of central Colombia, we selected several farms with high Cd concentration, and in these farms we studied the spatial variability of Cd in soil and plant tissues, along with other soil chemical properties. At increased soil depths there was a reduction in total Cd as well as in available levels of the element. Additionally, we found that Cd contents in cocoa beans can be modeled through a spatial regression model using the explanatory variables measured up to 30 cm and 100 cm of soil depths, as well as a weighted depth that collected information from the two depths, namely Cd in leaves, leaf litter, total and available, as well as the soil pH. In the weighted depth we found a clear relationship between the Cd in the coma almonds and the explanatory variables being considered. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Cocoa beans spatial regression model Leaf litter

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Relationship between Urban Three-Dimensional spatial Structure and Population Distribution: A Case Study of Kunming's Main Urban District, China

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REMOTE SENSING 2022年第15期14卷 3757-3757页

作者： Wang, Yang Yue, Xiaoli Li, Cansong Wang, Min Zhang, Hong'ou Su, Yongxian Yunnan Normal Univ Fac Geog Kunming 650500 Yunnan Peoples R China Guangdong Acad Sci Guangzhou Inst Geog Guangzhou 510070 Peoples R China Guangdong Univ Technol Sch Architecture & Urban Planning Guangzhou 510090 Peoples R China

The three-dimensional (3D) spatial structure within cities can reveal more information about land development than the two-dimensional spatial structure. Studying the relationship between the urban 3D spatial structure and the population distribution is a crucial aspect of the relationship between people and land within cities. However, a few relevant studies focus on the differences between employment population and night population distribution in relation to urban 3D spatial structure. Therefore, this study proposes a new concept of 3D space-filling degree (3DSFD), which is applicable to evaluate the city's 3D spatial structure. We took 439 blocks in Kunming's Main Urban District as a sample and analyzed the 3D spatial structure based on geographic information data at the scale of a single building. The characteristics and differences of the daytime and night population distribution in Kunming's Main Urban District were identified using cell phone signaling big data. Accordingly, a cross-sectional dataset of the relationship between the city's 3D spatial structure and the population distribution was constructed, with the 3D space-filling degree of the block as the dependent variable, two indicators of population distribution (daytime and night population density) as the explanatory variables, and seven indicators of distance from the city center, and building, road, and functional place densities, proportion of undevelopable land area, housing prices, and land use type as the control variables. We used spatial regression models to explore the significance, strength, and direction of the relationship between urban 3D spatial structure and population distribution. We found that the spatial error model (SEM) was the most effective. The results show that only night population distribution is significantly and positively related to 3DSFD. Every 1% increase in night population density in a block will increase the value of 3DSFD by 2.8307%. The night population distribu

关键词： three-dimensional spatial structure three-dimensional land use 3D space filling degree population distribution spatial regression model Kunming

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The Impact of Multi-Dimensional Urbanization on PM2.5 Concentrations in 261 Cities of China

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IEEE ACCESS 2020年 8卷 96199-96209页

作者： Yang, Yi Li, Jie Zhu, Guobin Guan, Xiaobin Zhu, Weiping Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China Wuhan Univ Sch Geodesy & Geomat Wuhan 430079 Peoples R China Wuhan Univ Sch Resource & Environm Sci Wuhan 430079 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430079 Peoples R China

Rapid urbanization has caused serious PM2.5 pollution. The existing researches mainly focus on the contribution of one aspect of urbanization to PM2.5 concentrations. However, few studies have comprehensively considered the direct and indirect effects of multi-dimensional urbanization on PM2.5 concentrations. In order to identify the impact of multi-dimensional urbanization on PM2.5 concentrations, the entropy-right method, the spatial lag model, spatial error model and spatial Durbin model were used to investigate the relationships between multi-dimensional urbanization and PM2.5 concentrations in 261 cities of China in 2016. Results demonstrated that the spillover effect of PM2.5 concentrations between different cities was significant. Economic urbanization had the strongest direct effect on PM2.5 concentrations. Social urbanization and ecological urbanization had a significantly negative impact on PM2.5 concentrations in local cities. However, the biggest indirect effect on PM2.5 concentrations in adjacent cities was social urbanization, followed by land urbanization. The direct and indirect effects of population urbanization on PM2.5 concentrations were non-significant and the least. The findings from this result analysis and discussion can be used as a theoretical basis to provide some policy suggestions for China's air governance and sustainable urban development.

关键词： Urban areas Sociology Statistics Pollution Biological system modeling Economic indicators PM2 5 concentrations multi-dimensional urbanization spillover effects spatial regression model

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Regional spatial patterns and influencing factors of environmental auditing for sustainable development: summaries and illuminations from international experiences

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ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY 2020年第4期22卷 3577-3597页

作者： Lu, Haiyan Wei, Yanqiang Yang, Suchang Liu, Yunwei Lanzhou Univ Sch Econ Lanzhou 730000 Peoples R China Lanzhou Univ Finance & Econ Sch Accountancy Lanzhou 730101 Peoples R China Chinese Acad Sci Key Lab Remote Sensing Gansu Prov Northwest Inst Ecoenvironm & Resources Lanzhou 730000 Peoples R China Xichang Univ Sch Environm & Resource Xichang 615000 Peoples R China

Environmental auditing (EA) is an efficient tool for supervising environmental governance for the realization of economic and social sustainable development. However, the extent to which EA influences the implementation of environmental protection projects and the dominating socioeconomic factors affecting the implementation of EA are unclear. Due to limited data availability, reports investigating this issue are relatively scarce. Based on annual investigation data from the International Organisation of Supreme Audit Institutions regarding the implementation of world EA projects between 2003 and 2013, this paper performed exploratory spatial data analysis to analyze the spatial correlations of EA projects in 204 countries/regions worldwide. In addition, spatial regression models for the year 2013 were established. The findings suggest that (I) high-high regions were mainly concentrated in North America, Europe, and parts of Latin America. The local implementation of EA had strong spatial interactive effects and was heavily influenced by adjacent regions. An experimental area that has already implemented EA can influence its adjacent regions, and the surrounding regions may learn from such implementation. (II) The spatial regression coefficient of the spatial lag model was 0.323 and highly significant (p < 0.001) compared to the traditional regression model. After considering the spatial variables, the importance of CO2 emissions decreased by 1.7%, while environmental input and per capita income decreased by 0.7% and 1%, respectively. The influence of these factors on the implementation of EA was driven by the factor of spatial proximity, which was a critical factor influencing the results. (III) In general, socioeconomic development level, environmental protection investment, and other factors, such as CO2 emission level and degree of social informatization, all increased the efficiency of EA implementation. These conclusions emphasize the important role of EA in h

关键词： spatial effect Exploratory spatial data analysis (ESDA) spatial regression model Environmental auditing Environmental management Sustainable development

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Estimating neighbourhood-level prevalence of adult obesity by socioeconomic, behavioural and built environment factors in New York City

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PUBLIC HEALTH 2020年 186卷 57-62页

作者： Sun, Y. Wang, S. Sun, X. Swansea Univ Coll Sci Dept Geog Swansea SA2 8PP W Glam Wales Sun Yat Sen Univ Sch Geog & Planning Guangdong Key Lab Urbanizat & Geosimulat Guangzhou 510275 Guangdong Peoples R China Univ Illinois CyberGIS Ctr Adv Digital & Spatial Studies Urbana IL 61801 USA Nankai Univ Zhou Enlai Sch Govt Tianjin 300350 Peoples R China Nankai Univ Computat Social Sci Lab Tianjin 300350 Peoples R China

Objectives: Some studies reveal that socio-economic status, behavioural factors, accessibility to supermarket or food store, are associated with the prevalence of obesity and overweight. In this study, we aimed to examine to what extent socio-economic, behavioural and built environment characteristics can contribute to spatial disparities in adult obesity. Study design: The spatial analysis was undertaken to understand the association of spatial disparities in adult obesity and spatial disparities in socio-economic, behavioural and built environment characteristics. Methods: A spatial regression model which can remove the impact of auto-correlation in the residuals of conventionally regression models was applied to modelling local-scale rate of adult obesity (N = 59). Results: Owing to the presence of residual spatial auto-correlation in the non-spatial regression model estimated, a spatial regression model was set up successfully to model local-scale rate of adult obesity across New York City (R-2 = 0.8353, N = 59). Compared with socio-economic and built environment factors, behavioural factors make statistically significant contributions to spatial disparities in the prevalence of adult obesity (POAO). Particularly, two behavioural factors ('sugary drinks consumption' and 'fruits and vegetable consumption') can explain more than 70% of the variance of POAO (adjusted R-2 = 0.7323, N = 59). Surprisingly, physical activity prevalence (percent of physically active adults) makes no statistically significant contributions. Conclusions: The results further suggest that the reduction of adult obesity prevalence could benefit more from decreasing intake of sugary drinks than increasing physical activity. The local government and policy are advised to prioritise decreasing exposure of residents to sugary drinks through restricting advertising or increasing taxes rather than increasing neighbourhoods' walkability through urban planning. (c) 2020 The Royal Society for Public

关键词： Adult obesity spatial regression model Behavioural factors Socio-economic factors Built environment

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Non-Linear Response of PM2.5 Pollution to Land Use Change in China

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REMOTE SENSING 2021年第9期13卷 1612-1612页

作者： Lu, Debin Mao, Wanliu Xiao, Wu Zhang, Liang Zhejiang Univ Dept Land Management Hangzhou 310058 Peoples R China Zhejiang Acad Surveying & Mapping Hangzhou 311100 Peoples R China Zhejiang Shuren Univ Sch Urban Construct Hangzhou 310015 Peoples R China

Land use change has an important influence on the spatial and temporal distribution of PM2.5 concentration. Therefore, based on the particulate matter (PM2.5) data from remote sensing instruments and land use change data in long time series, the Getis-Ord Gi* statistic and SP-SDM are employed to analyze the spatial distribution pattern of PM2.5 and its response to land use change in China. It is found that the average PM2.5 increased from 25.49 mu g/m(3) to 31.23 mu g/m(3) during 2000-2016, showing an annual average growth rate of 0.97%. It is still greater than 35 mu g/m(3) in nearly half of all cities. The spatial distribution pattern of PM2.5 presents the characteristics of concentrated regional convergence. PM2.5 is positively correlated with urban land and farmland, negatively correlated with forest land, grassland, and unused land. Furthermore, the average PM2.5 concentrations show the highest values for urban land and decrease in the order of farmland > unused land > water body > forest > grassland. The impact of land use change on PM2.5 is a non-linear process, and there are obvious differences and spillover effects for different land types. Thus, reasonably controlling the scale of urban land and farmland, optimizing the spatial distribution pattern and development intensity, and expanding forest land and grassland are conducive to curbing PM2.5 pollution. The research conclusions provide a theoretical basis for the management of PM2.5 pollution from the perspective of optimizing land use.

关键词： particulate matter (PM2 5) land use change non-linear spatial regression model

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The Effect of Land Use/Cover Change on Soil Erosion Change by spatial regression in Changwu County on the Loess Plateau in China

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FORESTS 2021年第9期12卷 1209-1209页

作者： Yu, Shichuan Wang, Fei Qu, Mei Yu, Binhou Zhao, Zhong Northwest A&F Univ Coll Forestry Yangling 712100 Shaanxi Peoples R China Northwest A&F Univ State Key Lab Soil Eros & Dryland Farming Loess P Yangling 712100 Shaanxi Peoples R China Natl Forestry & Grassland Adm Northwest Surveying Planning & Designing Inst Xian 710048 Peoples R China Key Lab Natl Forestry Adm Ecol Hydrol & Disaster Xian 710048 Peoples R China Northwest A&F Univ Coll Econ Management Yangling 712100 Shaanxi Peoples R China Changwu Cty Hongxing State Owned Ecol Forest Farm Changwu 713600 Peoples R China

Changwu County is a typical soil and water loss area on the Loess Plateau. Soil erosion is an important ecological process, and the impact of land use/cover change on soil erosion has received much attention. The present study used remote sensing images of the study area in 1987, 1997, 2007, and 2017 to analyze the land use/cover change (LULCC), and the RUSLE model was applied to estimate the soil erosion in different times. We exploited the Sankey diagram to visualize the spatiotemporal changes in land use/cover and soil erosion. We planned to obtain the most suitable model by comparing the application of different spatial regression models (Geographically weighted regression model, spatial lag model, spatial error model) and Ordinary least squares in LULCC and soil erosion changes. The results revealed that land use/cover has significantly changed in the last 30 years. From 1987 to 1997, cropland expansion came mainly from planted land and orchards, which transformed 68.99 km(2) and 64.93 km(2), respectively. In 1997-2007, the planted land increase was mainly through the conversion of cropland. In 2007-2017, the increase in orchard area came mainly from cropland. The forest land increase was mainly from the planted land. Soil erosion in Changwu County was dominated by slight erosion and light erosion, although the area of slight erosion and light erosion continued to decrease. The annual average soil erosion increased, which was estimated at 977.84 ton km(-2) year(-1), 1305.17 ton km(-2) year(-1), 1310.60 ton km(-2) year(-1), and 1891.46 ton km(-2) year(-1) in 1987, 1997, 2007, and 2017, respectively. These amounts of transformation mainly occurred when slight erosion was converted to light erosion, light erosion was converted to moderate erosion, and moderate erosion was converted to light and severe erosion. The spatial lag model and spatial error model have higher accuracy than the Geographically weighted regression model and Ordinary least squares when fitting

关键词： land use/cover change soil erosion spatial regression model model comparison remote sensing Loess Plateau

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The impact of intercity economic complementarity on HSR volume in the context of megalopolization

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JOURNAL OF TRANSPORT GEOGRAPHY 2022年第0期98卷 103222-103222页

作者： Hu, Xinlei Wang, Xiaokun (Cara) Ni, Linglin Shi, Feng Cent South Univ Sch Traff & Transportat Engn 22th Shaoshan South Rd Changsha 410075 Hunan Peoples R China Rensselaer Polytech Inst Dept Civil & Environm Engn 4032 JEC Bldg110 8th St Troy NY 12180 USA Zhejiang Univ Finance & Econ Dongfang Coll Hangzhou 310012 Zhejiang Peoples R China Rensselaer Polytech Inst Dept Civil & Environm Engn 110 8th St Troy NY 12180 USA

Driven by the regional integration, intercity economic complementarity and cooperation are enhanced in the context of megalopolization. Due to the increasing interaction and intensified connection between cities, this study attempts to investigate the impact of megalopolization on intercity human mobility from the perspective of complementary employment composition. The variation of differences in the composition of employment by industry sector between cities is identified through socioeconomic statistics in the recent decade. By using passenger flow data generated from ticket sales records, this study explores how economic complementarity affects high-speed rail (HSR) volume considering spatial dependence in megalopolises. The results show that HSR volume tends to be higher between cities with more distinct employment composition, and it is consistent with the existing studies that population size and economic output are positively correlated to the HSR passenger demand. Finally, the implication of different economy planning strategies is assessed to further highlight the interdependence between regional economic patterns and transportation activities.

关键词： Megalopolization High-speed rail Passenger volume Economic complementarity Employment composition spatial regression model

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