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Cropland use sustainability in Cheng-Yu Urban Agglomeration, China: Evaluation framework, driving factors and development paths

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JOURNAL OF CLEANER PRODUCTION 2020年 256卷 120692-120692页

作者： Cheng, Chao Liu, Yaolin Liu, Yanfang Yang, Renfei Hong, Yongsheng Lu, Yanchi Pan, Jiawei Chen, Yiyun Wuhan Univ Sch Resource & Environm Sci 129 Luoyu Rd Wuhan 430079 Hubei Peoples R China Wuhan Univ Key Lab Geog Informat Syst Minist Educ 129 Luoyu Rd Wuhan 430079 Hubei Peoples R China

Cropland and its production toward sustainable pattern play an indispensable role in supporting a virtuous circle of economy-society-ecology and achieving the Sustainable Development Goals. Taking Cheng-Yu Urban Agglomeration, China as a study area, this study developed an evaluation framework to conceptualize cropland use into the economy-society-ecology context with an improved Ecological Footprint analysis and triangle model. Stepwise linear regression model and GeoDetector were then introduced to identify the spatiotemporal driving factors and mechanism of cropland use sustainability. Results reveal the following. (1) From 2003 to 2017, cropland use sustainability temporally presents a downward trend of inverse S-shape fluctuation with sustainability status transitioning from weak unsustainability to strong unsustainability at the turning point in 2008. Cropland use sustainability spatially presents the spatial correlation with a significant agglomeration effect. The spatial distribution of sustainability status in the east is higher than that in the west. (2) Factors of cropland use sustainability, including cropland areas, sown area of farm crops, pesticides consumption, urbanization rate, city impacteforce and runoff depth interactionally form the multi-dimensional driving mechanism. (3) Sixteen cities across the province boundaries can be grouped into three zones, namely, intensive utilization zone, protection priority zone and ecological restoration zone with the targeted and differentiated development paths accordingly. These findings can contribute to simply understanding and visualizing the status and trends of cropland use sustainability and decision-making for national-level urban agglomeration construction. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： Ecological footprint Cropland's input-output process Triangle model exploratory spatial data analysis GeoDetector National-level urban agglomeration

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An Empirical Study on the Ecological Economy of the Huai River in China

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WATER 2020年第8期12卷 2162-2162页

作者： Zhang, Chunmei Wang, Chengxiang Mao, Guangxiong Wang, Min Hsu, Wei-Ling Huaiyin Normal Univ Sch Urban & Environm Sci Huaian 223300 Peoples R China Hanyang Univ Grad Sch Urban Studies Urban Design Anal Lab Seoul 04763 South Korea

The Huai River is an important flood control and discharge river in the middle and east of China, and the development of ecological economy with regional advantages is significant for the protection and improvement of the resources and environment of the basin. On the basis of defining the connotation of an ecological economic system, this study constructed an index system, and it applied the methods of data envelopment analysis (DEA) and exploratory spatial data analysis (ESDA) to study the ecological economy of the Huai River. This study concluded that (1) the efficiency in most areas was efficient, but inefficient in a few areas;(2) the causes of inefficiency were unreasonable production scale and unqualified production technology, which led to redundant input of resources, insufficient output of days with good air quality, and excessive output of particulate matter with less than or equal to 2.5 microns in diameter (PM2.5);and (3) the efficiency was different in different regions, so it was necessary to respectively formulate and implement strategies for protection and development of resources and environment. The research results can be used as an important reference for formulating ecological economic policies around the world.

关键词： the ecological economic mode the ecological economic system ecological economic efficiency evaluation data envelopment analysis exploratory spatial data analysis

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Assessment of an exploratory spatio-temporal data analysis method: Using a dynamic multi-variate geo-visualization of spatio-temporal electric vehicle charge data in an information rich online spatial environment to explore the public charge behaviour in Amsterdam

Assessment of an exploratory spatio-temporal data analysis m...

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作者： Daniel Scheerooren Wageningen University And Research

学位级别：硕士

The thesis research describes the development and assessment of a new method for exploratory Spatio- temporal data analysis (ESDA). The question of "why there?" has occupied the minds of the map users ever since maps were used as a research tool. Exploration of spatial and spatio-temporal data using maps can be a first step in constructing new knowledge and generating hypotheses for further research. New – mostly digital- technologies have expanded the map possibilities for exploration. Over the last decade several researchers developed different software tools to explore spatio-temporal data. These tools allow researchers to use their visual-cognitive capacities to visually explore spatio-temporal data and discover patterns and relationships between variables. Current ESDA's have several limitations: they often use aggregated data, static time frames, visualize only one or two attributes of the data and they anonymize space. To make better use of human visual-cognitive and analytical capacities in the exploratory phase, an ESDA tool was developed which enabled the user to interactively explore a dynamic multivariate geo- visualization of spatio-temporal point data in a highly detailed and information rich spatial context. With the plotKML R package, a geo-visualization was developed to be presented in Google Earth (GE) interface environment. The research used the electric vehicle charge session data from the municipality of Amsterdam, the Netherlands, as case study data. Users were asked to generate hypotheses on the "why there?" and "why then?" by relating their observations in the geo-visualization to spatial context information. Results showed that the new ESDA method was suitable to generate hypotheses on spatio- temporal point data. The combination of plotKML and GE was able to facilitate an interactive exploration of spatio-temporal data. The ability of the GE interface to visualize day-night transitions is unique and proved very useful. The assessment also s

关键词： exploratory spatial data analysis geo-visualization hypothesis generation spatio-temporal data plotKML Google Earth electric vehicle charging

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Exploring the spatial structure of housing prices under economic expansion and stagnation: The role of socio-demographic factors in metropolitan Rome, Italy

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LAND USE POLICY 2019年 81卷 143-152页

作者： Salvati, Luca Ciommi, Maria Teresa Serra, Pere Chelli, Francesco M. Council Agr Res & Econ CREA Viale Santa Margherita 80 I-52100 Arezzo Italy Czech Acad Sci Global Change Res Inst Lipova 9 CZ-37005 Ceske Budejovice Czech Republic Univ Politecn Marche Dept Econ & Social Sci Piazzale Martelli 8 I-60121 Ancona Italy Univ Autonoma Barcelona Dept Geog Grumets Res Grp Edifici B ES-08193 Cerdanyola Del Valles Spain

analysis of changes over time in the spatial structure of housing prices provides reliable information to infer latent patterns and processes of urban growth. Although the 2008 financial recession had a negative impact on building activity in Europe - and especially in southern Europe - the overall impact of the global crisis on the spatial structure of housing prices has been occasionally investigated at the local scale. The present study compares the spatial structure of transaction prices under economic expansion (2003-2009) and recession (2009-2015) in Rome (central Italy), evaluating the combined influence of demography, land-use and territorial factors. Transaction prices increased significantly during expansion and decreased less sharply during recession. The highest housing prices were recorded in downtown Rome. Convergence and divergence in the level of housing prices between urban and rural areas were observed respectively during economic expansion and recession. The empirical results of this study indicate that the 2008 financial crisis influenced considerably the spatial structure of housing prices in Rome, weakening the urban-rural divide and consolidating the gap between wealthy and poor neighbourhoods.

关键词： Urban growth Residential settlement exploratory spatial data analysis Italy

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A spatial View of How United States Cesarean Section Rates Changed from 1990 to 2014

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PROFESSIONAL GEOGRAPHER 2019年第4期71卷 762-769页

作者： Weimer, Kathleen R. Farmer, Carson J. Q. Reid, Colleen E. Univ Colorado Boulder CO 80309 USA Univ Colorado Geog Boulder CO 80304 USA

The overuse of cesarean sections (C-sections) in the United States is a contested issue. The rate of C-section births in 2015 at 32 percent was over double the World Health Organization recommendation of 10 to 15 percent. We employed spatial statistical methods and data visualization techniques to assess the temporal and spatial trends in C-section rates by county across the United States. Although the national rate of C-section remained stable at the beginning and end of this study period, an increase in rates from 1997 to 2009 was reflected simultaneously in national, state, and individual county rates. Local indicators of spatial dependence did not show spatial clustering as being connected to, or driving, the change, yet the visualization methods used here show details on individual county deviance from local temporal trends. By highlighting counties that do not follow the trends of their neighbors, we identify exceptional locations that could help further the study of the determinants of changing C-section rates in the United States.

关键词： cesarean sections exploratory spatial data analysis medical geography spatial statistics

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Measuring the negative externalities of a private leisure activity: hooligans and pickpockets around the stadium

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JOURNAL OF ECONOMIC GEOGRAPHY 2019年第2期19卷 465-504页

作者： Montolio, Daniel Planells-Struse, Simon Univ Barcelona IEB Fac Econ & Empresa Av Diagonal 690Torre 6 Planta 2 Barcelona 08034 Spain Univ Miguel Hernandez Ctr Crimina Avda Univ S-NEdif Helike Elche Alicant Elche 03201 Spain

Given the recent increase observed in crime related to football matches, we determine the extent to which this private leisure activity is responsible for negative crime externalities. We conduct an exploratory spatial data analysis and spatial regressions at the census tract level drawing on data for the matches played by Football Club Barcelona and geocoded crime data for the city of Barcelona (Spain);focusing on thefts (mainly pick pocketing) and assaults (interpersonal violence or hooliganism). We find an increase in the number of thefts across the whole city but, especially, in those census tracts within a 700-m radius of the stadium. These results are confirmed by the low number of crimes committed during away matches in the census tracts around the stadium. A similar spatial pattern is found for assaults. Our results provide evidence of a displacement effect of violent supporters (hooligans) towards the stadium on football days.

关键词： Crime football hooliganism negative externalities police forces exploratory spatial data analysis K42 H27 R1 L83

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Environmental and economic assessment of traffic-related air pollution using aggregate spatial information: A case study of Balneario Camboriu, Brazil

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JOURNAL OF TRANSPORT & HEALTH 2019年 14卷

作者： Tischer, Vinicius Fountas, Grigorios Polette, Marcus Rye, Tom Univ Vale Itajai Sch Marine Sci & Technol Rua Uruguai 458 Itajai SC Brazil Edinburgh Napier Univ Sch Engn & Built Environm Transport Res Inst 10 Colinton Rd Edinburgh EH10 5DT Midlothian Scotland

Introduction: Transportation is one of the main determinants of atmospheric pollutant emissions in urban areas. This externality has direct environmental, economic and public health consequences. This paper aims at investigating the space-time patterns of traffic air pollution in Balneario Camboriu (Brazil) over projected temporal scenarios and at estimating the damage costs of traffic air pollution to support transport policy-making. Methods: To estimate the emission rates of pollutants, emission factors and traffic data were jointly used, whereas the pollutant concentrations were estimated using the Gaussian plume dispersion model. To identify the affected areas as well as possible spatial heterogeneity patterns of air pollution within clustered areas, an exploratory spatial analysis was also conducted. To assess the economic impact of air pollution, damage costs were calculated for various pollutants. Results: The modeling results show that oxides of nitrogen (NO2) and oxides of sulphur (SO2) pollutants exceed the limits of air quality legislation, especially at a distance up to 10 m from the roads, while 60% and 71% of the intersections are found to yield pollutant concentrations above the thresholds, primarily during peak hours. The analysis also confirmed that homogeneous traffic zones with similar emission rates are spatially clustered exhibiting positive autocorrelation patterns. The results of the economic appraisal showed that the estimated costs of traffic-related emissions were $0.89, $1.38 and $1.43 million/year, respectively, for the current, short-term and long-term scenarios. Conclusions: This study serves as the first comprehensive analysis of traffic air pollution for the specific study region, providing implications and modeling tools that can be leveraged in public policies focusing on the elimination of the transportation-generated health burden. The developed analysis framework can also serve as a supporting tool for Public Agencies focusing on

关键词： Air pollution Emission factors exploratory spatial data analysis Damage costs Traffic Brazil

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spatial analysis of dual-scale water stresses based on water footprint accounting in the Haihe River Basin, China

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ECOLOGICAL INDICATORS 2018年 92卷 254-267页

作者： Li, Chunhui Xu, Meng Wang, Xuan Tan, Qian Beijing Normal Univ Sch Environm Key Lab Water & Sand Sci Minist Educ Beijing 100875 Peoples R China Zhejiang Univ Finance & Econ Sch Publ Adm Hangzhou 310018 Zhejiang Peoples R China China Agr Univ Coll Water Resources & Civil Engn Beijing 100083 Peoples R China Beijing Normal Univ Sch Environm State Key Lab Water Environm Simulat Beijing 100875 Peoples R China

Water scarcity and degradation of water quality in river basins are among the major issues addressed by water resources management authorities. Moreover, two typical challenges associated with water resources management include naturally unbalanced distribution and administrative disparities under multiple jurisdictions at the watershed scale. Effective accounting and management methods are thus desired to deal with such challenges. Water footprint accounting is widely used for assessing natural water resources availability and supporting optimal allocation among multiple jurisdictions, representing a useful tool for improving watershed sustainability. Not only will this enable successful revealing of the direct water consumption by relevant agents, but also indirect water consumption by concerning users. Such a method is particularly useful for addressing water-related issues in numerous watersheds of developing countries, which are subject to diverse water stressors. Hence, this study aimed at conducting water footprint accounting under city/regional and basin scales for the investigation of the amounts of blue and gray water in the Haihe River Basin (HRB), China. The Blue Water Footprint Index (BWFI) and the Gray Water Footprint Capacity Coefficient (K) were introduced to comprehensively evaluate water scarcity in terms of quantity and quality. The results revealed that all cities of the HRB suffered from extreme water scarcity. The industrial sector was identified as the primary contributor to the blue water footprint at the watershed. The agricultural gray-water footprint of the HRB accounted for 54% of the total. The exploratory spatial data analysis (ESDA) was adopted to analyze the spatial auto-correlation features of blue and gray water in the HRB. The results indicated that Dezhou and Binzhou had the highest water deficiency in both of the water stresses, and that Xinxiang, Puyang, Anyang and Jiaozuo had the highest water problem predominantly due to water

关键词： Blue water footprint Gray water footprint Haihe River Basin exploratory spatial data analysis Water footprint accounting

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A Rural Water Poverty analysis in China Using the DPSIR-PLS Model

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WATER RESOURCES MANAGEMENT 2018年第6期32卷 1933-1951页

作者： Sun, Caizhi Wu, Yongjie Zou, Wei Zhao, Liangshi Liu, Wenxin Liaoning Normal Univ Coll Urban & Environm Sci 850 Huanghe Rd Dalian 116029 Peoples R China Liaoning Normal Univ Sch Foreign Languages 850 Huanghe Rd Dalian 116029 Peoples R China Liaoning Normal Univ Ctr Studies Marine Econ & Sustainable Dev 850 Huanghe Rd Dalian 116029 Peoples R China

Water poverty assessment is a complex problem involving multiple systems such as water resources, the economy, society, and the environment. Thus, it is necessary to employ a comprehensive model comprising a set of interdisciplinary indicators that encompass water-related aspects, as well as a conceptual framework to organize all indicators and variables. Firstly, we develop water poverty indicators for rural areas of China by introducing the Driving forces-Pressures-States-Impacts-Responses (DPSIR) model, based on causality. Then, the indicator system and structural equation model (SEM) are combined, and the partial least squares approach to structural equation modeling (PLS-SEM) tests the hypothesis based on the causal relationship of the DPSIR model. Secondly, the weight of each variable is obtained using the modified model and structural equation software, and water poverty scores are calculated for each area. exploratory spatial analysis of the results suggests that the distribution of water poverty in China's rural areas displays low-low and high-high clustering. Policy makers should take advantage of the water resource situation in eastern and central regions to help the northwest, southwest, and southeast regions alleviate problems of water poverty.

关键词： Rural water poverty Partial least squares Structural equation exploratory spatial data analysis spatial clustering

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spatial Regression analysis of Poverty in R

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spatial DEMOGRAPHY 2019年第2-3期7卷 113-147页

作者： Kamenetsky, Maria Chi, Guangqing Wang, Donghui Zhu, Jun Univ Wisconsin Dept Populat Hlth Sci 610 Walnut St Madison WI 53726 USA Penn State Univ Populat Res Inst Dept Agr Econ Sociol & Educ 112E Armsby University Pk PA 16802 USA Penn State Univ Social Sci Res Inst 112E Armsby University Pk PA 16802 USA Princeton Univ Ctr Contemporary China 359 Wallace Hall Princeton NJ 08544 USA Univ Wisconsin Dept Stat 1300 Univ Ave Madison WI 53706 USA

Poverty has been studied across many social science disciplines, resulting in a large body of literature. Scholars of poverty research have long recognized that the poor are not uniformly distributed across space. Understanding the spatial aspect of poverty is important because it helps us understand place-based structural inequalities. There are many spatial regression models, but there is a learning curve to learn and apply them to poverty research. This manuscript aims to introduce the concepts of spatial regression modeling and walk the reader through the steps of conducting poverty research using R: standard exploratory data analysis, standard linear regression, neighborhood structure and spatial weight matrix, exploratory spatial data analysis, and spatial linear regression. We also discuss the spatial heterogeneity and spatial panel aspects of poverty. We provide code for data analysis in the R environment and readers can modify it for their own data analyses. We also present results in their raw format to help readers become familiar with the R environment.

关键词： Poverty exploratory spatial data analysis spatial regression R

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