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Exploring associations between streetscape factors and crime behaviors using Google Street View images

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Frontiers of Computer Science 2022年第4期16卷 45-58页

作者： Mingyu Deng Wei Yang Chao Chen Chenxi Liu School of Computer Science Chongqing UniversityChongqing400044China School of Management Science and Real Estate Chongqing UniversityChongqing400044China State Key Laboratory of Mechanical Transmission Chongqing UniversityChongqing400044China

Understanding the influencing mechanism of the urban streetscape on crime is fairly important to crime prevention and urban ***,the development of deep learning technology and big data of street view images,makes it possible to quantitatively explore the relationship between streetscape and *** study computed eight streetscape indexes of the street built environment using Google Street View images ***,the association between the eight indexes and recorded crime events was revealed with a poisson regression model and a geographically weighted poisson regression *** experiment was conducted in downtown and uptown Manhattan,New *** regression results show that the influences of Motorization Index on crimes are significant and positive,while the effects of the Light View Index and Green View Index on crimes depend heavily on the socioeconomic *** a local perspective,the Pedestrian Space Index,Green View Index,Light View Index and Motorization Index have a significant spatial influence on crimes,while the same visual streetscape factors have different effects on different streets due to the combination differences of socioeconomic,cultural and streetscape *** key streetscape elements of a given street that affect a specific criminal activity can be identified according to the strength of the *** results provide both theoretical and practical implications for crime theories and crime prevention efforts.

关键词： crime Google Street View streetscape spatial analysis geographically weighted poisson regression

The habitation Model Trend Calculation(MTC):A new effective tool for predictive modeling in archaeology

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Geo-spatial Information Science 2019年第4期22卷 314-331,I0008页

作者： George Malaperdas Nikolaos Zacharias Department of History Archaeology and Cultural Resources ManagementUniversity of PeloponneseKalamataGreece

The aim of this paper is to create and present a new archaeological predictive model via GIS,incorporating what archaeologists consider the most important criterion absent of similar past models,that of critical *** new model suggested in this paper is named habitation Model Trend Calculation(MTC)and is not only integrates the archaeological questions with a critical view,but it can be easily adjusted,according to the conditions or the questions concerning the archaeological ***,it uses new topographical and geomorphological indexes such as Topographical Index(TPI),Hillslope and Landform Classification that give a new sense of the topographical and geomorphological characteristics of the examined area;therefore this model is a more powerful tool compared to older models that did not use new topographical and geomorphological *** success of the created model is checked as a case study in the region of Messenia,Greece during the Mycenaean *** region of Messenia is considered as one of the most important Mycenaean regions of Greece due to the great number and the importance of Mycenaean sites *** the present paper,140 habitation sites were divided into four hierarchical categories(centers,large villages,villages,and farms)based on the extent and the plurality of the tholos tombs that exist in the broader region and according to the hierarchical categorization used by the archaeologists who have studied the *** new predictive model presented in this work can assist in solving a series of criticisms that have been expressed in the previous studies regarding such ***,in the case of Mycenaean Messenia,the model shows excellent results in relation to the habitats of the time.

关键词： GIS Mycenaean Messenia predictive model archaeology habitation Model Trend Calculation(MTC) analytical hierarchy process(AHP) spatial analysis

Bridging open source tools and Geoportals for interactive spatial data analytics

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Geo-spatial Information Science 2019年第3期22卷 185-192页

作者： Bing She Tao Hu Xinyan Zhu Shuming Bao University of Michigan Ann ArborMIUSA Center for Geographical analysis Harvard UniversityCambridgeMAUSA Geocomputation Center for Social Sciences Wuhan UniversityWuhanChina State Key Lab of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina Collaborative Innovation Center of Geospatial Technology WuhanChina China Data Institute Ann ArborMIUSA

Geoportals have been the primary source of spatial information to researchers in diverse *** years have seen a growing trend to integrate spatial analysis and geovisual analytics inside *** could use the Geoportal to conduct basic analysis without offline *** practice,domain-specific analysis often requires researchers to integrate heterogeneous data sources,leverage new statistical models,or build their own customized *** tasks are increasingly being tackled with open source tools in programming languages such as Python or ***,it is unrealistic to incorporate the numerous open source tools in a Geoportal platform for data processing and *** work provides an exploratory effort to bridge Geoportals and open source tools through Python *** Geoportal demonstrated in this work is the Urban and Regional Explorer for China studies.A python package is provided to manipulate this platform in the local programming *** server side of the Geoportal implements a set of service endpoints that allows the package to upload,transform,and process user data and seamlessly integrate them into the existing datasets.A case study is provided that illustrated the use of this package to conduct integrated analyses of search engine data and baseline census *** work attempts a new direction in Geoportal development,which could further promote the transformation of Geoportals into online analytical workbenches.

关键词： Geoportal open source Python web GIS spatial analysis

Vulnerability of groundwater resources under climate change in the Pannonian basin

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Geo-spatial Information Science 2019年第4期22卷 345-358,I0010页

作者： Mărgărit-Mircea Nistor National Institute for Economic Research“Costin C.Kiritescu”of the Romanian Academy/Department of Mountain Economy(CE-MONT) Vatra DorneiRomania Department of Hydrogeology Earthresearch CompanyCluj-NapocaRomania

The impact of climate change on groundwater vulnerability has been assessed in the Pannonian basin over 1961–***-resolution climatemodels,aquifers composition,land cover,and digital elevation model were the main factors which served to perform the spatial analysis using Geographical Information *** analysis reported here is focused on the long-term period,including three temporal time sets:the past period of 1961–1990(1990s),the present period of 2011–2040(2020s),and the future period of 2041–2070(2050s).During the 1990s,the high and very high areas of groundwater vulnerability were identified in all the central,western,eastern,southeastern,and northern sides of the Pannonian *** these areas,the water availability is lower and the pollution load index is high,due to the agricultural *** low and very low vulnerability classwas depicted in the South-West part of the basin and in few locations from the peripheral areas,mainly in the North and *** medium groundwater vulnerability spreads over the Pannonian basin,but it ismore concentrated in the central,South,and *** most affected territory is Hungary,while the territories of Slovenia,Croatia,and Bosnia and Herzegovina are less *** the present and future periods,the very high groundwater vulnerability increased in areas by 0.74%and 0.87%,*** low class area decreased between the 1990s and the 2020s by 2.33%and it is expected to decrease up to 2.97%in the *** on this analysis and the groundwater vulnerabilitymaps,the Pannonian basin appears more vulnerable to climate change in the present and *** findings demonstrate that the aquifers from Pannonian basin experience high negative effect under climate *** addition,the land cover contributes to this negative status of groundwater *** original maps of groundwater vulnerability represent an instrument for water management planning and for research.

关键词： Vulnerability aquifers spatial analysis climatic models pollution load index infiltration index water availability

Energy characteristics of urban buildings: Assessment by machine learning

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Building Simulation 2021年第1期14卷 179-193页

作者： Wei Tian Chuanqi Zhu Yu Sun Zhanyong Li Baoquan Yin Tianjin Key Laboratory of Integrated Design and On-line Monitoring for Light Industry&Food Machinery and Equipment College of Mechanical EngineeringTianjin University of Science and TechnologyTianjin300222China Tianjin International Joint Research and Development Center of Low-Carbon Green Process Equipment Tianjin300222China School of Architecture Harbin Institute of TechnologyHarbin150090China Tianjin Architecture Design Institute Tianjin300074China

Machine learning techniques have attracted more attention as advanced data analytics in building energy ***,most of previous studies are only focused on the prediction capability of machine learning algorithms to provide reliable energy estimation in *** learning also has great potentials to identify energy patterns for urban buildings except for model ***,this paper explores energy characteristic of London domestic properties using ten machine learning algorithms from three aspects:tuning process of learning model;variable importance;spatial analysis of model *** results indicate that the combination of these three aspects can provide insights on energy patterns for urban *** tuning process of these models indicates that gas use models should have more terms in comparison with electricity in London and the interaction terms should be considered in both gas and electricity *** rankings of important variables are very different for gas and electricity prediction in London residential buildings,which suggests that gas and electricity use are affected by different physical and social ***,the importance levels for these key variables are markedly different for gas and electricity *** are much more important variables for electricity use in comparison with gas use for the importance levels over *** areas with larger model discrepancies can be determined using the local spatial analysis based on these machine learning *** identified areas have significantly different energy patterns for gas and electricity *** research is required to understand these unusual patterns of energy use in these areas.

关键词： urban buildings energy characteristics machine learning variable importance spatial analysis

Bayesian mapping of neural tube defects prevalence in Heshun County, Shanxi Province, China during 1998～2001

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2007年第6期8卷 921-925页

作者： CHI Wen-xue WANG Jin-feng LI Xin-hu ZHENG Xiao-ying LIAO Yi-lan China University of Geosciences Beijing 100083 China Institute of Geographical Sciences and Nature Resources Research Chinese Academy of Sciences Beo＇ing 100101 China Institute of Population Research Peking University Beijing 100871 China

Objective: To estimate the prevalence rates of neural tube defects (NTDs) in Heshun County, Shanxi Province, China by Bayesian smoothing technique. Methods: A total of 80 infants in the study area who were diagnosed with NTDs were analyzed. Two mapping techniques were then used. Firstly, the GIS software ArcGIS was used to map the crude prevalence rates. Secondly, the data were smoothed by the method of empirical Bayes estimation. Results: The classical statistical approach produced an extremely dishomogeneous map, while the Bayesian map was much smoother and more interpretable. The maps produced by the Bayesian technique indicate the tendency of villages in the southeastern region to produce higher prevalence or risk values. Conclusions: The Bayesian smoothing technique addresses the issue of heterogeneity in the population at risk and it is therefore recommended for use in explorative mapping of birth defects. This approach provides procedures to identify spatial health risk levels and assists in generating hypothesis that will be investigated in further detail.

关键词： Birth defects Neural tube defects (NTDs) Disease map spatial analysis Bayesian smoothing China

analysis of Types and Changes of Village-level Economy in Rural Gongyi City, Henan Province Since 1990

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Chinese Geographical Science 2008年第2期18卷 101-108页

作者： QIAO Jiajun KONG Yunfeng LI Xiaojian China-Australia Cooperative Research Institute for Geographic Information analysis and Applications Henan University Kaifeng 475001 China College of Environment ＆ Planning Henan University Kaifeng 475001 China）

Based on the research on the rural living standard in China in terms of annual net income per capita, we de-fine six types of village-level economy, i.e. "to be extremely poor", "to make a basic living", "to dress warmly and eat one's fill", "to try to enrich (to disengage poverty)", "to be well-off" and "to be affluent". The data of average annual net income of all the 292 villages between 1990 and 2004 in rural Gongyi City, Henan Province were collected, veri-fied and classified. By using standard deviation, coefficient of variation and regression analysis, it is found that the Gongyi's rural economy has boosted up remarkably from the relative-poverty and absolute-poverty stages in 1990 to the well-off in 2004. However, the absolute differences between villages present a trend of enlargement, while the rela-tive differences fluctuating. On the other hand, spatial analysis of village-level economy shows that most villages with relatively high economic development level were located along national expressway and most villages with abso-lute-poverty lay in remote mountainous areas in 1990. Since the 1990s, the rapid urbanization and industrialization have had strongly positive effects on rural economic growth. Initial economic foundation, natural resources and tradi-tional techniques also contribute to village economy. From the perspective of geography, villages with location advan-tages, such as near urban center or industrial parks, have more chances for their economic development and the "core-periphery" economic structure has been presented in the process of rural development.

关键词： rural village village-level economy net income spatial analysis Gongyi City

Promoting the production of salinized cotton field by optimizing water and nitrogen use efficiency under drip irrigation

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Journal of Arid Land 2021年第7期13卷 699-716页

作者： LIN En LIU Hongguang LI Xinxin LI Ling Sumera ANWAR College of Water Conservancy&Architectural Engineering Shihezi UniversityShihezi 832000China Xinjiang Production&Construction Group Key Laboratory of Modern Water-Saving Irrigation Shihezi 832000China Institute of Molecular Biologyand Biotechnology The University of LahoreLahore 54660Pakistan

Cotton is the main economically important crop in Xinjiang,China,but soil salinization and shortage of water and nutrients have restricted its production.A field experiment was carried out in the salinity-affected arid area of Northwest China from 2018 to 2019 to explore the effects of nitrogen and water regulation on physiological growth,yield,water and nitrogen use efficiencies,and economic benefit of *** salinity levels were 7.7(SL)and 12.5 dS/m(SM).Drip irrigation was used with low,medium and adequate irrigation levels representing 60%,80%and 100%of cotton crop water demand,respectively,and three nitrogen applications,i.e.,206,275 and 343 kg/hm^(2),accounting for 75%,100%and 125%of local N application,respectively were *** multi-objective optimization based on spatial analysis showed that,at SL salinity,water use efficiency(WUE),nitrogen use efficiency(NUE),economic benefit and yield simultaneously reached more than 85%of their maxima at 379.18-398.32 mm irrigation and 256.69-308.87 kg/hm^(2).At SM salinity,WUE,yield and economic benefit simultaneously reached more than 85%of their maxima when irrigation was 351.24-376.30 mm and nitrogen application was 230.18-289.89 kg/hm^(2).NUE,yield and economic benefit simultaneously reached their maxima at 428.01-337.72 mm irrigation,and nitrogen application range was 222.14-293.93 kg/hm^(2).The plants at SL salinity had 21.58%-46.59%higher WUE rates,14.91%-34.35%higher NUE rates and 20.71%-35.34%higher yields than those at SM *** results are of great importance for the nutrient and water management in cotton field in the arid saline area.

关键词： cotton growth multi-objective optimization soil salinization water and nitrogen regulation spatial analysis

GIS-based logistic regression method for landslide susceptibility mapping in regional scale

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2006年第12期7卷 2007-2017页

作者： ZHU Lei HUANG Jing-feng Department of Natural Science. Zhejiang University Hangzhou 310029 China Institute of Agricultural Remote Sensing and Information Technology Application Zhejiang University Hangzhou 310029 China Shandong Agriculture Administrators College Jinan 250100 China

Landslide susceptibility map is one of the study fields portraying the spatial distribution of future slope failure sus- ceptibility. This paper deals with past methods for producing landslide susceptibility map and divides these methods into 3 types. The logistic linear regression approach is further elaborated on by crosstabs method, which is used to analyze the relationship between the categorical or binary response variable and one or more continuous or categorical or binary explanatory variables derived from samples. It is an objective assignment of coefficients serving as weights of various factors under considerations while expert opinions make great difference in heuristic approaches. Different from deterministic approach, it is very applicable to regional scale. In this study, double logistic regression is applied in the study area. The entire study area is first analyzed. The logistic regression equation showed that elevation, proximity to road, river and residential area are main factors triggering land- slide occurrence in this area. The prediction accuracy of the first landslide susceptibility map was showed to be 80%. Along the road and residential area, almost all areas are in high landslide susceptibility zone. Some non-landslide areas are incorrectly divided into high and medium landslide susceptibility zone. In order to improve the status, a second logistic regression was done in high landslide susceptibility zone using landslide cells and non-landslide sample cells in this area. In the second logistic regression analysis, only engineering and geological conditions are important in these areas and are entered in the new logistic regression equation indicating that only areas with unstable engineering and geological conditions are prone to landslide during large scale engineering activity. Taking these two logistic regression results into account yields a new landslide susceptibility map. Double logistic regression analysis improved the non-landslid

关键词： Landslide Susceptibility Logistic regression GIS spatial analysis