scholarly journals How Far Has China’s Urbanization Gone?

2018 ◽  
Vol 10 (8) ◽  
pp. 2953 ◽  
Author(s):  
Yiping Xiao ◽  
Yan Song ◽  
Xiaodong Wu
Keyword(s):  
Urban Development ◽  
Spatial Autocorrelation ◽  
Spatial Data ◽  
Spatial Association ◽  
Early Years ◽  
Spatial Data Analysis ◽  
Rapid Urbanization ◽  
Urban Rural ◽  
Global Spatial Autocorrelation ◽  
Over Time

China’s rapid urbanization has attracted wide international attention. However, it may not be sustainable. In order to assess it objectively and put forward recommendations for future development, this paper first develops a four-dimensional Urbanization Quality Index using weights calculated by the Deviation Maximization Method for a comprehensive assessment and then reveals the spatial association of China’s urbanization by Exploratory Spatial Data Analysis. The study leads to three major findings. First, the urbanization quality in China has gradually increased over time, but there have been significant differences between regions. Second, the four aspects of urbanization quality have shown the following trends: (i) the quality of urban development has steadily increased; (ii) the sustainability of urban development has shown a downward trend in recent years; (iii) the efficiency of urbanization improved before 2006 but then declined slightly due to capital, land use, and resource efficiency constraints; (IV) the urban–rural integration deteriorated in the early years but then improved over time. Third, although the urbanization quality has a significantly positive global spatial autocorrelation, the local spatial autocorrelation varies between eastern and western regions. Based on these findings, this paper concludes with policy recommendations for improving urbanization quality and its sustainability in China.

scholarly journals Spatial and Temporal Evaluation of Ecological Footprint Intensity of Jiangsu Province at the County-Level Scale

2020 ◽  
Vol 17 (21) ◽  
pp. 7833
Author(s):  
Decun Wu ◽  
Jinping Liu
Keyword(s):  
Spatial Data ◽  
Ecological Footprint ◽  
Temporal Trends ◽  
Spatial Association ◽  
Jiangsu Province ◽  
Control Measures ◽  
Spatial Data Analysis ◽  
Spatial Distributions ◽  
County Level ◽  
Over Time

Due to the high ecological pressure that exists in the process of rapid economic development in Jiangsu Province, it is necessary to evaluate its ecological footprint intensity (EFI). This article focuses on ecological footprint intensity analysis at the county scale. We used county-level data to evaluate the spatial distributions and temporal trends of the ecological footprint intensity in Jiangsu’s counties from 1995 to 2015. The temporal trends of counties are divided into five types: linear declining type, N-shape type, inverted-N type, U-shape type and inverted-U shape type. It was discovered that the proportions of the carbon footprint intensity were maintained or increased in most counties. Exploratory spatial data analysis shows that there was a certain regularity of the EFI spatial distributions, i.e., a gradient decrease from north to south, and there was a decline in the spatial heterogeneity of EFI in Jiangsu’s counties over time. The global Moran’s index (Moran’s I) and local spatial association index (LISA) are used to analyze both the global and local spatial correlation of EFIs among counties of Jiangsu Province. The high-high and low-low agglomeration effects were the most common, and there were assimilation impacts of counties with strong agglomeration on adjacent units over time. The results implied the utility of differentiated EFI reduction control measures and promotion of low-low agglomeration and suppression of high-high agglomeration in EFI-related ecology policy.

Analyzing The Violent Crime Patterns In Peninsular Malaysia : Exploratory Spatial Data Analysis (ESDA) Approach

2014 ◽  
Vol 72 (1) ◽  
Author(s):  
Syerrina Zakaria ◽  
Nuzlinda Abd. Rahman
Keyword(s):  
Data Analysis ◽  
Spatial Autocorrelation ◽  
Violent Crime ◽  
Spatial Data ◽  
Hot Spot ◽  
Peninsular Malaysia ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
Policy Makers ◽  
Violent Crime Rate

The objective of this study is to analyze the spatial cluster of crime cases in Peninsular Malaysia by using the exploratory spatial data analysis (ESDA). In order to identify and measure the spatial autocorrelation (cluster), Moran’s I index were measured. Based on the cluster analyses, the hot spot of the violent crime occurrence was mapped. Maps were constructed by overlaying hot spot of violent crime rate for the year 2001, 2005 and 2009. As a result, the hypothesis of spatial randomness was rejected indicating cluster effect existed in the study area. The findings reveal that crime was distributed nonrandomly, suggestive of positive spatial autocorrelation. The findings of this study can be used by the goverment, policy makers or responsible agencies to take any related action in term of crime prevention, human resource allocation and law enforcemant in order to overcome this important issue in the future. 

scholarly journals SPATIAL STUDY OF INCIDENCE RATES OF OCCUPATIONAL ACCIDENTS IN BRAZIL FROM 2002 TO 2012

2018 ◽  
Vol 36 (4) ◽  
pp. 927
Author(s):  
André Luis Santiago MAIA ◽  
Gecynalda Soares da Silva GOMES ◽  
Isabelle Galdino de ALMEIDA
Keyword(s):  
Spatial Data ◽  
Spatial Association ◽  
Job Creation ◽  
Incidence Rates ◽  
Spatial Data Analysis ◽  
Occupational Accidents ◽  
Exploratory Spatial Data Analysis ◽  
High Incidence ◽  
Random Manner ◽  
Critical Regions

The intensive process of economic growth and job creation in Brazil in the last years is often associated an important dimension where this process is far drop satisfactory: the high incidence rates of occupational accidents. Important instruments can be constructed from the quantitative study considering possible changes caused by economic dynamics over the years. We conducted exploratory spatial data analysis  (ESDA) and Local Indicators of Spatial Association (LISA) to analyze the spatial distribution of this rate in order to identify critical regions in Brazil. Data were extracted from the Brazilian Ministry of Labor and Employment (MTE) and from the Brazilian Ministry of Social Security websites for the years from 2002 to 2012. Results show that the incidence rate of occupational accidents in Brazil is distributed in a geographically non-random manner and municipalities with high rates tends to cluster.

Assessing Spatial Equity: An Evaluation of Measures of Accessibility to Public Playgrounds

1998 ◽  
Vol 30 (4) ◽  
pp. 595-613 ◽  
Author(s):  
E Talen ◽  
L Anselin
Keyword(s):  
Spatial Data ◽  
Empirical Studies ◽  
Spatial Association ◽  
Spatial Data Analysis ◽  
Socioeconomic Indicators ◽  
Model Average ◽  
Urban Service ◽  
Spatial Equity ◽  
Analytical Perspective

Geographical and political research on urban service delivery—who benefits and why—has proliferated during the past two decades. Overall, this literature is not characterized by a particular attention to the importance of method in drawing conclusions about spatial equity based on empirical studies. Specifically, there has been scant interest in the effect of geographic methodology on assessing the relationship between access and socioeconomic characteristics that are spatially defined. In this paper we take a spatial analytical perspective to evaluate the importance of methodology in assessing whether or not, or to what degree the distribution of urban public services is equitable. We approach this issue by means of an empirical case study of the spatial distribution of playgrounds in Tulsa, Oklahoma, relative to that of the targeted constituencies (children) and other socioeconomic indicators. In addition to the ‘traditional’ measure (count of facilities in an areal unit), we consider a potential measure (based on the gravity model), average travel distance, and distance to the nearest playground as indicators of accessibility. We find significant differences between the spatial patterns in these measures that are suggested by local indicators of spatial association and other techniques of exploratory spatial data analysis. The choice of access measure not only implies a particular treatment of spatial externalities but also affects conclusions about the existence of spatial mismatch and inequity.

scholarly journals SPATIAL PATTERN OF SUSTAINABLE URBAN DEVELOPMENT INDICATOR FOR THE MONTREAL URBAN COMMUNITY

2015 ◽  
Vol 39 (4) ◽  
pp. 220-231 ◽  
Author(s):  
Shohel Reza Amin ◽  
Umma Tamima
Keyword(s):  
Sustainable Development ◽  
Urban Development ◽  
Spatial Pattern ◽  
Spatial Data ◽  
Urban Community ◽  
Spatial Data Analysis ◽  
Sustainable Urban Development ◽  
Development Indicator ◽  
Development Plans ◽  
The City

The City of Montreal initiated a First Strategic Plan for Sustainable Development in 2005 followed by a Community and Corporate Sustainable Development Plan in 2010–2015. This study proposes a sustainable urban development indicator (SUDI) for each Montreal Urban Community (MUC) to evaluate the achievements of sustainable development plans. This study identifies thirty-two variables as the attributes of sustainable urban development. The multivariate technique and Exploratory Spatial Data Analysis are applied to determine the spatial pattern of SUDI for each MUC. The spatial pattern of SUDI identifies that Ville Marie, Verdun, Sud-Ouest, Mercier-Hochelaga-Maisonneuve and Plateau Mont-Royal have strong sustainable development. The findings of this study help the City of Montreal to understand the improvement of the sustainable development plans for Montreal city and to distribute the municipal budget for the community benefits accordingly.

Analysis on Spatial Difference of the Rural Resident’s per Capita Net Income in Qinhuangdao City Based on ESDA

2014 ◽  
Vol 955-959 ◽  
pp. 3893-3898
Author(s):  
Yu Hong Wu
Keyword(s):  
Spatial Data ◽  
Rural Economy ◽  
Spatial Association ◽  
Spatial Data Analysis ◽  
Moran’S I ◽  
Net Income ◽  
Gis Technology ◽  
Moran's I ◽  
Per Capita ◽  
Global Moran’S I

Based on the exploratory spatial data analysis (ESDA) and GIS technology, the spatial differences of the rural economic development level of Qinhuangdao city was investigated by adopting the rural resident’s per capita net income data at town level in Qinhuangdao city from 2007 to 2011. The results of global Moran’s I value for rural resident’s per capita net income at town level showed that there existed significant positive spatial autocorrelation and significant spatial aggregation in the spatial distribution of rural resident’s per capita net income. However, the global Moran’s I value showed a decreasing trend during 2007 to 2011, indicating an enlarged spatial disparity of rural economy at the town level. The results of the Moran scatter plots and LISA cluster maps of 2007 and 2011 showed that most of towns were characterized by positive local spatial association , ie. They were located in the HH or the LL quadrant. The significant HH towns were mostly to be found in the south of Qinhuangdao city, Haigang district, Changli county, Lulong county. The significant LL towns were mostly to be found in the Qinglong county, north of Qinhuangdao city.

Spatial Pattern Evolution of Coal Consumption at Provincial Level in China

2013 ◽  
Vol 734-737 ◽  
pp. 1752-1756
Author(s):  
Kai Yong She ◽  
Wen Jun Chen
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Data ◽  
Hot Spots ◽  
Spatial Interaction ◽  
Interaction Mechanism ◽  
Spatial Data Analysis ◽  
Management Policy ◽  
Coal Consumption ◽  
Cold Spots ◽  
Consumption Activity

Exploratory Spatial Data Analysis was used to analyze the evolvement of spatia1 pattern on coal consumption in China since 2002. General spatial autocorrelation of coal consumption in 31 provinces of China was analyzed by Morans I and Getis-Ord General G. Getis-0rd Gi* was used to test the local spatial dependence, identifying the spatial distribution of hot spots and cold spots. The results show that coal consumption per capita of 31 provinces in China exhibits an enhanced trend of spatial autocorrelation. The areas with similar level of coal consumption are clustered in space. The coal consumption activity can be affected by the neighborhoods and their own regions. Hotspot areas are mainly concentrated in North and Northeast China and continuously increase with time, coldspot areas are mainly concentrated in South China and constantly decrease by time. So government needs to consider the spatial interaction mechanism of coal consumption when establishing the energy management policy.

scholarly journals Metropolitan urban hotspots of chronic sleep deprivation: evidence from a community health survey in Gyeongbuk Province, South Korea

2015 ◽  
Vol 10 (2) ◽  
Author(s):  
Sun-Bi Um ◽  
Jung-Sup Um
Keyword(s):  
South Korea ◽  
Sleep Deprivation ◽  
Community Health ◽  
Health Survey ◽  
Spatial Data ◽  
Spatial Association ◽  
Spatial Data Analysis ◽  
Community Health Survey ◽  
Chronic Sleep Deprivation ◽  
Chronic Sleep

The geographic concentration of chronic sleep deprivation (CSD) remains largely unexplored. This paper examined the community-specific spatial pattern of the prevalence of CSD and the presence of clustered spatial hotspots among the Korean elderly population in Gyeongbuk Province, South Korea, revealing CSD hotspots and underscoring the importance of geography-focused prevention strategies. The study analysed cross-sectional data collected from 9847 elderly individuals aged 60 years and older who participated in a Korean Community Health Survey conducted in 2012. To assess the level of spatial dependence, an exploratory spatial data analysis was conducted using Global Moran’s I statistic and the local indicator of spatial association. The results revealed marked geographic variations in CSD prevalence ranging from 33.4 to 73.4%, with higher values in the metropolitan urban areas and lower in the rural areas. Almost half of the community residents [both men (44.1%) and women (53.5%)] slept 6 h or less per 24 h. The average CSD prevalence (53.6% men and 65.1% women) in the hotspots was about 13.0% higher than that in other areas (42.6% for men and 51.1% for women). To our knowledge, this is the first study to generate a CSD hotspot map that includes data on sleep deprivation across metropolitan district levels. This study demonstrates that not only is sleep deprivation distributed differentially across communities but these differences may be explained by urbanisation.

scholarly journals A Statistical Toolbox For Mining And Modeling Spatial Data

2017 ◽  
Vol 19 (5) ◽  
pp. 5-24
Author(s):  
Gérard D’Aubigny
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Spatial Autocorrelation ◽  
Spatial Data ◽  
Point Of View ◽  
Spatial Data Analysis ◽  
Formal Framework ◽  
Mining Projects ◽  
Small Change ◽  
Spatial Entities

Most data mining projects in spatial economics start with an evaluation of a set of attribute variables on a sample of spatial entities, looking for the existence and strength of spatial autocorrelation, based on the Moran’s and the Geary’s coefficients, the adequacy of which is rarely challenged, despite the fact that when reporting on their properties, many users seem likely to make mistakes and to foster confusion. My paper begins by a critical appraisal of the classical definition and rational of these indices. I argue that while intuitively founded, they are plagued by an inconsistency in their conception. Then, I propose a principled small change leading to corrected spatial autocorrelation coefficients, which strongly simplifies their relationship, and opens the way to an augmented toolbox of statistical methods of dimension reduction and data visualization, also useful for modeling purposes. A second section presents a formal framework, adapted from recent work in statistical learning, which gives theoretical support to our definition of corrected spatial autocorrelation coefficients. More specifically, the multivariate data mining methods presented here, are easily implementable on the existing (free) software, yield methods useful to exploit the proposed corrections in spatial data analysis practice, and, from a mathematical point of view, whose asymptotic behavior, already studied in a series of papers by Belkin & Niyogi, suggests that they own qualities of robustness and a limited sensitivity to the Modifiable Areal Unit Problem (MAUP), valuable in exploratory spatial data analysis.

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