scholarly journals SPATIAL ASSOCIATION TO CHARACTERIZE THE CLIMATE TELECONNECTION PATTERNS IN ECUADOR BASED ON SATELLITE PRECIPITATION ESTIMATES

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 101-106
Author(s):  
D. Ballari ◽  
L. Campozano ◽  
E. Samaniego ◽  
D. Orellana
Keyword(s):  
Spatial Patterns ◽  
Spatial Data ◽  
Large Scale ◽  
Spatial Association ◽  
Spatial Data Analysis ◽  
Moran’S I ◽  
Climate Indices ◽  
Moran's I ◽  
Local Climate ◽  
Teleconnection Patterns

Abstract. Climate teleconnections show remote and large-scale relationships between distant points on Earth. Their relations to precipitation are important to monitor and anticipate the anomalies that they can produce in the local climate, such as flood and drought events impacting agriculture, health, and hydropower generation. Climate teleconnections in relation to precipitation have been widely studied. Nevertheless, the spatial association of the teleconnection patterns (i.e. the spatial delineation of regions with teleconnections) has been unattended. Such spatial association allows to characterize how stable (heterogeneity/dependent and statistically significant) is the underlying spatial phenomena for a given pattern. Thus our objective was to characterize the spatial association of climate teleconnection patterns related to precipitation using an exploratory spatial data analysis approach. Global and local indicators of spatial association (Moran’s I and LISA) were used to detect spatial patterns of teleconnections based on TRMM satellite images and climate indices. Moran’s I depicted high positive spatial association for different climate indices, and LISA depicted two types of teleconnections patterns. The homogenous patterns were localized in the Coast and Amazonian regions, meanwhile the disperse patterns had a major presence in the Highlands. The results also showed some areas that, although with moderate to high teleconnection influences, had a random spatial patterns (i.e. non-significant spatial association). Other areas showed both teleconnections and significant spatial association, but with dispersed patterns. This pointed out the need to explore the local underlying features (topography, orientation, wind and micro-climates) that restrict (non-significant spatial association) or reaffirm (disperse patterns) the teleconnection patterns.

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.

scholarly journals Measuring The Impact of Spatial Perturbations on The Relationship Between Data Privacy and Validity of Descriptive Statistics

2020 ◽  
Author(s):  
Kelly Broen ◽  
Rob Trangucci ◽  
Jon Zelner
Keyword(s):  
Spatial Statistics ◽  
Spatial Patterns ◽  
Spatial Data ◽  
Data Privacy ◽  
Perturbation Methods ◽  
Descriptive Statistics ◽  
Moran’S I ◽  
Mortality Data ◽  
Moran's I ◽  
The Impact

Abstract Background: Like many scientific fields, epidemiology is addressing issues of research reproducibility. Spatial epidemiology, which often uses the inherently identifiable variable of participant address, must balance reproducibility with participant privacy. In this study, we assess the impact of several different data perturbation methods on key spatial statistics and patient privacy. Methods: We analyzed the impact of perturbation on spatial patterns in the full set of address- level mortality data from Lawrence, MA during the period from 1911-1913. The original death locations were perturbed using seven different published approaches to stochastic and deterministic spatial data anonymization. Key spatial descriptive statistics were calculated for each perturbation, including changes in spatial pattern center, Global Moran’s I, Local Moran’s I, distance to the k-th nearest neighbors, and the L-function (a normalized form of Ripley’s K). A spatially adapted form of k-anonymity was used to measure the privacy protection conferred by each method, and the its compliance with HIPAA privacy standards. Results: Random perturbation at 50 meters, donut masking between 5 and 50 meters, and Voronoi masking maintain the validity of descriptive spatial statistics better than other perturbations. Grid center masking with both 100x100 and 250x250 meter cells led to large changes in descriptive spatial statistics. None of the perturbation methods adhered to the HIPAA standard that all points have a k-anonymity > 10. All other perturbation methods employed had at least 265 points, or over 6%, not adhering to the HIPAA standard. Conclusions: Using the set of published perturbation methods applied in this analysis, HIPAA- compliant de-identification was not compatible with maintaining key spatial patterns as measured by our chosen summary statistics. Further research should investigate alternate methods to balancing tradeoffs between spatial data privacy and preservation of key patterns in public health data that are of scientific and medical importance.

scholarly journals Measuring the impact of spatial perturbations on the relationship between data privacy and validity of descriptive statistics

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Kelly Broen ◽  
Rob Trangucci ◽  
Jon Zelner
Keyword(s):  
Spatial Statistics ◽  
Spatial Patterns ◽  
Spatial Data ◽  
Data Privacy ◽  
Perturbation Methods ◽  
Descriptive Statistics ◽  
Moran’S I ◽  
Mortality Data ◽  
Moran's I ◽  
The Impact

Abstract Background Like many scientific fields, epidemiology is addressing issues of research reproducibility. Spatial epidemiology, which often uses the inherently identifiable variable of participant address, must balance reproducibility with participant privacy. In this study, we assess the impact of several different data perturbation methods on key spatial statistics and patient privacy. Methods We analyzed the impact of perturbation on spatial patterns in the full set of address-level mortality data from Lawrence, MA during the period from 1911 to 1913. The original death locations were perturbed using seven different published approaches to stochastic and deterministic spatial data anonymization. Key spatial descriptive statistics were calculated for each perturbation, including changes in spatial pattern center, Global Moran’s I, Local Moran’s I, distance to the k-th nearest neighbors, and the L-function (a normalized form of Ripley’s K). A spatially adapted form of k-anonymity was used to measure the privacy protection conferred by each method, and its compliance with HIPAA and GDPR privacy standards. Results Random perturbation at 50 m, donut masking between 5 and 50 m, and Voronoi masking maintain the validity of descriptive spatial statistics better than other perturbations. Grid center masking with both 100 × 100 and 250 × 250 m cells led to large changes in descriptive spatial statistics. None of the perturbation methods adhered to the HIPAA standard that all points have a k-anonymity > 10. All other perturbation methods employed had at least 265 points, or over 6%, not adhering to the HIPAA standard. Conclusions Using the set of published perturbation methods applied in this analysis, HIPAA and GDPR compliant de-identification was not compatible with maintaining key spatial patterns as measured by our chosen summary statistics. Further research should investigate alternate methods to balancing tradeoffs between spatial data privacy and preservation of key patterns in public health data that are of scientific and medical importance.

scholarly journals Spatial Filtering with EViews and MATLAB

2016 ◽  
Vol 36 (1) ◽  
Author(s):  
Robert Ferstl
Keyword(s):  
Data Analysis ◽  
Spatial Autocorrelation ◽  
Spatial Data ◽  
Spatial Filtering ◽  
Spatial Data Analysis ◽  
Moran’S I ◽  
Spatial Clusters ◽  
Moran's I ◽  
Detection Of Outliers ◽  
Filtering Procedure

This article summarizes the ideas behind a few programs we developed for spatial data analysis in EViews and MATLAB. They allow the user to check for spatial autocorrelation using Moran’s I and provide a spatial filtering procedure based on the Gi statistic by Getis and Ord (1992). We have also implemented graphical tools like Moran Scatterplots for the detection of outliers or local spatial clusters.

scholarly journals Spatial Agglomeration of China’s Forest Products Manufacturing Industry: Measurement, Characteristics and Determinants

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1006
Author(s):  
Zhenhuan Chen ◽  
Hongge Zhu ◽  
Wencheng Zhao ◽  
Menghan Zhao ◽  
Yutong Zhang
Keyword(s):  
Spatial Distribution ◽  
Spatial Data ◽  
Large Scale ◽  
Manufacturing Industry ◽  
Economies Of Scale ◽  
Forest Products ◽  
Spatial Data Analysis ◽  
Forest Protection ◽  
Spatial Agglomeration ◽  
Continuous Space

China’s forest products manufacturing industry is experiencing the dual pressure of forest protection policies and wood scarcity and, therefore, it is of great significance to reveal the spatial agglomeration characteristics and evolution drivers of this industry to enhance its sustainable development. Based on the perspective of large-scale agglomeration in a continuous space, in this study, we used the spatial Gini coefficient and standard deviation ellipse method to investigate the spatial agglomeration degree and location distribution characteristics of China’s forest products manufacturing industry, and we used exploratory spatial data analysis to investigate its spatial agglomeration pattern. The results show that: (1) From 1988 to 2018, the degree of spatial agglomeration of China’s forest products manufacturing industry was relatively low, and the industry was characterized by a very pronounced imbalance in its spatial distribution. (2) The industry has a very clear core–periphery structure, the spatial distribution exhibits a “northeast-southwest” pattern, and the barycenter of the industrial distribution has tended to move south. (3) The industry mainly has a high–high and low–low spatial agglomeration pattern. The provinces with high–high agglomeration are few and concentrated in the southeast coastal area. (4) The spatial agglomeration and evolution characteristics of China’s forest products manufacturing industry may be simultaneously affected by forest protection policies, sources of raw materials, international trade and the degree of marketization. In the future, China’s forest products manufacturing industry should further increase the level of spatial agglomeration to fully realize the economies of scale.

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 ANALISIS PEMANFAATAN KAWASAN WILAYAH PESISIR MENGGUNAKAN LOCAL INDICATORS OF SPATIAL ASSOCIATION (LISA) (STUDI KASUS : KABUPATEN KULON PROGO)

2017 ◽  
Vol 8 (2) ◽  
pp. 781
Author(s):  
Tirsa Ninia Lina ◽  
Eko Sediyono ◽  
Sri Yulianto Joko Prasetyo
Keyword(s):  
Spatial Association ◽  
Moran’S I ◽  
Moran's I

Kawasan pesisir Kabupaten Kulon Progo terdiri dari empat kecamatan, yaitu kecamatan Galur, Panjatan, Wates, dan Temon. Kawasan pesisir ini rentan terhadap dampak negatif aktifitas manusia seperti penggunaan tanah atau pemanfaatannya yang sering tumpang tindih. Tujuan penelitian ini untuk menganalisis autokorelasi spasial terhadap pemanfaatan kawasan wilayah pesisir di Kabupaten Kulon Progo. Penelitian ini menggunakan salah satu pengujian autokorelasi spasial yaitu Local Indicators of Spatial Association (LISA) dengan indikator Local Moran's I, yang menghasilkan signifikansi secara statistik tinggi (hotspots), signifikansi secara statistik rendah (coldspots), dan pencilan (outlier). Hasil dari penelitian ini menunjukkan bahwa kecamatan yang termasuk kategori hotspots (H-H) diantaranya Temon dengan lima hotspots pada kawasan permukiman perdesaan, pertanian lahan kering, industri, sempadan pantai, dan suaka alam, Panjatan dengan tiga hotspots pada kawasan permukiman perkotaan, perdagangan, dan sempadan sungai, Galur dengan dua hotspots pada kawasan pertanian lahan basah dan perdagangan, dan Wates dengan satu hotspots pada kawasan industri.Kata kunci: kawasan pesisir, Kabupaten Kulon Progo, Local Indicators of Spatial Association, LISA, Local Moran's I.

scholarly journals Did the COVID-19 Pandemic Crisis Affect Housing Prices Evenly in the U.S.?

2021 ◽  
Vol 13 (21) ◽  
pp. 12277
Author(s):  
Xinba Li ◽  
Chuanrong Zhang
Keyword(s):  
Real Estate ◽  
Spatial Patterns ◽  
Price Change ◽  
Housing Prices ◽  
Housing Price ◽  
Real Estate Market ◽  
Moran’S I ◽  
Moran's I ◽  
Price Changes ◽  
The U.S

While it is well-known that housing prices generally increased in the United States (U.S.) during the COVID-19 pandemic crisis, to the best of our knowledge, there has been no research conducted to understand the spatial patterns and heterogeneity of housing price changes in the U.S. real estate market during the crisis. There has been less attention on the consequences of this pandemic, in terms of the spatial distribution of housing price changes in the U.S. The objective of this study was to explore the spatial patterns and heterogeneous distribution of housing price change rates across different areas of the U.S. real estate market during the COVID-19 pandemic. We calculated the global Moran’s I, Anselin’s local Moran’s I, and Getis-Ord’s statistics of the housing price change rates in 2856 U.S. counties. The following two major findings were obtained: (1) The influence of the COVID-19 pandemic crisis on housing price change varied across space in the U.S. The patterns not only differed from metropolitan areas to rural areas, but also varied from one metropolitan area to another. (2) It seems that COVID-19 made Americans more cautious about buying property in densely populated urban downtowns that had higher levels of virus infection; therefore, it was found that during the COVID-19 pandemic year of 2020–2021, the housing price hot spots were typically located in more affordable suburbs, smaller cities, and areas away from high-cost, high-density urban downtowns. This study may be helpful for understanding the relationship between the COVID-19 pandemic and the real estate market, as well as human behaviors in response to the pandemic.

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.

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