scholarly journals Application of modified Spatial K’luster Analysis by Tree Edge Removal Method (SKATER) on the level of Crime data in Way Kanan district, Lampung

CAUCHY ◽  
2017 ◽  
Vol 4 (4) ◽  
pp. 155
Author(s):  
Kadek Yama Rinaldi
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Patterns ◽  
Crime Rate ◽  
Minimum Spanning Tree ◽  
Spatial Clustering ◽  
Theory Approach ◽  
Tree Edge ◽  
Edge Removal ◽  
Specific Object ◽  
Relationship Of

<p>Modification of method Spatial K'luster Analysis by Tree Edge Removal (SKATER) is one of the regionalization method for clustering based on the location by spatial autocorrelation and spatial patterns. This method uses graph theory approach to identify the homogeneous location is the minimum spanning tree. In addition to clustering objects based on similarity characteristics, in everyday life, often found that there are significant spatial clustering that affect specific object. This study was conducted to determine the relationship of the crime rate between districts in Way Kanan, Lampung. Based on these results, the characteristics of the crime rate in terms of spoliation, robbery and gambling have spatial autocorrelation and spatial patterns. Further applied modifications of SKATER. Generate 4 cluster (k) graded of the 14 districts. on average k<sub>1 </sub>(17.67% )  k<sub>2</sub> (10.09%)   k<sub>3</sub> (7.80%)  k<sub>4</sub> (4.28%).</p>

scholarly journals The Spatial Patterns of Service Facilities Based on Internet Big Data: A Case Study on Chengdu

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Hao Li ◽  
Jianshu Duan ◽  
Yidan Wu ◽  
Sizhuo Gao ◽  
Ting Li
Keyword(s):  
Spatial Distribution ◽  
Spatial Autocorrelation ◽  
Spatial Patterns ◽  
Spatial Data ◽  
Spatial Clustering ◽  
Spatial Data Mining ◽  
Sustainable Urban Development ◽  
Practical Significance ◽  
Ring Road ◽  
Multiple Clusters

In the context of the mid-late development of China’s urbanization, promoting sustainable urban development and giving full play to urban potential have become a social focus, which is of enormous practical significance for the study of urban spatial pattern. Based on such Internet data as a map’s Point of Interest (POI), this paper studies the spatial distribution pattern and clustering characteristics of POIs of four categories of service facilities in Chengdu of Sichuan Province, including catering, shopping, transportation, scientific, educational, and cultural services, by means of spatial data mining technologies such as dimensional autocorrelation analysis and DBSCAN clustering. Global spatial autocorrelation is used to study the correlation between an index of a certain element and itself (univariate) or another index of an adjacent element (bivariate); partial spatial autocorrelation is used to identify characteristics of spatial clustering or spatial anomaly distribution of geographical elements. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is able to detect clusters of any shape without prior knowledge. The final step is to carry out quantitative analysis and reveal the distribution characteristics and coupling effects of spatial patterns. According to the results, (1) the spatial distribution of POIs of all service facilities is significantly polarized, as they are concentrated in the old city, and the trend of suburbanization is indistinctive, showing three characteristics, namely, central driving, traffic accessibility, and dependence on population activity; (2) the spatial distribution of POIs of the four categories of service facilities is featured by the pattern of “one center, multiple clusters,” where “one center” mainly covers the area within the first ring road and partial region between the first ring road and the third ring road, while “multiple clusters” are mainly distributed in the well-developed areas in the second circle of Chengdu, such as Wenjiang District and Shuangliu District; and (3) there is a significant correlation between any two categories of POIs. Highly mixed multifunctional areas are mainly distributed in the urban center, while service industry is less aggregated in urban fringe areas, and most of them are single-functional or dual-functional regions.

scholarly journals Clustering of Maxima: Spatial Dependencies among Heavy Rainfall in France

2013 ◽  
Vol 26 (20) ◽  
pp. 7929-7937 ◽  
Author(s):  
Elsa Bernard ◽  
Philippe Naveau ◽  
Mathieu Vrac ◽  
Olivier Mestre
Keyword(s):  
Spatial Patterns ◽  
Extreme Value Theory ◽  
Heavy Rainfall ◽  
Spatial Clustering ◽  
Relevant Information ◽  
Value Theory ◽  
Research Field ◽  
Multivariate Extreme Value Theory ◽  
Spatial Dependencies ◽  
The Mean

Abstract One of the main objectives of statistical climatology is to extract relevant information hidden in complex spatial–temporal climatological datasets. To identify spatial patterns, most well-known statistical techniques are based on the concept of intra- and intercluster variances (like the k-means algorithm or EOFs). As analyzing quantitative extremes like heavy rainfall has become more and more prevalent for climatologists and hydrologists during these last decades, finding spatial patterns with methods based on deviations from the mean (i.e., variances) may not be the most appropriate strategy in this context of studying such extremes. For practitioners, simple and fast clustering tools tailored for extremes have been lacking. A possible avenue to bridging this methodological gap resides in taking advantage of multivariate extreme value theory, a well-developed research field in probability, and to adapt it to the context of spatial clustering. In this paper, a novel algorithm based on this plan is proposed and studied. The approach is compared and discussed with respect to the classical k-means algorithm throughout the analysis of weekly maxima of hourly precipitation recorded in France (fall season, 92 stations, 1993–2011).

Multi-Radius Density Clustering Algorithm Based on Outlier Factor

2014 ◽  
Vol 472 ◽  
pp. 427-431
Author(s):  
Zong Lin Ye ◽  
Hui Cao ◽  
Li Xin Jia ◽  
Yan Bin Zhang ◽  
Gang Quan Si
Keyword(s):  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Similar Process ◽  
The Core ◽  
Dbscan Algorithm ◽  
Proposed Model ◽  
Density Clustering ◽  
Relationship Of ◽  
Core Points ◽  
The Relationship

This paper proposes a novel multi-radius density clustering algorithm based on outlier factor. The algorithm first calculates the density-similar-neighbor-based outlier factor (DSNOF) for each point in the dataset according to the relationship of the density of the point and its neighbors, and then treats the point whose DSNOF is smaller than 1 as a core point. Second, the core points are used for clustering by the similar process of the density based spatial clustering application with noise (DBSCAN) to get some sub-clusters. Third, the proposed algorithm merges the obtained sub-clusters into some clusters. Finally, the points whose DSNOF are larger than 1 are assigned into these clusters. Experiments are performed on some real datasets of the UCI Machine Learning Repository and the experiments results verify that the effectiveness of the proposed model is higher than the DBSCAN algorithm and k-means algorithm and would not be affected by the parameter greatly.

scholarly journals Variability of spatial patterns of autocorrelation and heterogeneity embedded in precipitation

2018 ◽  
Vol 50 (1) ◽  
pp. 215-230
Author(s):  
Dedi Liu ◽  
Qin Zhao ◽  
Shenglian Guo ◽  
Pan Liu ◽  
Lihua Xiong ◽  
...  
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Autocorrelation ◽  
Spatial Patterns ◽  
Spatial Interpolation ◽  
Kendall Test ◽  
Test Methods ◽  
Distributed Hydrological Model ◽  
Interpolation Methods ◽  
Increasing Trend ◽  
Spatial Interpolation Methods

Abstract Spatial interpolation of precipitation data is an essential input for hydrological modelling. At present, the most frequently used spatial interpolation methods for precipitation are based on the assumption of stationary in spatial autocorrelation and spatial heterogeneity. As climate change is altering the precipitation, stationary in spatial autocorrelation and spatial heterogeneity should be first analysed before spatial interpolation methods are applied. This study aims to propose a framework to understand the spatial patterns of autocorrelation and heterogeneity embedded in precipitation using Moran's I, Getis–Ord test, and semivariogram. Variations in autocorrelation and heterogeneity are analysed by the Mann–Kendall test. The indexes and test methods are applied to the 7-day precipitation series which are corresponding to the annual maximum 7-day flood volume (P-AM7FV) upstream of the Changjiang river basin. The spatial autocorrelation of the P-AM7FV showed a statistically significant increasing trend over the whole study area. Spatial interpolation schemes for precipitation may lead to better estimation and lower error for the spatial distribution of the areal precipitation. However, owing to the changing summer monsoons, random variation in the spatial heterogeneity analysis shows a significant increasing trend, which reduces the reliability of the distributed hydrological model with the input of local or microscales.

scholarly journals Focusing neighborhood context and self-rated health in the Pró-Saúde Study

2018 ◽  
Vol 34 (5) ◽  
Author(s):  
Simone M. Santos ◽  
Guilherme Loureiro Werneck ◽  
Eduardo Faerstein ◽  
Claudia S. Lopes ◽  
Dóra Chor
Keyword(s):  
Neighborhood Context ◽  
Individual Factors ◽  
University Employees ◽  
Self Rated Health ◽  
Tree Edge ◽  
Edge Removal ◽  
Health Related ◽  
The Relationship ◽  
The City ◽  
Level 2

The influence of neighborhood characteristics on self-rated health has been little studied. A multilevel approach using hierarchical models was applied to analyze the relationship between the socioeconomic characteristics in 621 neighborhoods (level 2) in the city of Rio de Janeiro, Brazil, and the self-rated health of 3,054 university employees (level 1) from the baseline of the Pró-Saúde Study. Neighborhoods were created using the SKATER algorithm (Spatial ‘K’luster Analysis by Tree Edge Removal) to cluster census tracts according to four indicators and a minimum population of 5,000 people. After adjustment for individual factors (per capita income, schooling, age, sex, ethnicity, health-related behavior and chronic diseases), low level of neighborhood income and higher numbers of members per household were significantly associated with poor self-rated health. Participants living in medium income-level neighborhoods were 34% more likely to self-rate their health as being poor. Those living in areas with a higher density of members per household were 50% more likely to present poor self-rated health. Neighborhood context influences self-rated health, beyond the effect of individual factors. Worsening neighborhood socioeconomic conditions affect health adversely, which in turn increasing the chance of poor self-rated health.

scholarly journals Exploring the Evolution of Drought Characteristics in Balochistan, Pakistan

2020 ◽  
Vol 10 (3) ◽  
pp. 913 ◽  
Author(s):  
Shoaib Jamro ◽  
Falak Naz Channa ◽  
Ghulam Hussain Dars ◽  
Kamran Ansari ◽  
Nir Y. Krakauer
Keyword(s):  
Climate Change ◽  
Drought Index ◽  
Risk Index ◽  
Trend Test ◽  
Standardized Precipitation Evapotranspiration Index ◽  
Long Duration ◽  
Tree Edge ◽  
Edge Removal ◽  
The 1960S

In the wake of a rapidly changing climate, droughts have intensified, in both duration and severity, across the globe. The Germanwatch long-term Climate Risk Index ranks Pakistan among the top 10 countries most affected by the adverse effects of climate change. Within Pakistan, the province of Balochistan is among the most vulnerable regions due to recurring prolonged droughts, erratic precipitation patterns, and dependence on agriculture and livestock for survival. This study aims to explore how the characteristics of droughts have evolved in the region from 1902–2015 using 3-month and 12-month timescales of a popular drought index, the Standardized Precipitation Evapotranspiration Index (SPEI). The region was divided into six zones using Spatial “K”luster Analysis using Tree Edge Removal (SKATER) method, and run theory was applied to characterize droughts in terms of duration, severity, intensity, and peak. The results of the non-parametric Mann–Kendall trend test applied to SPEI indicate prevailing significant negative trends (dryer conditions) in all the zones. Balochistan experienced its most severe droughts in the 1960s and around 2000. The effects of climate change are also evident in the fact that all the long duration droughts occurred after 1960. Moreover, the number of droughts identified by 3-month SPEI showed a significant increase after 1960 for all six zones. The same trend was found in the 12-month SPEI but for only three zones.

The impact of spatial heterogeneity on selection: a case study on Pinus pinaster breeding seedling orchards

2008 ◽  
Vol 38 (1) ◽  
pp. 114-124 ◽  
Author(s):  
Rafael Zas
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Patterns ◽  
Genetic Gain ◽  
Pinus Pinaster ◽  
Spatial Models ◽  
Selection Methods ◽  
Family Selection ◽  
The Impact

Although failure to account for spatial autocorrelation has been dramatic in some forest progeny trials, little attention has been paid to how this issue may affect selections within the trials. The effects of spatial autocorrelation of height growth on the estimation of genetic gain and on the spatial distribution of the selected trees were studied in four Pinus pinaster Ait. progeny trials that were rogued using different selection methods and intensities. When selections are based on unadjusted original values, selected trees tend to be located in the best microsites and are unlikely to be the most genetically superior. This resulted in a loss of genetic gain that varied between 10% and 20% and sometimes exceeded 30%. Differences in the loss of gain among different selection methods and intensities were minor and followed no clear pattern. Selecting on the basis of a conventional model resulted in spatial patterns of the retained trees that were clearly aggregated in all cases. However, selections based on spatially adjusted data resulted in random spatial patterns, except with family selection because of the use of multiple-tree plots. Because clumping of the retained trees may seriously affect the quantity and quality of the seed crop, breeders are strongly encouraged to use appropriate spatial models for roguing breeding seedling orchards.

Spatial Epidemiology Of Chronic Lymphocytic Leukemia and Small Lymphocytic Lymphoma In The State Of Georgia

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 1683-1683
Author(s):  
Catherine Bulka ◽  
Loretta J. Nastoupil ◽  
Jeffrey Switchenko ◽  
Kevin Ward ◽  
Rana Bayakly ◽  
...  
Keyword(s):  
Chronic Lymphocytic Leukemia ◽  
Spatial Autocorrelation ◽  
Spatial Patterns ◽  
Cancer Registry ◽  
Census Tract ◽  
Hot Spots ◽  
Research Funding ◽  
The State ◽  
Lymphocytic Leukemia ◽  
Cold Spots

Abstract Background Exploring spatial patterns of disease incidence allows for the identification of areas of elevated or decreased risk. For chronic lymphocytic leukemia and small lymphocytic leukemia (CLL/SLL), which have poorly understood etiologies, identifying spatial patterns through cluster analysis may provide insight about potential environmental and socio-demographic risk factors. Methods In order to investigate the spatial patterns of CLL/SLL incidence among adults (≥ 20 years), we linked cancer incidence data for the period 1999-2008 from the Georgia Comprehensive Cancer Registry (a CDC-supported a statewide population-based cancer registry collecting all cancer cases diagnosed among Georgia residents since 1995) with population data from the 2000 U.S. Census. CLL/SLL cases were aggregated to the census tract level. CLL/SLL incidence in Georgia was standardized indirectly by age, sex, and race to national rates obtained from SEER*Stat software. Choropleth maps were created to depict the ratio of observed to expected incidence (standardized incidence ratios [SIR]) by census tract using ArcGIS. Spatial Empirical Bayes smoothing was performed on the SIR values using GeoDa 1.01. To assess spatial correlation of SIRs, we conducted global and local cluster analyses by calculating global Moran’s I and local Moran’s I (also known as Local Indicators of Spatial Autocorrelation [LISA]) values. Cluster analyses were repeated, stratifying by age (20-59 years, 60+ years), sex, and race (Caucasian and African American). P-values less than 0.01 were considered statistically significant. Results 765 incident CLL/SLL cases occurred among adults residing in Georgia between 1999 and 2008 (Table 1). There was a positive spatial autocorrelation for cases of CLL/SLL age 60 and older indicating these cases were geographically clustered (p = 0.0010) (Table 2). The LISA cluster map of the smoothed standardized incidence ratios shows the locations of “hot-spots” (high-high clusters) and “cold-spots” (low-low clusters) with clustering of high smoothed SIRs was found in the metro-Atlanta area, Albany, Macon, and outside of Augusta while cold-spots were mostly in the southern region of the state. Conclusions Despite the low number of cases of CLL/SLL in Georgia during the 10-year period studied, we found evidence of spatial clustering among adults 60 years and above. Hot-spots of smoothed SIRs were located in the metro-Atlanta area, Albany, Macon, and near Augusta, but these varied when stratified by age, sex, and race, suggesting confounding or effect modification that warrants further investigation. Disclosures: Flowers: Spectrum: Research Funding; Celgene: Consultancy, Research Funding; Millennium/Takeda: Consultancy, Research Funding; Genentech BioOncology: Consultancy; Sanofi: Research Funding; Janssen: Research Funding; Abbott: Research Funding.

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