Spatial autocorrelation for massive spatial data: verification of efficiency and statistical power asymptotics

2019 ◽  
Vol 21 (2) ◽  
pp. 237-269 ◽  
Author(s):  
Qing Luo ◽  
Daniel A. Griffith ◽  
Huayi Wu
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Data ◽  
Statistical Power ◽  
Data Verification ◽  
Massive Spatial Data

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 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.

Parallel Queries of Cluster-Based k Nearest Neighbor in MapReduce

2016 ◽  
pp. 163-182
Author(s):  
Wei Yan
Keyword(s):  
Spatial Data ◽  
Spatial Databases ◽  
Nearest Neighbor ◽  
Programming Model ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
Data Intensive ◽  
Parallel Queries ◽  
Massive Spatial Data ◽  
Nearest Neighbor Queries

Parallel queries of k Nearest Neighbor for massive spatial data are an important issue. The k nearest neighbor queries (kNN queries), designed to find k nearest neighbors from a dataset S for every point in another dataset R, is a useful tool widely adopted by many applications including knowledge discovery, data mining, and spatial databases. In cloud computing environments, MapReduce programming model is a well-accepted framework for data-intensive application over clusters of computers. This chapter proposes a parallel method of kNN queries based on clusters in MapReduce programming model. Firstly, this chapter proposes a partitioning method of spatial data using Voronoi diagram. Then, this chapter clusters the data point after partition using k-means method. Furthermore, this chapter proposes an efficient algorithm for processing kNN queries based on k-means clusters using MapReduce programming model. Finally, extensive experiments evaluate the efficiency of the proposed approach.

Spatial Autocorrelation and Association Measures

2011 ◽  
pp. 1502-1508
Author(s):  
J. Negreiros ◽  
M. Painho ◽  
I. Lopes ◽  
A.C. Costa
Keyword(s):  
Spatial Analysis ◽  
Spatial Autocorrelation ◽  
Spatial Data ◽  
Regional Analysis ◽  
Geographical Information ◽  
Association Measures ◽  
Share Data ◽  
Definition Of ◽  
Graphic Database

Several classical statements relating to the definition of GIS can be found in specialized literature such as the GIS International Journal, expressing the idea that spatial analysis can somehow be useful. GIS is successful not only because it integrates data, but it also enables us to share data in different departments or segments of our organizations. I like this notion of putting the world’s pieces back together again (ArcNews, 2000). “GIS is simultaneously the telescope, the microscope, the computer and the Xerox machine of regional analysis and the synthesis of spatial data” (Abler, 1988). “GIS is a system of hardware, software and liveware implemented with the aim of storing, processing, visualizing and analyzing data of a spatial nature. Other definitions are also possible” (Painho, 1999). “GIS is a tool for revealing what is otherwise invisible in geographical information” (Longley, Goodchild, Maguire, & Rhind, 2001). Certainly, GIS is not a graphic database.

Research on Multiple Spatial Data Gathering Mechanism Based on Geo-Information Browser

2013 ◽  
Vol 765-767 ◽  
pp. 1287-1290
Author(s):  
Ken Chen ◽  
Fang Wang ◽  
Fang Miao ◽  
Fu Chao Cheng
Keyword(s):  
Spatial Data ◽  
Data Exchange ◽  
Data Gathering ◽  
Basic Research ◽  
Information Gathering ◽  
Digital Earth ◽  
Organization And Management ◽  
And Performance ◽  
S Model ◽  
Massive Spatial Data

The spatial data presented several characteristics of mass, multiple, isomerism and multiple tenses, its organization and management mechanism is an important direction of research for Digital Earth. The management of grave emergency with regards to a series of spatial and non-spatial data concerning gathering and handling, having put a higher demand forward the ability of information gathering mechanism on client. The current existing client access mechanism such as C/S model lacks of unified data exchange standards, similarly, B/S model cannot handle the spatial data effectively. It is also difficulty to display for complex and massive spatial data in visualized and real-time. That efficiency depends entirely on the network environment and performance of storage equipment. In order to realize the massive spatial data unified dispatching and efficient sharing based on the principle of Information-gathering and Service-polymerization. We put forward a concept of Spatial-data-cloud which based on G/S model, supported by HGML as the standard and criterion of spatial data exchange, presentation, organization, storage and management. It could also be set up a new work mechanism which use Geo-information browser polymerization multiple and massive complex spatial and non-spatial data. This will provide us a lightweight client called Geo-information browser with which is by the principle Information-gathering and Service-polymerization. It provides emergency management for technical supports such as intelligent decision support, comprehensive research and judgment, and rapid disposal etc. It is the development of basic research of a novel model of Digital Earth.

Research on the Metadata Storage Mode and Efficiency of Distributed File System Based on HGML

2010 ◽  
Vol 40-41 ◽  
pp. 221-227 ◽  
Author(s):  
Fang Miao ◽  
Fu Chao Cheng ◽  
Wen Hui Yang ◽  
Li Tan
Keyword(s):  
Spatial Data ◽  
File System ◽  
Distributed File System ◽  
Entire System ◽  
The Core ◽  
Different Characteristics ◽  
Massive Spatial Data ◽  
Access Efficiency

In the G / S mode, in order to meet the storage demands of massive spatial data, the requirements of the distributed file system (DFS) on back-end servers are extremely high. As one of the core tasks of DFS, the metadata storage is the necessary premise which ensures the reliability and efficiency of the entire system. This paper introduces a metadata storage mode based on HGML, and then designs and implements two solutions, which are scattered storage and integrated storage. According to the different characteristics of the two solutions, access efficiency of the metadata has been tested respectively. The result shows that the new metadata storage mode can basically satisfy the storage demands of massive spatial data.

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