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 Delaunay Triangulation-Based Spatial Clustering Technique for Enhanced Adjacent Boundary Detection and Segmentation of LiDAR 3D Point Clouds

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3926 ◽  
Author(s):  
Jongwon Kim ◽  
Jeongho Cho
Keyword(s):  
Delaunay Triangulation ◽  
Spatial Data ◽  
Spatial Clustering ◽  
Synthetic Data ◽  
Point Clouds ◽  
Spatial Data Mining ◽  
New Approach ◽  
Clustering Technique ◽  
3D Point Clouds ◽  
Multiple Clusters

In spatial data with complexity, different clusters can be very contiguous, and the density of each cluster can be arbitrary and uneven. In addition, background noise that does not belong to any clusters in the data, or chain noise that connects multiple clusters may be included. This makes it difficult to separate clusters in contact with adjacent clusters, so a new approach is required to solve the nonlinear shape, irregular density, and touching problems of adjacent clusters that are common in complex spatial data clustering, as well as to improve robustness against various types of noise in spatial clusters. Accordingly, we proposed an efficient graph-based spatial clustering technique that employs Delaunay triangulation and the mechanism of DBSCAN (density-based spatial clustering of applications with noise). In the performance evaluation using simulated synthetic data as well as real 3D point clouds, the proposed method maintained better clustering and separability of neighboring clusters compared to other clustering techniques, and is expected to be of practical use in the field of spatial data mining.

Mapping the Spatial Distribution Patterns of Personal Time Spent Based on Trip Purpose

2018 ◽  
Vol 9 (2) ◽  
pp. 1-13 ◽  
Author(s):  
Ko Ko Lwin ◽  
Yoshihide Sekimoto
Keyword(s):  
Spatial Distribution ◽  
Survey Data ◽  
Spatial Data ◽  
Distribution Patterns ◽  
Spatial Data Mining ◽  
Urban Transport ◽  
Social Characteristics ◽  
Public Facility ◽  
Spatial Distribution Patterns ◽  
Trip Purpose

Understanding the spatial distribution patterns of the time spent by people based on their trip purpose and other social characteristics is important for sustainable urban transport planning, public facility management, socio-economic development, and other types of policy planning. Although personal trip survey data includes travel behavior and other social characteristics, many are lacking in detail regarding the spatial distribution patterns of individual movements based on time spent, typically due to privacy issues and difficulties in converting non-spatial survey data into a spatial format. In this article, geospatially-enabled personal trip data (Geospatial Big Data), converted from traditional paper-based survey data, are subjected to a spatial data mining process in order to examine the detailed spatial distribution patterns of time spent by the public based on various trip purposes and other social characteristics, using the Tokyo metropolitan area as a case study.

scholarly journals Spatial distribution of pedestrian-motor vehicle collisions before and after pedestrian countdown signal installation in Toronto, Canada

2017 ◽  
Vol 25 (2) ◽  
pp. 110-115 ◽  
Author(s):  
Linda Rothman ◽  
Marie-Soleil Cloutier ◽  
Alison K Macpherson ◽  
Sarah A Richmond ◽  
Andrew William Howard
Keyword(s):  
Spatial Distribution ◽  
Spatial Patterns ◽  
Spatial Data ◽  
Motor Vehicle ◽  
Spatial Data Analysis ◽  
Motor Vehicle Collisions ◽  
Vehicle Collisions ◽  
Point Patterns ◽  
Kernel Density Estimates ◽  
Before And After

BackgroundPedestrian countdown signals (PCS) have been installed in many cities over the last 15 years. Few studies have evaluated the effectiveness of PCS on pedestrian motor vehicle collisions (PMVC). This exploratory study compared the spatial patterns of collisions pre and post PCS installation at PCS intersections and intersections or roadways without PCS in Toronto, and examined differences by age.MethodsPCS were installed at the majority of Toronto intersections from 2007 to 2009. Spatial patterns were compared between 4 years of police-reported PMVC prior to PCS installation to 4 years post installation at 1864 intersections. The spatial distribution of PMVC was estimated using kernel density estimates and simple point patterns examined changes in spatial patterns overall and stratified by age. Areas of higher or lower point density pre to post installation were identified.ResultsThere were 14 911 PMVC included in the analysis. There was an overall reduction in PMVC post PCS installation at both PCS locations and non-PCS locations, with a greater reduction at non-PCS locations (22% vs 1%). There was an increase in PMVC involving adults (5%) and older adults (9%) at PCS locations after installation, with increased adult PMVC concentrated downtown, and older adult increases occurring throughout the city following no spatial pattern. There was a reduction in children’s PMVC at both PCS and non-PCS locations, with greater reductions at non-PCS locations (35% vs 48%).ConclusionsResults suggest that the effects of PCS on PMVC may vary by age and location, illustrating the usefulness of exploratory spatial data analysis approaches in road safety. The age and location effects need to be understood in order to consistently improve pedestrian mobility and safety using PCS.

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>

Study on Fuzzy Clustering Algorithm of Spatial Data Mining

2013 ◽  
Vol 416-417 ◽  
pp. 1244-1250
Author(s):  
Ting Ting Zhao
Keyword(s):  
Data Mining ◽  
Fuzzy Clustering ◽  
Spatial Data ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Rapid Development ◽  
Spatial Database ◽  
Spatial Data Mining ◽  
Data Set ◽  
Fuzzy Similarity

With rapid development of space information crawl technology, different types of spatial database and data size of spatial database increases continuously. How to extract valuable information from complicated spatial data has become an urgent issue. Spatial data mining provides a new thought for solving the problem. The paper introduces fuzzy clustering into spatial data clustering field, studies the method that fuzzy set theory is applied to spatial data mining, proposes spatial clustering algorithm based on fuzzy similar matrix, fuzzy similarity clustering algorithm. The algorithm not only can solve the disadvantage that fuzzy clustering cant process large data set, but also can give similarity measurement between objects.

scholarly journals Analysis of DNA diversity by spatial autocorrelation.

Genetics ◽  
1995 ◽  
Vol 140 (2) ◽  
pp. 811-819
Author(s):  
G Bertorelle ◽  
G Barbujani
Keyword(s):  
Spatial Distribution ◽  
Genetic Structure ◽  
Spatial Autocorrelation ◽  
Spatial Patterns ◽  
Dna Sequences ◽  
Dna Analysis ◽  
Sequence Data ◽  
Natural Populations ◽  
Evolutionary Processes ◽  
Dna Diversity

Abstract Two statistics are proposed for summarizing spatial patterns of DNA diversity. These autocorrelation indices for DNA analysis, or AIDAs, can be applied to RFLP and sequence data; the resulting set of autocorrelation coefficients, or correlogram, measures whether, and to what extent, individual DNA sequences or haplotypes resemble the haplotypes sampled at arbitrarily chosen spatial distances. Analyses of computer-generated sets of data, and of RFLP data from two natural populations, show that AIDAs allow one to objectively and simply identify basic patterns in the spatial distribution of haplotypes. These statistics, therefore, seem to be a useful tool both to explore the genetic structure of a population and to suggest hypotheses on the evolutionary processes that shaped the observed patterns.

scholarly journals Knowledge Discovery Web Service for Spatial Data Infrastructures

2020 ◽  
Vol 10 (1) ◽  
pp. 12
Author(s):  
Morteza Omidipoor ◽  
Ara Toomanian ◽  
Najmeh Neysani Samany ◽  
Ali Mansourian
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Web Service ◽  
Spatial Data ◽  
Spatial Clustering ◽  
Spatial Data Mining ◽  
Data Mining Techniques ◽  
Data Infrastructures ◽  
Spatial Data Infrastructures ◽  
High Level

The size, volume, variety, and velocity of geospatial data collected by geo-sensors, people, and organizations are increasing rapidly. Spatial Data Infrastructures (SDIs) are ongoing to facilitate the sharing of stored data in a distributed and homogeneous environment. Extracting high-level information and knowledge from such datasets to support decision making undoubtedly requires a relatively sophisticated methodology to achieve the desired results. A variety of spatial data mining techniques have been developed to extract knowledge from spatial data, which work well on centralized systems. However, applying them to distributed data in SDI to extract knowledge has remained a challenge. This paper proposes a creative solution, based on distributed computing and geospatial web service technologies for knowledge extraction in an SDI environment. The proposed approach is called Knowledge Discovery Web Service (KDWS), which can be used as a layer on top of SDIs to provide spatial data users and decision makers with the possibility of extracting knowledge from massive heterogeneous spatial data in SDIs. By proposing and testing a system architecture for KDWS, this study contributes to perform spatial data mining techniques as a service-oriented framework on top of SDIs for knowledge discovery. We implemented and tested spatial clustering, classification, and association rule mining in an interoperable environment. In addition to interface implementation, a prototype web-based system was designed for extracting knowledge from real geodemographic data in the city of Tehran. The proposed solution allows a dynamic, easier, and much faster procedure to extract knowledge from spatial data.

scholarly journals Inequality of rural income distribution in Iran: an exploratory analysis of spatial data

2021 ◽  
Vol 17 (85) ◽  
Author(s):  
Mohammad Aghapour sabbaghi
Keyword(s):  
Spatial Distribution ◽  
Spatial Data ◽  
Rural Areas ◽  
Rural Economy ◽  
Regional Differences ◽  
Spatial Clustering ◽  
Theil Index ◽  
Unequal Distribution ◽  
Rural Income ◽  
Regional Component

Despite regional differences, spatial distribution has not been addressed in studies on rural income in Iran. The main goal of this study is to analyze the spatial pattern of inequality in rural areas of the country. In this research, Moran’s I index, Theil index and Gini coefficient have been used for the period 2005-2015. The results show that both inter-regional and intra-regional components affect the unequal distribution of rural income, but the importance of the intra-regional component is slightly higher. The study of the data obtained from the Moran’s Ι index shows that there is evidence of the spatial clustering phenomenon in rural economy of the country.  

scholarly journals Spatial Autocorrelation Analysis of Multi-Scale Damaged Vegetation in the Wenchuan Earthquake-Affected Area, Southwest China

Forests ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 195
Author(s):  
Jian Li ◽  
Jingwen He ◽  
Ying Liu ◽  
Daojie Wang ◽  
Loretta Rafay ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Linear Regression ◽  
Regression Model ◽  
Spatial Autocorrelation ◽  
Wenchuan Earthquake ◽  
Influencing Factors ◽  
Spatial Patterns ◽  
Linear Regression Model ◽  
Moran's I ◽  
Standard Linear

Major earthquakes can cause serious vegetation destruction in affected areas. However, little is known about the spatial patterns of damaged vegetation and its influencing factors. Elucidating the main influencing factors and finding out the key vegetation type to reflect spatial patterns of damaged vegetation are of great interest in order to improve the assessment of vegetation loss and the prediction of the spatial distribution of damaged vegetation caused by earthquakes. In this study, we used Moran’s I correlograms to study the spatial autocorrelation of damaged vegetation and its potential driving factors in the nine worst-hit Wenchuan earthquake-affected cities and counties. Both dependent and independent variables showed a positive spatial autocorrelation but with great differences at four aggregation levels (625 × 625 m, 1250 × 1250 m, 2500 × 2500 m, and 5000 × 5000 m). Shrubs can represent the characteristics of all damaged vegetation due to the significant linear relationship between their Moran’s I at the four aggregation levels. Clustering of similar high coverage of damaged vegetation occurred in the study area. The residuals of the standard linear regression model also show a significantly positive autocorrelation, indicating that the standard linear regression model cannot explain all the spatial patterns in damaged vegetation. Spatial autoregressive models without spatially autocorrelated residuals had the better goodness-of-fit to deal with damaged vegetation. The aggregation level 8 × 8 is a scale threshold for spatial autocorrelation. There are other environmental factors affecting vegetation destruction. Our study provides useful information for the countermeasures of vegetation protection and conservation, as well as the prediction of the spatial distribution of damaged vegetation, to improve vegetation restoration in earthquake-affected areas.

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