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===Introduction to Spatial Point Patterns=== | ===Introduction to Spatial Point Patterns=== | ||
* What is Spatial Point Patterns Analysis? | * What is Spatial Point Patterns Analysis? | ||
Revision as of 15:15, 3 January 2018
IS415 GeoSpatial Analytics for Business Intelligence
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Spatial Point Patterns Analysis and Applications
Contents
Lesson Content
Introduction to Spatial Point Patterns
- What is Spatial Point Patterns Analysis?
- Analysing spatial point patterns
- Modelling spatial relationships
- Analysing spatio-temporal data
- Geographic Knowledge Discovery
Density-based Point Patterns Measures
- Quadrat analysis
- Kernel density estimation
Distance-based Point Pattern Measures
- Nearest neighbour distance
- G function
- F function
- K function
- L function
Hands-on Session
Hands-on Exercise Week 8: Visualising and Analysing Spatial Point Patterns
Weekly Readings
Point Pattern Analysis [1]
Spatial point pattern analysis and its application in geographical epidemiology [2]
Spatial Point Pattern Analysis software
R [3]
R Packages
spatstat
- Homepage [4]
- Analysing spatial point patterns in 'R' [5]
aspace [6]
maptools [7]
sp [8]
shapefiles [9]
Others
SEXTANTE for QGIS [10]and QGIS-SEXTANTE cookbook [11]
SAGA [12]
GRASS [13] and QGIS GRASS Cookbook [14]
SaTScan [15]
References
Methods
Several Fundamentals in Implementing Spatial Statistics in GIS [16]
Open source, spatial analysis, and activity-travel behaviour research: capabilities of the aspace package [17]
Kernel Density Estimation [18]
CrimeStat III-Part II: Spatial Description [19]
CrimeStat III-Part III: Spatial Modeling[20]
Applications
Mapping Crime: Understanding Hot Spots [21]
Spatial Analysis for Identifying Concentrations of Urban Damage [22]
A Tale of Two Cities: Density Analysis of CBD on Two Midsize Urban Areas in Northeastern Italy[23]
Crime Mapping and Spatial Analysis in National Forests [24]
Street centrality vs. commerce and service locations in cities: a Kernel Density Correlation case study in Bologna, Italy [25]
Spatial Point Pattern Analysis for Targeting Prospective New Customers: Bringing GIS Functionality into Direct Marketing [26]
Clusters of firms in an inhomogeneous space: The high-tech industries in Milan [27]
Methods for visualizing the explosive remnants of war [28]
