1. Lab Practical 2 on
Spatial Pattern Analysis
GIS Analytic (GEOM2152)
Student name: Nguyen Van Tuan
Student code: S3859353
Due date: 28/03/2023
2. ACARA_SML Catholic PS Government PS
Persons-weighted Code: 45727 Code: 44698
Unweighted Code: 45653 Code: 44698
X,Y Coordinates Catholic PS_X Catholic PS_Y Government PS_X Government PS_Y
Persons-weighted 324620.633327 5810755.928917 330008.493621 5807658.393752
Unweighted 326631.026772 5810075.549423 331710.864556 5808067.218181
X,Y Coordinates Catholic PS_X Catholic PS_Y Government PS_X Government PS_Y
Persons-weighted 323540.563499 5813740.689625 330784.069619 5809256.859876
Unweighted 326213.037153 5812541.566163 331834.701065 5809728.25158
Task 1: Find the Central Features
Task 2: Find the Mean Centres
Task 3: Find the Median Centres
3. Standard Distance (m) Catholic PS Government PS
Persons-weighted 24396.368602 25519.989586
Unweighted 23005.642022 25542.254502
X,Y Standard
Distances and
Rotations
Catholic
PS_X
Catholic
PS_Y
Catholic
PS_R
Government
PS_X
Government
PS_Y
Governmen
t PS_R
Persons-weighted 324620.633327 5810755.928917 129.168157 330008.493621 5807658.393752 135.775744
Unweighted 326631.026772 5810075.549423 131.874221 331710.864556 5808067.218181 134.45306
Average Nearest
Neighbour
Analysis
Observed
Mean
Distance
Expected
Mean
Distance
Nearest
Neighbour
Ratio
z
score
p
value
Study Area (m2)
Catholic PS 2481.661931 3033.027167 0.818213 -5.387653 0.000000 8831283646.05385
Government PS 1546.849808 1929.543503 0.801666 -9.239638 0.000000 8831283646.05385
Task 4: Find the Standard Distances
Task 5: Find the Directional Distributions
Task 6: Perform the Average Nearest Neighbour analyses
4. Hot Spot Analysis
(Getis-Ord Gi*)
Average
Distance
Maximum
Distance
Beginning
Distance
Distance
Increment
First Peak
Distance
First Peak z-
score
Catholic PS 2481.661931 17283.513327 17300 2500 19800 12.518666
Government PS 1546.849808 9313.645697 9300 1500 19800 47.349606
Task 7: Perform Hot Spot Analysis
5. Outline some typical applications of the following spatial statistical measures / tools:
▪ Central features: find place to build a school, calculate the central feature for a block group feature class, weighted by population, to
identify which part of town is most accessible and make that census block a top candidate
▪ Mean centres: 1) Compare the mean centre for burglaries shifts when evaluating daytime versus nighttime incidents to help police
departments better allocate resources. 2) Calculate the mean centre of elk observations within a park over several years to see where elk
congregate in summer and winter to provide better information to park visitors.
▪ Median centres: Used to measure the central tendency that is robust to spatial outliers. For example: to find a suitable location for a
hospital that needs to be centrally located. The Median Centre will gravitate towards an area with the most features.
▪ Standard distances: 1) To compare the same type of feature over different time periods—for example, a crime analyst could compare
daytime and nighttime burglaries to see if burglaries are more dispersed or more compact during the day than at night. 2)To measure the
distribution of emergency calls over several months for each responding fire station in a region and compare them to see which stations
respond over a wider area.
▪ Standard deviational ellipses: 1) Mapping the distributional trend for a set of crimes might identify a relationship to particular physical
features (a string of bars or restaurants, a particular boulevard, and so on). 2) Mapping groundwater well samples for a particular
contaminant might indicate how the toxin is spreading and, consequently, may be useful in deploying mitigation strategies
▪ Spatial autocorrelation measures like Moran’s I: used to identify an appropriate neighborhood distance for a variety of spatial analysis
methods by finding the distance where spatial autocorrelation is strongest such as 1) Measure broad trends in ethnic or racial segregation
over time—is segregation increasing or decreasing? 2) Summarize the diffusion of an idea, disease, or trend over space and time—is the
idea, disease, or trend remaining isolated and concentrated, or spreading and becoming more diffuse?
▪ Incremental spatial autocorrelation analysis: used to determine at what distance spatial autocorrelation is the most significant (meaning
statistical significance).
▪ Hot spot analysis, Getis-Ord Gi* or Local Moran’s I: Hot spot is used for identification of clustering of spatial phenomena. Such as 1)
Where are kitchen fires a larger than expected proportion of all residential fires? 2) Where should the evacuation sites be located? 3)
Where/When do peak intensities occur? 4) Which locations and at during what time periods should we allocate more of our resources?
