GIS 2 - Spatial Analysis

GIS 2 - Spatial Analysis

Duration: 20 hours

Teaching Methodology: Hands on

Course Schedule: Schedule

Fees $450

Course Mode: Blended Face-to-face or online via Zoom


Geographic Information Systems (GIS) are computerized systems designed for the storage, retrieval and analysis of geographically referenced data.

GIS uses advanced analytical tools to explore at a scientific level the spatial relationships, patterns, and processes of cultural, biological, demographic, economic, geographic, and physical phenomena




Professionals and students exploring a potential career with GIS.


This course provides a system for GIS users to develop proficiency in various spatial analysis methods, including location analysis; change over time, location, and value comparisons; geographic distribution; pattern analysis; and cluster identification.

Chapter 1 Mapping where things are
1-1 Working with categories
1-2 Controlling which values are displayed
1-3 Limiting values to display

Chapter 2 Mapping the most and least
2-1 Mapping quantities
2-2 Choosing classes
2-3 Creating a map series
2-4 Working with charts

Chapter 3 Mapping density
3-1 Displaying density for analysis
3-2 Creating dot density maps
3-3 Creating a density surface

Chapter 4 Finding what’s inside
4-1 Overlaying datasets for analysis
4-2 Finding features partially inside

Chapter 5 Finding what’s nearby
5-1 Selecting features nearby
5-2 Creating buffer features
5-3 Clipping features
5-4 Buffering values
5-5 Using multiple buffer zones
5-6 Quantifying nearness
5-7 Creating distance surfaces
5-8 Calculating cost along a network
5-9 Calculating nearness along a network

Chapter 6 Mapping change
6-1 Mapping change in location
6-2 Mapping change in location and magnitude
6-3 Mapping percentage change in value

Chapter 7 Measuring geographic distribution
7-1 Calculating centers
7-2 Adding weights to centers
7-3 Calculating standard distance
7-4 Calculating a standard deviational ellipse
7-5 Calculating the linear directional mean

Chapter 8 Analyzing patterns
8-1 Using average nearest neighbor
8-2 Identifying the clustering of values
8-3 Checking for multidistance clustering
8-4 Measuring spatial autocorrelation

Chapter 9 Identifying clusters
9-1 Performing cluster and outlier analysis
9-2 Performing hot spot analysis