🌏🌍Beyond Mapping Training Series🌏🌍

Spatial or geographic data is everywhere (we will use spatial and geographic data interchangeably). From maps and GPS to social media and sensors, we are constantly generating and consuming spatial data. But how can we make sense of this data and use it to our advantage? That’s where spatial analysis comes in. Spatial analysis is a branch of statistics and geography that deals with the analysis of spatial data.  Spatial analysis can help you understand the spatial distribution, variation, and autocorrelation of your data, and identify spatial clusters, hotspots, outliers, and trends. Spatial analysis can also help you model and interpolate spatial phenomena, and test hypotheses about spatial relationships. Spatial analysis can help you answer questions such as:

• How are the features or phenomena distributed across the study area? Are they evenly or unevenly spaced?

• What is the density of the features or phenomena in different regions? Are there areas of high or low concentration?

• What is the average distance between the features or phenomena? How does it vary across the study area?

• Where are the potential customers or markets for a product or service?

• How are the sales or profits distributed across different regions or locations?

• What are the factors that influence the demand or supply of a product or service in a specific area?

• How can the location or layout of a business affect its performance or efficiency?

• What are the best locations for new facilities, stores, or services?

• How are micro and small enterprises distributed across different regions or sectors? 

• How are customers, competitors, or suppliers clustered or dispersed in a given area?

• Are there any outliers or anomalies in the spatial distribution or pattern? Are there any features or phenomena that are isolated, clustered, or dispersed?

• Is the spatial distribution or pattern random, regular, or clustered? How can we test this statistically?

• What are the factors or variables that influence the spatial distribution or pattern? How can we measure their correlation or association?

• How does the spatial distribution or pattern change over time? Are there any trends or patterns of change?

• How does the spatial distribution or pattern compare with other features or phenomena? Are there any similarities or differences?

• How does the spatial distribution or pattern affect other features or phenomena? Are there any impacts or consequences?

• How can we model or predict the spatial distribution or pattern based on existing data and assumptions?

• What are the spatial patterns of crime, disease, or poverty?

• How does climate change affect the distribution and abundance of species or resources?

If you want to learn how to answer these questions and more using spatial data and GIS software, then you need to enroll in our Spatial Analysis for Beginners course!

What is this course about?

This course is an introduction to spatial analysis, a branch of statistics and geography that deals with the analysis of spatial data. Spatial data are data that have a location or a geographic reference, such as points, lines, polygons, or raster grids.

Spatial analysis in this course is not the same the “usual” term of spatial analysis commonly known and popularized by GIS vendors. Functions such as map overlay and buffering are not considered as spatial analysis. They are spatial data manipulation functions as their results are just data that need to be processed further to be meaningful.

Spatial analysis can help you understand the spatial distribution, variation, and autocorrelation of your data, and identify spatial clusters, hotspots, outliers, and trends. Spatial analysis can also help you model and interpolate spatial phenomena, and test hypotheses about spatial relationships. In this course, you will learn the basic concepts and methods of spatial analysis, and how to apply them using GIS software. You will also learn how to communicate your findings using maps, charts, graphs, and reports.

What will you learn (tentative)?

By the end of this course, you will be able to:

• Define spatial analysis and its objectives

• Describe the characteristics and formats of spatial data

• Identify and access common sources of spatial data

• Use GIS software to display and manipulate spatial data

• Apply cartographic principles and techniques to create effective maps

• Perform basic operations and calculations on spatial data

• Calculate and interpret summary statistics for spatial data

• Visualize the distribution and variation of spatial data using charts and graphs

• Define and measure spatial distribution and spatial heterogeneity

• Apply various methods of modeling spatial distribution

• Interpret the results and uncertainty of spatial distribution models

• Define and measure spatial autocorrelation and spatial dependence

• Apply various methods of identifying spatial clusters and hotspots

• Interpret the results and significance of spatial pattern tests

• Apply spatial analysis methods to real-world datasets using GIS software

• Communicate the findings and implications of spatial analysis using maps, charts, graphs, and reports

What is the course outline (tentative)?

The course is divided into three sessions, each covering a different aspect of spatial analysis:

• Module 1: Introduction to Spatial Analysis

  • What is spatial analysis and why is it important?

  • Types of spatial data and spatial data sources

  • Spatial data visualization and cartography

• Module 2: Spatial Distribution and Exploration Analysis

  • Concepts of spatial distribution and spatial heterogeneity

  • Methods of measuring spatial distribution

  • Methods of modeling spatial distribution

  • Spatial data exploration and descriptive statistics

• Module 3: Spatial Pattern Analysis

  • Concepts of spatial autocorrelation and spatial dependence

  • Methods of measuring spatial autocorrelation

  • Methods of identifying spatial clusters and hotspots

  • Applications of spatial pattern analysis to real-world problems

Who is this course for?

This course is designed for beginners who want to learn the basics of spatial analysis. No prior knowledge or experience in GIS or statistics is required. However, some familiarity with basic concepts such as mean, median, standard deviation, etc. would be helpful. This course is suitable for anyone who works with or is interested in geographic data, such as:

• Business Owners

• Marketers

• Researchers

• Students

• Teachers

• Consultants

• Analysts

• Planners

• Managers

Course Schedule:

Details of the course offering are as follow:

• Date:  18 – 19 September, 2023

• Mode of Offering: Online

• Language: English (if all participants are Malaysian and there is request to deliver the course in Bahasa Malaysia, we will consider the request if agreeable by all participants)

• Course Fee: RM997 RM397 per person (1st 50 person, RM697 thereafter), which includes access to online lectures, exercises, sample datasets, and certificates. Unsponsored Full-time student with proof of active status is eligible to get 50% discount.

Prerequisite:

This course is not about learning basic GIS. We assumed that every participant already has basic GIS knowledge. We also do not provide any software for this course.

The main software for this course is open-source QGIS. However, if most participants have access to ArcGIS, then we will consider using ArcGIS. Otherwise, we stick with QGIS. This course is not about learning software, instead the knowledge of spatial analysis.

To allow prospects with no GIS knowledge to participate in this training course, we will conduct a FREE half-day training course on QGIS few days before the date of the training course.

SPECIAL BONUS:

We are committed to making sure the participants be able to use the skills they learned from the course, we are offering a FREE 3-month group coaching (valued at RM1997). Tentative schedules as follow: 1st month – 2-hour bi-weekly, 2nd & 3rd month – 1-hour bi-weekly. We will also provide Facebook or Telegram Support Group.