Course Description

Business analytics is the process of transforming data and information into insights that will produce useful information for business professionals. A majority of business analytics deals with things like big data.

 Course Objectives

Provide a comprehensive introduction to GIS concepts, tools, and their applications in business analytics, with practical insights for real-world decision-making.
Tools: ArcGIS (primarily), with references to Tableau.

Outline

Day (1): Introduction to GIS and Business Analytics

1.1 What is GIS?

• Definition and Components:
• Geographic Information Systems (GIS) are frameworks for capturing, storing, analyzing, and visualizing spatial data.
• Components include:
• Hardware: Computers for processing.
• Software: Tools like ArcGIS Pro and ArcGIS Online.
• Data: Spatial (e.g., maps) and attribute (e.g., sales figures).
• People: Analysts and decision-makers.
• Methods: Analytical techniques.
• Core Concepts:
• Spatial data represents locations (e.g., coordinates, maps).
• Attribute data provides descriptive information (e.g., sales for a store).
• Georeferencing aligns data to real-world coordinates.
• Examples:
• Mapping store locations to analyze market coverage.
• Visualizing customer density for targeted marketing.
• Why GIS for Business?:
• Enhances decision-making by adding spatial context to data.
• Applications include retail expansion, logistics, and risk assessment.

1.2 GIS in Business Analytics

• Business Use Cases:
• Retail: Site selection based on demographics and competitor proximity.
• Logistics: Optimizing delivery routes.
• Real Estate: Assessing property values based on spatial factors.
• Marketing: Location-based customer targeting.

   Case Study: Retail Chain Expansion:

• Scenario: A coffee chain plans to open new stores.
• GIS Approach: Map population density, income levels, and competitor locations using ArcGIS Pro.
• Outcome: Data-driven site selection improves profitability.
• Benefits of GIS:
• Uncovers hidden spatial patterns (e.g., customer clusters).
• Integrates with business intelligence tools (e.g., Tableau, Power BI).
• Supports predictive analytics for demand forecasting.

1.3 GIS Tools and Software

• Overview of Tools:
• ArcGIS Pro: Industry-standard desktop GIS software, used in this course for analysis and visualization.
• ArcGIS Online: Cloud-based platform for creating and sharing interactive maps and dashboards.
• Tableau: BI tool with spatial visualization capabilities.
• Others: Google Earth Engine (cloud-based), GeoPandas (Python library).
• Why ArcGIS?:
• Robust, widely used in industry, supports vector/raster data, and offers advanced tools.
• Suitable for beginners (user-friendly interface) and intermediates (advanced geoprocessing).
• Setting Expectations:
• Beginners: Learn to navigate ArcGIS Pro and load data.
• Intermediates: Explore advanced ArcGIS Pro tools (e.g., ModelBuilder) and ArcGIS Online dashboards.
• Hands-On Preview:
• Explore ArcGIS Pro interface, load a sample dataset (e.g., city demographics), and create a simple map.

Learning Outcomes:

• Understand GIS components and their relevance to business.
• Recognize key business applications of GIS.
• Familiarize with ArcGIS Pro and ArcGIS Online as primary tools.

 Day (2): GIS Data Types and Sources

2.1 GIS Data Fundamentals

• Data Types:
• Vector Data: Points (e.g., store locations), lines (e.g., roads), polygons (e.g., city boundaries).
• Raster Data: Grid-based (e.g., satellite imagery, elevation maps).
• Comparison: Vector for precise shapes, raster for continuous data.
• Coordinate Systems and Projections:
• Geographic: Latitude/longitude (e.g., WGS84).
• Projected: Flat maps (e.g., UTM for local analysis).
• Importance: Ensures accurate spatial alignment.
• Attribute Data:
• Non-spatial data linked to spatial features (e.g., sales figures for a store).
• Stored in tables, queried for analysis.

 2.2 Data Sources for Business

• Types of Data Sources:
• Open Data: OpenStreetMap (roads, buildings), USGS (elevation), government portals (census data).
• Commercial Data: Esri’s Living Atlas (demographics), Nielsen (market data), TomTom (traffic data).
• APIs: Google Maps API, Foursquare (location-based data).
• Business-Relevant Data:
• Demographics: Population, income, age (e.g., for market analysis).
• Infrastructure: Roads, transit hubs (e.g., for logistics).
• Competitors: Locations and market share (e.g., for retail strategy).
• Challenges:
• Data quality: Accuracy, completeness, timeliness.
• Data formats: Shapefiles (.shp), GeoJSON, CSV with coordinates.

2.3 Data Preparation for Analysis

• Geocoding:
• Converting addresses to coordinates (e.g., customer addresses to points).
• Tools: ArcGIS Pro’s geocoding tools, ArcGIS Online geocoding services.
• Data Cleaning:
• Handling missing values and correcting errors (e.g., misaligned coordinates).
• Standardizing formats (e.g., consistent address formats).
• Data Integration:
• Combining spatial and non-spatial data (e.g., sales with store locations).
• Joining tables in ArcGIS Pro using common fields (e.g., store ID).
• Hands-On Preview:
• Import vector/raster data into ArcGIS Pro.
• Geocode a sample business dataset (e.g., store addresses) using ArcGIS Online.

Learning Outcomes:

• Differentiate between vector and raster data and their business uses.
• Identify and source relevant GIS data.
• Understand data preparation techniques for accurate analysis.

 Day (3): Spatial Analysis for Business Decisions

• Key Techniques:
• Buffering: Create zones around features (e.g., 1-mile radius around stores).
• Overlay: Combine layers to find relationships (e.g., stores and high-income areas).
• Proximity Analysis: Measure distances (e.g., customers to nearest store).
• Business Applications:
• Trade Area Analysis: Define market areas based on drive time or distance.
• Competitor Analysis: Identify competitors within a buffer zone.
• Example:
• Scenario: A retailer assesses store accessibility.
• Analysis: Buffer stores by 5 km in ArcGIS Pro, overlay with population data.

3.2 Advanced Spatial Analysis

• Techniques:
• Hotspot Analysis: Identify clusters of activity (e.g., high-sales regions).
• Interpolation: Estimate values for unmeasured areas (e.g., sales potential).
• Network Analysis: Optimize routes (e.g., delivery paths).

  Business Applications:

• Demand Forecasting: Predict sales based on spatial patterns.
• Logistics Optimization: Minimize delivery costs using network analysis.
• Case Study: Delivery Route Optimization:
• Scenario: A logistics company wants to reduce fuel costs.
• GIS Approach: Use ArcGIS Pro’s Network Analyst for shortest paths.
• Outcome: Reduced costs and improved efficiency.

3.3 Implementing Analysis in GIS

• Using ArcGIS:
• Tools: ArcGIS Pro’s Analysis Tools (e.g., Buffer, Overlay), ArcGIS Network Analyst extension.
• ArcGIS Online: Perform simple spatial analysis for web-based applications.
• Workflow:
• Load data (e.g., store locations, customer data) into ArcGIS Pro.
• Perform analysis (e.g., buffer, hotspot) using Geoprocessing tools.
• Interpret results for business decisions.
• Hands-On Preview:
• Conduct buffering and hotspot analysis on a retail dataset in ArcGIS Pro.
• Explore ArcGIS Online for sharing analysis results.

Learning Outcomes:

• Apply basic spatial analysis techniques for business insights.
• Understand advanced techniques and their applications.
• Prepare for hands-on analysis using ArcGIS Pro.

 Day (4): Data Visualization and Mapping

4.1 Principles of Map Design

• Cartographic Principles:
• Color: Use intuitive colors (e.g., red for high values, blue for low).
• Scale: Choose appropriate map scale for detail (e.g., city vs. country).
• Symbology: Use clear symbols (e.g., circles for stores, lines for roads).
• Map Types:
• Choropleth: Color-coded regions (e.g., sales by county).
• Heatmap: Density visualization (e.g., customer concentration).
• Point Maps: Individual locations (e.g., store pins).
• Best Practices:
• Simplify: Avoid clutter (e.g., too many layers).
• Label clearly: Ensure readability (e.g., legible fonts).
• Consider audience: Tailor maps for stakeholders.

4.2 Dashboards and Storytelling

• Interactive Maps:
• Features: Zoom, pan, click-to-view details.
• Tools: ArcGIS Online for web maps, ArcGIS Pro for desktop maps.
• Dashboards:
• Combine maps with charts (e.g., sales trends by region) using ArcGIS Dashboards.
• Example: ArcGIS Dashboards showing store performance and customer density.
• Storytelling with GIS:
• Structure: Problem, analysis, solution (e.g., "Where should we open stores?”).
• Use Case: Present a market expansion plan to executives using ArcGIS StoryMaps.

• Case Study: Retail Performance Dashboard:
• Scenario: A chain visualizes store performance.
• GIS Approach: Create a choropleth map in ArcGIS Pro, publish to ArcGIS Online for a dashboard.
• Outcome: Clear insights for strategic decisions.

4.3 Tools for Visualization

• ArcGIS for Mapping:
• ArcGIS Pro: Create thematic maps (e.g., choropleth for sales).
• ArcGIS Online: Share interactive maps and dashboards.
• Integration with BI Tools:
• Tableau: Import GIS data for spatial dashboards.
• Power BI: Limited spatial capabilities but useful for non-GIS visuals.
• Hands-On Preview:
• Build a choropleth map in ArcGIS Pro for sales data.
• Create a dashboard in ArcGIS Online.

Learning Outcomes:

• Apply cartographic principles to create effective maps.
• Build dashboards and tell data-driven stories.
• Prepare for hands-on visualization exercises.

 

Day (5): Practical Applications and Emerging Trends

5.1 Case Studies and Applications

• Retail: Site Selection:
• Scenario: A supermarket chain plans new stores.
• GIS Approach: Overlay demographics, traffic, and competitor data in ArcGIS Pro.
• Outcome: Identify high-potential locations.
• Logistics: Supply Chain Optimization:
• Scenario: A courier service reduces delivery times.
• GIS Approach: Network analysis in ArcGIS Pro for optimal routes.
• Outcome: Lower costs and faster deliveries.
• Risk Analysis:
• Scenario: An insurance company assesses flood risk.
• GIS Approach: Overlay property locations with flood maps in ArcGIS Online OTTER.
• Outcome: Accurate risk pricing.

5.2 Emerging Trends in GIS

• Geospatial Machine Learning:
• Predictive analytics: Forecast sales based on spatial patterns.
• Example: Use ArcGIS Pro’s GeoAI tools or GeoPandas with scikit-learn.
• Cloud-Based GIS:
• Tools: ArcGIS Online, Google Earth Engine.
• Benefits: Scalability, real-time data access.
• Example: Real-time traffic analysis for logistics.
• Integration with BI:
• Combine GIS with Python (GeoPandas), R, or Tableau.
• Example: Spatial clustering for customer segmentation in ArcGIS Pro.

5.3 Ethical Considerations and Future Learning

• Ethical Issues:
• Data Privacy: Protect customer location data (e.g., GDPR compliance).
• Bias in Data: Address skewed datasets (e.g., under-sampled rural areas).
• Transparency: Communicate map limitations to stakeholders.

 Future Learning:

• Resources: Esri Training (https://www.esri.com/training/), ArcGIS tutorials, OpenStreetMap documentation.
• Advanced Tools: ArcGIS Pro, Python for GIS (GeoPandas, ArcPy).
• Capstone Project Preview:
• Analyze a business dataset (e.g., optimize store locations) in ArcGIS Pro.
• Present findings with maps and insights using ArcGIS StoryMaps.

Learning Outcomes:

• Apply GIS to real-world business scenarios.
• Understand emerging trends like geospatial ML and cloud GIS.
• Recognize ethical challenges and plan for continued learning.

 

 

List of Abbreviations Used

Abbreviation	Full Form and Explanation
API	Application Programming Interface: A set of rules allowing software applications to communicate, used for accessing geospatial data (e.g., Google Maps API).
BI	Business Intelligence: Tools and processes for analyzing business data, integrated with GIS for spatial insights (e.g., Tableau, Power BI).
CSV	Comma-Separated Values: A file format for storing tabular data, used in GIS for importing datasets with coordinates (e.g., customer addresses).
GDPR	General Data Protection Regulation: An EU regulation for protecting personal data, relevant to handling customer location data.
GIS	Geographic Information System: A framework for capturing, storing, analyzing, and visualizing spatial data, central to the course.
ML	Machine Learning: A subset of AI used in geospatial analytics for predictive modeling (e.g., forecasting sales).
USGS	United States Geological Survey: A source of open geospatial data, such as elevation maps, used in GIS applications.
UTM	Universal Transverse Mercator: A projected coordinate system used for local-scale GIS analysis, providing flat map representations.
WGS84	World Geodetic System 1984: A global geographic coordinate system using latitude/longitude, standard for GIS data alignment.
ArcGIS Pro	ArcGIS Professional: Esri’s desktop GIS software for advanced spatial analysis and mapping, used in this course.
ArcGIS Online	ArcGIS Online: Esri’s cloud-based platform for creating and sharing interactive maps and dashboards.

 

Training Method

A highly interactive combination of lecture and discussion sessions will be managed to maximize the amount and quality of information, knowledge, and experience transfer. The sessions will start by raising the most relevant questions and motivating everybody to find the right answers. The attendants will also be encouraged to raise more of their questions and to share developing the right answers using their analysis and experience. Course material through power point equipped with necessary animation, learning videos, and general discussion to provide the participant with full understanding concerning the subject course will be provided.

Flip chart – Lectures – Practical situations – Video – PowerPoint – Presentation –  style coach- led discussions – activities – Exercises - Quiz - Case study - Assessment.