The uploaded PowerPoint file, **"Part I - ch-1 GIS Lesson.pptx,"** is an **introduction to geographic information systems (GIS)** presentation. It covers: ### **Key Topics:** 1. **Definition of GIS** - A system that captures, stores, analyzes, and visualizes spatial data for decision-making. - Example: Mapping flood zones, urban planning, disease tracking. 2. **Components of GIS** - **Hardware**: Computers, GPS devices - **Software**: ArcGIS, QGIS - **Data**: Spatial (maps) and attribute (descriptive) data - **People**: GIS analysts, researchers, decision-makers - **Methods**: Techniques for spatial analysis 3. **Types of GIS Data** - **Raster Data**: Grid-based images (e.g., satellite images) - **Vector Data**: Points (e.g., locations), Lines (e.g., roads), Polygons (e.g., land parcels) 4. **Key GIS Functions** - **Data Capture**: Collecting geographic data - **Data Storage**: Organizing data in databases - **Data Analysis**: Identifying spatial patterns - **Visualization**: Creating maps and charts - **Decision-Making**: Supporting urban planning and resource management 5. **GIS Applications** - **Urban Planning**: Infrastructure management - **Environmental Management**: Ecosystem monitoring - **Disaster Management**: Flood and earthquake mapping - **Agriculture**: Precision farming, crop monitoring - **Health**: Disease tracking and healthcare accessibility 6. **Key GIS Concepts** - **Spatial Data**: Geographic data with coordinates - **Layers & Overlay**: Stacking data layers for analysis - **Georeferencing**: Assigning coordinates to data - **Spatial Analysis**: Advanced techniques like route selection 7. **Real-World Representation in GIS** - **Vector Model**: Points, lines, polygons - **Raster Model**: Pixel grids with values - **Attribute Data**: Descriptive information tied to features 8. **Case Study: Flood Risk Mapping** - Goal: Identify flood-prone areas. - Data Layers: DEM (elevation), river networks, land use - Process: Slope and flow maps, buffering rivers, overlaying land-use data - Output: Flood risk map showing high/medium/low-risk zones ### **Conclusion** GIS is a powerful tool for spatial data analysis, planning, and decision-making. It integrates different data types and techniques to provide real-world solutions in various fields. Let me know if you need edits, explanations, or improvements for your PowerPoint slides!

1. GIS

2. Geography:-
 The study of the Earth’s physical features,
environments, and human interactions with those
environments.
 It explores landforms, climates, populations,
resources, and how people shape and are shaped
by the world around them.

3. Information:-
 Data that has been processed or organized to
provide meaning or context.
 In GIS, information refers to spatial data (like
coordinates, attributes, and maps) that help us
understand patterns or relationships.

4. System:-
 A set of connected components or processes that
work together to achieve a goal.
 In GIS, a system integrates hardware, software,
data, and people to capture, analyze, and visualize
geographic information.

5. Part I - GIS
CHAPTER ONE: Introduction to Geographic Information Systems (GIS)

6. Title: Introduction to Geographic Information Systems (GIS)
Subtitle: Part I - GIS | Chapter One
Presented by: Tajebe Negash WeldeMichael

7. What is GIS?
Definition: A system that captures, stores, analyzes, and
visualizes spatial data.
Supports decision-making through spatial analysis.
Example: Mapping flood zones, planning cities, tracking
diseases.

8. What GIS can do?

9. What Analysis GIS can do ?

10. Components of GIS
 Hardware: Computers, GPS devices.
 Software: ArcGIS, QGIS.
 Data: Spatial (maps) and attribute (descriptive)
data.
 People: Analysts, researchers, decision-makers.
 Methods: Techniques and workflows for
analysis.

11. Key Functions of GIS
 Data Capture: Collecting geographic data.
 Data Storage: Organizing data in databases.
 Data Analysis: Identifying spatial patterns.
 Visualization: Creating maps and charts.
 Decision Making: Supporting planning and management.

12. Types of GIS Data
1. Raster Data: Grid-based images (e.g., satellite
images).
2. Vector Data:
 Points (e.g., locations).
 Lines (e.g., roads).
 Polygons (e.g., land parcels).

13. GIS Applications
 Urban Planning: Managing infrastructure.
 Environmental Management: Monitoring ecosystems.
 Disaster Management: Flood and earthquake mapping.
 Agriculture: Precision farming and crop monitoring.
 Health: Disease tracking and healthcare access.

14. Concepts of GIS
 Spatial Data: Geographical data with coordinates.
 Layers & Overlay: Stacking data layers for analysis.
 Spatial Relationships: Proximity, intersection, etc.
 Georeferencing: Assigning coordinates to data.
 Data Integration: Merging multiple data sources.
 Spatial Analysis: Advanced analysis like route selection.

15. Objectives of GIS
 Data Collection & Management: Organizing geographic data.
 Spatial Analysis & Decision Support: Finding patterns.
 Visualization & Mapping: Creating maps and models.
 Environmental Management: Monitoring land use.
 Urban Planning: Designing infrastructure.
 Scientific Research: Climate change, public health.

16. Real-World Representation in GIS
 Vector Data Model: Points, lines, polygons.
 Raster Data Model: Grid of pixels with values.
 Attribute Data: Descriptive data tied to features.

18. Vector Data
 Advantages: High precision, small file size.
 Disadvantages: Complex processing, manual
digitization.
 Examples: Roads, buildings, land parcels.

19. Raster Data
 Advantages: Ideal for continuous data, fast analysis.
 Disadvantages: Large file size, pixelated boundaries.
 Examples: Satellite images, elevation models.

20. Attribute Data
 Advantages: Enhances GIS analysis.
 Disadvantages: Data errors, processing overhead.
 Examples: Population, land use, temperature.

21. Feature Vector Data Raster Data Attribute Data
Structure Points/lines/polygons Grid of cells Descriptive tables
Best For Boundaries, networks Elevation, imagery Feature descriptions
Accuracy High Lower (pixelated) Varies
File Size Small Large (high-res) Varies
Analysis Speed Slower Faster Database-dependent
Summary Table - Vector vs. Raster vs. Attribute Data

22. Real-World Feature Vector Representation Raster Representation
Tree Point Single cell
River Line Connected cells
Lake Polygon Group of cells
Population Density Data table linked to map Heatmap/grid
Real-World Objects vs. GIS Representation

23. Layers in GIS
 Example: City GIS database with
layers for:
o Roads (lines)
o Buildings (points)
o Land Use Zones (polygons)
o Population Density (attribute data)

24. Case Study - Flood Risk Mapping
 Goal: Identify flood-prone areas.
 Data Layers:
o DEM (elevation).
o River networks.
o Land use.
 Process:
o Generate slope and flow maps.
o Buffer rivers to mark risk zones.
o Overlay land-use data to assess impact.

25. Visualization and Results
 Flood Risk Map: Shows high/medium/low risk zones.
 Impact Analysis: Highlights affected infrastructure.
 Outcome: Informs flood prevention strategies.

26. Conclusion & Summary
 GIS: A powerful tool for spatial data analysis.
 Key Concepts: Layers, spatial analysis,
georeferencing.
 Applications: Planning, environment, disaster
management.
 Models: Vector, raster, and attribute data.