🗺️Geospatial Engineering Unit 5 – Geographic Information Systems (GIS)

What's GIS All About?

• Geographic Information Systems (GIS) integrate hardware, software, and data for capturing, managing, analyzing, and displaying all forms of geographically referenced information
• Allows users to view, understand, question, interpret, and visualize data in many ways that reveal relationships, patterns, and trends in the form of maps, globes, reports, and charts
• Helps answer questions and solve problems by looking at data in a way that is quickly understood and easily shared
• GIS technology can be integrated into any enterprise information system framework
• Enables better decision making by providing a spatial perspective on data and allowing for more informed analysis
  • For example, GIS can help determine the best location for a new store based on demographic data, accessibility, and competition in the area
• Supports a wide range of applications from environmental management and transportation planning to business analytics and public health
• Combines the power of databases with the visualization capabilities of maps, creating a powerful tool for understanding and solving real-world problems

Key GIS Concepts

• Spatial data represents real-world features and their locations on the Earth's surface
  • Includes points (cities), lines (roads), and polygons (land parcels)
• Attribute data provides additional information about spatial features
  • For instance, a city might have attributes such as population, elevation, and name
• Coordinate systems and map projections enable accurate representation and analysis of spatial data
  • Geographic coordinate system uses latitude and longitude to define locations on the Earth's spherical surface
  • Projected coordinate systems flatten the Earth's surface onto a two-dimensional plane for mapping and analysis
• Topology describes the spatial relationships between features
  • Includes concepts like connectivity (how lines connect), adjacency (what polygons share borders), and containment (points within a polygon)
• Geocoding is the process of converting addresses into spatial data (points) that can be mapped and analyzed
• Spatial analysis involves using GIS tools to manipulate, query, and analyze spatial data to derive new insights and information
• Cartography, the art and science of mapmaking, is a key component of communicating GIS results through effective visualization

GIS Software and Tools

• GIS software provides the tools and functions needed to store, analyze, and display geographic information
• Esri's ArcGIS is a leading GIS software suite used across industries and organizations
  • Includes desktop applications (ArcMap, ArcGIS Pro), server-based tools (ArcGIS Enterprise), and cloud-based platforms (ArcGIS Online)
• Open-source alternatives like QGIS and PostGIS offer free and customizable GIS solutions
• Spatial databases (PostgreSQL with PostGIS, Oracle Spatial) enable efficient storage, management, and querying of large spatial datasets
• Web mapping APIs (Leaflet, OpenLayers) and libraries (D3.js, Mapbox GL JS) facilitate the development of interactive, web-based GIS applications
• Mobile GIS tools (Esri Collector, Survey123) allow for field data collection and real-time updating of spatial databases
• Specialized tools exist for specific GIS tasks, such as remote sensing (ENVI), spatial statistics (GeoDa), and 3D visualization (CityEngine)
• The choice of GIS software and tools depends on factors like project requirements, budget, technical expertise, and interoperability with existing systems

Data Types and Sources

• Vector data represents discrete features as points, lines, or polygons
  • Examples include city locations (points), roads (lines), and land parcels (polygons)
• Raster data represents continuous surfaces using a grid of cells, each with a specific value
  • Digital elevation models (DEMs) and satellite imagery are common examples of raster data
• Tabular data, such as CSV files or database tables, can be linked to spatial features using unique identifiers (e.g., a parcel ID linking to a land ownership database)
• Primary data sources involve direct collection of spatial data through field surveys, GPS measurements, or remote sensing
• Secondary data sources provide pre-existing spatial datasets, often from government agencies or commercial providers
  • U.S. Census Bureau offers demographic and socioeconomic data
  • U.S. Geological Survey provides topographic maps, DEMs, and land cover data
  • OpenStreetMap is a collaborative project creating a free, editable map of the world
• Metadata, or "data about data," provides essential information about a dataset's content, quality, format, and provenance
• Data quality issues, such as accuracy, completeness, and currency, must be considered when selecting and using spatial datasets

Spatial Analysis Techniques

• Overlay analysis combines multiple spatial layers to identify relationships and create new information
  • For example, overlaying a land use layer with a flood risk layer to identify vulnerable areas
• Buffer analysis creates zones of a specified distance around features to analyze proximity
  • Useful for tasks like identifying properties within 500 meters of a proposed highway
• Network analysis examines the flow and connectivity of features in a network, such as roads or utility lines
  • Includes finding shortest paths, optimizing routes, and identifying service areas
• Terrain analysis derives new information from elevation data (DEMs)
  • Includes calculating slope, aspect, hillshade, and viewsheds
• Spatial interpolation estimates values at unsampled locations based on known values at nearby points
  • Kriging and inverse distance weighting (IDW) are common interpolation methods
• Spatial pattern analysis identifies clusters, dispersion, or randomness in the distribution of features
  • Ripley's K function and Moran's I are examples of spatial pattern analysis tools
• Spatial modeling uses GIS to simulate real-world processes and predict outcomes
  • Agent-based models and cellular automata are examples of spatial modeling techniques
• Geostatistics analyzes and models spatial patterns and relationships in data
  • Variograms and kriging are key geostatistical tools used in GIS

Map Making and Visualization

• Cartographic design principles guide the creation of effective and visually appealing maps
  • Includes choosing appropriate symbols, colors, and typography to convey information clearly
• Thematic maps display the spatial distribution of a specific theme or attribute
  • Choropleth maps use color gradients to represent values across polygons (e.g., population density by county)
  • Proportional symbol maps use scaled symbols to represent values at points or polygons (e.g., city population)
• Web mapping enables the creation of interactive, online maps that can be accessed and shared easily
  • Tools like ArcGIS Online, Mapbox, and Leaflet facilitate web map creation and publishing
• 3D visualization adds depth and realism to spatial data, enhancing understanding and communication
  • CityEngine and ArcGIS Pro offer tools for creating 3D city models and landscapes
• Animated maps can show spatial patterns and processes that change over time
  • For example, an animated map showing the spread of a disease outbreak
• Dashboards integrate maps with charts, graphs, and other data to provide a comprehensive view of a topic or issue
  • Esri's Operations Dashboard is a popular tool for creating GIS-powered dashboards
• Storytelling with maps combines narrative, multimedia, and interactive maps to engage audiences and convey complex information
  • Esri's StoryMaps platform enables the creation of compelling, map-based stories

Real-World GIS Applications

• Urban planning utilizes GIS to analyze land use patterns, transportation networks, and demographic trends to inform city development and management
• Environmental management applies GIS to monitor and mitigate issues like deforestation, habitat loss, and climate change
  • For example, using satellite imagery to track changes in forest cover over time
• Public health organizations use GIS to map disease outbreaks, identify at-risk populations, and plan interventions
  • John Snow's famous cholera map is an early example of using spatial analysis in public health
• Transportation agencies rely on GIS for planning, managing, and maintaining transportation infrastructure
  • Includes optimizing bus routes, identifying accident hotspots, and prioritizing road repairs
• Disaster response and emergency management use GIS to assess risks, plan evacuations, and coordinate relief efforts
  • For instance, using real-time data to map the spread of a wildfire and identify affected communities
• Business and marketing leverage GIS for site selection, customer analysis, and targeted advertising
  • Retailers can use GIS to identify optimal locations for new stores based on factors like population density and competition
• Agriculture applies GIS for precision farming, crop monitoring, and yield analysis
  • Farmers can use GPS-enabled tractors and drones to collect data and optimize planting, fertilization, and harvesting

Future Trends in GIS

• Big data and cloud computing are enabling the processing and analysis of massive spatial datasets
  • Cloud platforms like Google Earth Engine and Amazon Web Services offer scalable GIS capabilities
• Artificial intelligence (AI) and machine learning are enhancing GIS analytics and automation
  • Deep learning can be used to automatically classify land cover from satellite imagery
• Internet of Things (IoT) sensors are generating real-time, location-aware data streams for GIS analysis
  • Smart city initiatives use IoT sensors to monitor traffic, air quality, and energy usage
• Augmented reality (AR) and virtual reality (VR) are creating immersive, interactive experiences with spatial data
  • AR can be used to visualize underground utilities on a construction site
• Blockchain technology has potential applications in secure, decentralized management of spatial data and transactions
  • Land registries and supply chain management could benefit from blockchain-based GIS solutions
• Open data initiatives are increasing access to and transparency of spatial data
  • Platforms like OpenStreetMap and government open data portals enable wider use and collaboration around GIS
• Geospatial data science is an emerging field that combines GIS, data science, and statistical analysis to extract insights from spatial data
  • Includes techniques like spatial data mining, machine learning, and network analysis