Technology shapes how we do things…stairs vs slides in buildings.
Notes and examples on “Introduction & Terminology” and “Conceptual Frameworks for Spatial Analysis.”
Introduction & Terminology
1. On applications
- Noise Mapping (Google) & Noise Mapping
- Air Quality Mapping (Google) & Air Quality Mapping
- Crime Mapping (Google) & Crime Mapping
- Health Mapping (Google) & Health Mapping
2. GIS, Spatial Analysis, and Software
3. Terminology & Definitions
Conceptual Frameworks for Spatial Analysis
The Geospatial Perspective: “a distinct perspective on the world, a unique lens through which to examine events, patterns, and processes that operate on or near the surface of our planet.”
The domain of geospatial analysis is the surface of the Earth, extending upwards in the analysis of topography and the atmosphere, and downwards in the analysis of groundwater and geology. In scale it extends from the most local, when archaeologists record the locations of pieces of pottery to the nearest centimetre or property boundaries are surveyed to the nearest millimetre, to the global, in the analysis of sea surface temperatures or global warming. In time it extends backwards from the present into the analysis of historical population migrations, the discovery of patterns in archaeological sites, or the detailed mapping of the movement of continents, and into the future in attempts to predict the tracks of hurricanes, the melting of the Greenland ice-cap, or the likely growth of urban areas.
Geospatial Analysis: what happens where, and makes use of geographic information that links features and phenomena on the Earth’s surface to their locations.
1. Basic “Primitives”
- place: complicated concept: Wikipedia
- attributes: “any recorded characteristic or property of a place” + measurement levels (qualitative, quantitative) + examples in ArcGIS
- objects: raster (images) & vector (points, lines, areas) below (from Making Maps):
- maps: defining maps & defining maps and more defining maps (PDF)
- multiple properties of place: attributes & classification
- fields: discrete (example) and continuous (example) phenomena
- networks: example & example
- density: examples (Google)
- detail: scale and generalization (from Making Maps):
2. Spatial Relationships
- co-location: poverty and riots or mammography and income or curious bugs.
- distance and direction: garbage pickup (network analysis)
- spatial context: more or less the same as co-location
- neighborhood: defining a neighborhood (buffer) in GIS and viewsheds & Civil War viewsheds.
- spatial heterogeneity: “The results of any analysis over a limited area can be expected to change as that limited area is relocated, and to be different from the results that would be obtained for the surface of the Earth as a whole.” In essence, places are complicated and prediction from place to place difficult.
- spatial dependence: even though places are complicated: Tobler’s “First Law of Geography”: “All things are related, but nearby things are more related than distant things.” Example: Bike trails and property values
- spatial sampling: weather map and terrain:
- spatial interpolation: filling in between known data
- smoothing and sharpening (generalization; see above)
3. Spatial Statistics
- Spatial probability: probability of landslides
- Uncertainty: variation in how certain we are about what we analyze and map with GIS: soils and water quality
- Statistical inference: defined and predicting radiation spread and inferring who you are for marketing
4. Spatial Data Infrastructure
- Interoperability: standards for spatial data (so everything works together): OGC
…All this jargon…