“Into to GIS Analysis”
GIS analysis finds patterns and relationships in spatial data to solve real world problems. The workflow follows a five steps process: find & define your specific question, understand your data, choose a method, process it, evaluate the result & lastly form a conclusion. Some important terms in the chapter:
- Feature types: Discrete (points, lines, polygons), Continuous (gapless surfaces like elevation), or Area (zip codes, census tracts).
- Data models: Vector (coordinate based) or Raster (cell based).
- Attributes: Categories, Ranks, Counts/Amounts, or Ratios.
GIS work involves but is not limited to; querying, calculating, and summarizing tables. Stuff depends on matching these three components listed above in the correct manner such as relating the right data model with the right attribute both according to your question. Hence, before I start any analysis I will understand my question foremost and then move onto deciding things because that lone thing can make it or break it.
“Mapping Where Things Are”
Mapping locations shows distribution, clustering, and dispersion. Data need be prepped first with accurate coordinates and clean category codes. Like the question was the most important thing in the first chapter, your data means just as much here. Three key things again:
- You can map one feature type, isolate a subset, or map multiple categories to show relationships and hierarchy.
- People can only reliably tell apart about seven colors or patterns so anything past that needs grouping.
- Symbology rules:
- Color beats shape for points
- Vary line width for networks
- Keep reference layers muted (light-gray basemaps) so context doesn’t fight the data
When we start to build a map most of us just wish to add more and more things, add in more features, add in more information, yes I concede more information might be better but most of the time it is simplicity and visual hierarchy instead which decide whether a map actually works or not and that seven category limit is the hard ceiling. For anything public facing I’ll group categories aggressively and stick to minimal basemaps so the real data stands out.
“Mapping the Most and Least”
Mapping quantities compares places to show trends and concentrations. The big rule: when areas are unequal in size, use ratios (averages, proportions, densities), never raw counts, or the pattern is misleading.
- Classifications:
- Natural Breaks: clustered data
- Quantile: equal features per class
- Equal Interval: identical value ranges
- Standard Deviation: distance from the mean
- Outliers be handled or they compress everything else into one class.
- Display options: graduated symbols, graduated colors, pie/bar charts, contours.
The classification we pick completely changes the story the map tells, and raw counts should never go on variable-sized polygons.