Practice trajectory
Three GMetrix attempts logged: 640 → 900 → 840. Persistent weak sub-domains feeding this sheet: datums & coordinate systems (101), spatial data relationships (202), automated data collection (403), spatial file types (503), graphic representation (301). Press e to expand all sections, c to collapse.
📌 Classification Methods
| Scenario trigger | Method |
|---|---|
| Same number of features per class | Quantile |
| Visually balanced map / show relative rank | Quantile |
| Skewed data / outliers / natural clusters | Natural Breaks |
| Show true magnitude differences | Natural Breaks |
| Above/below average / normally distributed | Std Deviation |
| Uniform / evenly spread distribution | Equal Interval |
| Map series / cross-time comparison | Equal Interval (fixed breaks) |
Know each weakness
Quantile → groups unlike values together |
Equal Interval → empty classes with skewed data |
Natural Breaks → NOT comparable across maps |
Std Deviation → misleading with non-normal data
Trap to watch
"Normally distributed data — put equal features per class" → the normal distribution is a red herring. Equal features per class = Quantile regardless of distribution shape.
🗃️ Raster Operations — Local / Focal / Zonal / Global
Local
Cell-by-cell math across rasters. No neighbors. No zones. Matching positions only.
Add/subtract/multiply rasters · reclassify values · change detection (raster A − raster B)
Focal
Each cell calculated from its surrounding neighborhood / moving window.
Slope · aspect · smoothing filter · majority filter · any "neighboring cells" scenario
Zonal
Statistics calculated per zone / polygon group.
Avg elevation per watershed · max temp per climate zone · total precip per county
Global
Uses all cells in the entire raster for each output cell.
Euclidean distance · cost distance · viewshed analysis
Quick test
Neighbors involved? → Focal | Per zone/polygon? → Zonal | Whole raster? → Global | Cell-to-cell math? → Local
🔀 Overlay Operations — Union / Intersect / Clip / Erase
| Operation | Output | Trigger words |
|---|---|---|
| Union | Everything from A + B, full extent, all attributes | "all features from both," "combine," "merge" |
| Intersect | Only where A and B overlap, both attributes | "within," "inside," "overlap," AND condition |
| Clip | A cut to B's shape, only A's attributes | "cut to boundary," "extract within study area" |
| Erase | A minus B's area | "outside," "beyond," "more than X away," "exclude" |
Direction rule — most important
Want what's INSIDE a zone → Buffer → IntersectWant what's OUTSIDE a zone → Buffer → Erase
"Forest more than 2km from roads" → Buffer roads → Erase buffer from forest
📏 ISO Data Quality Elements
| Type | What it means | Example |
|---|---|---|
| Positional accuracy | Feature is in the wrong location | Road 50m west of true position |
| Logical consistency | Features contradict each other or violate a real-world rule | Road through a lake · bridge below river · parcel coded as both residential AND industrial |
| Attribute accuracy | Feature location is correct but attribute value is wrong | Highway coded as residential street |
| Temporal accuracy | Data doesn't reflect the correct time period | Demolished building still shown · 2005 dataset used for 2026 study |
| Completeness | Missing features or excess features | Roads not captured · duplicate polygons |
| Thematic accuracy | Features classified or labeled incorrectly | Land cover type misidentified |
Two-question test
Wrong place? → Positional | Features contradict? → Logical | Wrong value? → Attribute | Out of date? → Temporal
Intentional vs unintentional error
Intentional: generalization · map projection distortion (cartographer chooses to accept these)Unintentional: GPS error · digitizing mistakes · scanner distortion
🎨 Bertin's Visual Variables
| Variable | Best for | Example |
|---|---|---|
| Value (light→dark) | Ordered / quantitative | Light to dark blue for population density |
| Hue | Nominal / categorical | Red/blue/green for land use types |
| Size | Quantitative at point locations | Circle size for city population |
| Shape | Nominal at point locations | Circle vs square vs triangle symbols |
| Texture | Nominal or ordered — pattern of marks | Dashed vs solid lines · stipple · hatch spacing |
| Orientation | Directional data | Wind direction arrows |
| Position | All data types | X/Y location on the map |
Key pairing
Quantitative/ordered → Value or Size | Categorical/nominal → Hue or ShapeTexture = dashed/dotted/hatched PATTERNS — not lightness, not color
🌈 Color Schemes
| Scheme | Data type | Structure | Example |
|---|---|---|---|
| Sequential | Ordered / quantitative | One hue, light → dark (one direction) | White → dark blue for rainfall |
| Diverging | Ordered with meaningful midpoint | Two hues from neutral center | Red → white → blue for temp anomalies |
| Qualitative | Nominal / categorical | Distinct hues, no implied order | Land use types, political parties |
Exam triggers
One direction of progression → SequentialTwo directions from a meaningful midpoint / "anomaly" / "above-below average" → Diverging
No implied order / categorical → Qualitative
Classic pairing
Standard Deviation classification + Diverging color scheme = perfect for above/below average analysis
🗺️ Map Types
| Map type | Data requirement | Trigger |
|---|---|---|
| Choropleth | Normalized only rates, %, density — NEVER raw counts | "per capita," "per sq mile," "rate," "percentage" |
| Graduated symbol | Raw counts — single scaled symbol per location | "symbol size proportional to value" |
| Dot density | Raw counts — each dot = fixed quantity, randomly placed in polygon | "each dot represents X" |
| Isopleth | Continuous surface values | "contour lines," "equal values," temperature/elevation |
| KDE | Point events → smooth density surface | "hotspot," "concentration," "without showing individual points" |
Most-tested rule
Choropleth must NEVER use raw counts. Larger areas always have more of anything — normalize first (divide by population, area, etc.)
🔗 Topology
Topology enables — memorize all 3
1. Selecting adjacent features
2. Enforcing planar data integrity
3. Automating data clean-up
| Rule | Applies to | Flags |
|---|---|---|
| Must not overlap | Polygons | Two polygons sharing interior space |
| Must not have gaps | Polygons | Uncovered sliver between polygons |
| Must not have dangles | Lines | Unconnected line endpoint |
| Must not have pseudo nodes | Lines | Only two lines meeting end-to-end (no true intersection) |
| Planar enforcement | Polygons | No gaps + no overlaps = every location in exactly one polygon |
Topology does NOT
Increase positional accuracy — topology works with coordinates you already have. It enforces relationships, not precision.
🌐 Projections & Datums
| Projection | Preserves | Use case |
|---|---|---|
| Mercator | Shapes/angles (conformal) | Navigation — rhumb lines are straight |
| Albers Equal-Area Conic | Area | US/Canada thematic mapping standard |
| Lambert Conformal Conic | Shapes/angles | Aeronautical charts, State Plane (wide states) |
| Gnomonic | Great circles as straight lines | Flight route planning |
| Azimuthal Equidistant | Distance + direction from center | UN emblem — every point true distance from pole |
| Polar Stereographic | Local shapes (conformal) | Antarctica / polar mapping |
| Robinson | Compromise (nothing perfectly) | World reference maps — no analysis |
Horizontal datums
WGS 84 (GPS default · global)
NAD 83 (US standard · GRS 80)
NAD 27 (legacy · Clarke 1866)
NAD 27→83 shift = up to 100m — EXPECTED
Vertical datums
NAVD 88 = current US standard elevation
NGVD 29 = legacy (convert to NAVD 88)
GPS → ellipsoidal height
GEOID18 → converts to NAVD 88
NADCON = horizontal transform only
State Plane vs UTM
State Plane: ~3° wide zones · Lambert (wide states) or Transverse Mercator (tall states) · WA = LambertUTM: 6° wide · 60 zones · WA = zones 10 & 11 · False easting 500,000m · Scale factor 0.9996 (secant)
Easting math: Easting − 500,000 = distance from central meridian (+ = east, − = west)
🌍 OGC Web Services
| Service | Returns | Editable? | Trigger |
|---|---|---|---|
| WMS | Rendered map image | No | "display map image" |
| WMTS | Pre-tiled cached image | No | "pre-rendered tiles," "fixed zoom levels," basemaps |
| WFS | Vector features + attributes | No | "query," "download," "vector features" |
| WFS-T | Vector features + attributes | Yes | "edit," "insert," "update," "delete" features |
| WCS | Raster/coverage data | No | "raster," "coverage," "grid data" |
| GML | XML-based vector encoding | — | WFS native format · XML + OGC + geographic features |
📁 Spatial File Formats
| Format | Key facts | Limitations |
|---|---|---|
| Shapefile | Multiple files (.shp .shx .dbf .prj) · one geometry type | 2GB limit · 10-char field names · no nulls in numeric · no topology · no domains |
| GeoJSON | Open standard · JSON · web mapping standard · ODbL licensed | WGS 84 only · no topology · large file sizes for complex data |
| GeoPackage | Open standard · SQLite · vector + raster · OGC shapefile replacement | Best for offline mobile use · multi-feature class storage |
| File GDB | Esri · up to 1TB · domains · subtypes · topology · long field names | Proprietary · limited write support in open-source GIS |
| LAS / LAZ | LiDAR point cloud standard · x/y/z + intensity + return + classification | LAZ = compressed LAS |
| COG | Cloud Optimized GeoTIFF · HTTP range requests · no full download needed | Raster only · read-optimized not edit-optimized |
🗄️ Database Design & Management
Design sequence
Needs Assessment → Conceptual (entities & relationships) → Logical (tables, fields, data types) → Physical (platform implementation) → Implementation
| Concept | Definition |
|---|---|
| Primary key | Unique identifier in its own table — no nulls, no duplicates |
| Foreign key | Field in one table referencing the primary key of another table |
| Coded value domain | Restricts field to a predefined list (Paved / Gravel / Dirt) |
| Range domain | Restricts numeric field to min/max range (speed limit 5–85) |
| Attribute join | Shared key field · merges tables · one-to-one or many-to-one only |
| Spatial join | Based on geographic location · no shared key needed |
| Relate | Navigable link · no merge · handles one-to-many |
| Versioning | Enterprise GDB only · multi-user simultaneous editing without conflicts |
| Many-to-many | Requires a junction/intermediate table |
📋 Professional Practice & Ethics
GISCI minimum metadata
Source · Date · Projection · Author⛔️ NOT location ⛔️ NOT user
| Reality type | Definition |
|---|---|
| Virtual | Fully immersive — replaces real world entirely (3D building model from desk) |
| Augmented | Digital overlay ON the real world (tablet overlay on real street view) |
| Mixed | Digital + physical interact in real time (virtual objects anchor to real surfaces) |
Agile vs Waterfall
Agile: iterative cycles · continuous feedback · frequent delivery · requirements evolveWaterfall: all requirements defined upfront · linear sequential phases · no going back
📐 Analytical Methods
| Method | Use case |
|---|---|
| Buffer | Zone of specified distance around features. Output = always a vector polygon. |
| Dissolve | Merge features sharing the same attribute value. Removes internal boundaries. |
| Spatial join | Transfer attributes based on geographic location/relationship |
| Point-in-polygon | Determine which polygon each point falls within — transfers polygon attributes to point |
| KDE | Smooth continuous density surface from points. Bandwidth controls smoothness. |
| Kriging | Geostatistical interpolation using spatial autocorrelation. Gold standard for unknown values. |
| IDW | Inverse Distance Weighting — closer points weighted more. Simpler than Kriging. |
| Thiessen / Voronoi | Every location assigned to nearest point feature. Proximity-based, not statistical. |
| Cost path | Least-cost route across a cost surface raster (pipeline routing, corridor siting) |
| Watershed delineation | Starts with DEM → fill sinks → flow direction → flow accumulation → pour point → boundary |
Map algebra operations
Local: cell × cell (multiply slope × erodibility) |
Logical: cells where value > 50 → returns 1/0 binary raster |
Mathematical: square root, log of cell values
🛰️ Remote Sensing
| Concept | Definition |
|---|---|
| Passive sensor | Detects naturally available energy (reflected sunlight or emitted heat) · needs daylight for optical |
| Active sensor | Emits own energy, measures return · works day/night/cloud cover · LiDAR (laser) · SAR/RADAR (microwave) |
| Spatial resolution | Pixel size on the ground (1m vs 30m) |
| Spectral resolution | Number and width of wavelength bands captured |
| Temporal resolution | How frequently sensor revisits same area |
| Radiometric resolution | Sensitivity to signal differences — expressed in bits (8-bit = 256 values) |
| NDVI | (NIR − Red) / (NIR + Red) · healthy vegetation = high NIR, low Red = high NDVI |
| LiDAR | Time-of-flight laser · multiple returns (canopy, branches, ground) · stored as LAS/LAZ |
| Vertical imagery | Nadir (straight down) · accurate measurements · photogrammetric mapping · consistent scale |
| Oblique imagery | Angled view · shows building facades · better urban context visualization |
🧭 Coordinate Systems Quick Reference
| System | Units | Use |
|---|---|---|
| GCS (Geographic) | Degrees (angular) | Storage format · global data · cannot measure distance directly |
| PCS (Projected) | Meters or feet (linear) | Analysis · distance/area measurement · flat surface |
| State Plane | Feet (US) | County/state-level precision work · surveying · cadastral |
| UTM | Meters | Regional mapping · 6° wide zones · WA = 10 & 11 |
| Web Mercator | Meters | Web basemaps only · distorts area · NOT for analysis |
North America coordinate check
Longitude must be NEGATIVE (−60° to −170°) = west of prime meridianLatitude must be POSITIVE (+15° to +85°) = north of equator
Positive longitude = eastern hemisphere = NOT North America
.prj file WKT check
GEOGCS = Geographic Coordinate System (degrees) | PROJCS = Projected Coordinate System (linear units)No .prj file = unknown coordinate system — do not assume WGS 84
✅ Select All That Apply — Test Strategy
The habit to build
Treat every option as an independent true/false question. Don't look for THE answer — evaluate each option on its own. Every option that is independently true must be selected. Never stop early because you think you have enough.
Red flag words — almost always WRONG
"always" · "never" · "only" · "completely replaced" · "eliminates the need for" · "cannot be used with"Absolute statements are wrong ~80% of the time on this exam.
Common traps
• Topology increases positional accuracy ❌ — it enforces relationships, not precision• GeoJSON supports multiple coordinate systems ❌ — WGS 84 only
• VGI is always less accurate than professional data ❌ — "always" = red flag
• Waterfall = iterative cycles ❌ — that's Agile
• Natural Breaks is comparable across maps ❌ — recalculates each time
GISCI Geospatial Core Technical Knowledge · 150 questions · Passing 730/1000
← Geospatial