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GIS Fall Course Prep Guide.

Courses: GEOG 340, 344, 350, 360
American River College — Summer 2026 Preparation

Your Starting Point

Coming out of GEOG 330 and 334, you have solid foundational GIS skills. You're also working through a Python for GIS course covering GeoPandas, Shapely, GDAL/OGR, PostGIS, and FastAPI — that background will pay dividends across several of these courses.

Esri Learn Courses Already Completed

You've already knocked out a strong foundation in spatial relationships, overlay, proximity, raster display, 3D analysis, and sharing — all of which connect directly to GEOG 344 and 350.

How Your Python Course Maps to Each Class

GEOG 340 — Map Design & Cartography

This is the most design-eye dependent of your four courses. Your Python work touches data processing but not visual communication — this is the gap to address.

Core Concepts to Study

Thematic Mapping Techniques Study all five types the course covers, and understand when each is appropriate: - Choropleth — area-based, normalized data (population density, not raw counts) - Proportional symbol — raw quantities, point or polygon centroids - Dot density — distribution of phenomena across an area - Isarithmic — continuous phenomena (elevation, temperature, precipitation) - Multivariate — showing multiple data dimensions simultaneously

Data Classification Methods The same data tells wildly different stories depending on how it's classified. Know the tradeoffs: - Natural breaks (Jenks) — minimizes within-class variance, best for naturally clustered data - Equal interval — simple, but sensitive to outliers - Quantile — equal counts per class, hides distribution - Standard deviation — good for normally distributed data, shows deviation from mean

Color Theory for Cartography Bookmark colorbrewer2.org — it's the standard reference and you'll use it constantly. Understand: - Sequential schemes — one variable, ordered (light = low, dark = high) - Diverging schemes — data with a meaningful midpoint (above/below average) - Qualitative schemes — categorical/nominal data with no implied order - Color-blindness safe options (always check this box in ColorBrewer)

Map Projections You've touched this in 330, but go deeper. Know the four distortion properties — area, shape (conformality), distance, direction — and which projection families preserve which: - Cylindrical (Mercator) — conformal, distorts area at poles - Conic (Albers) — equal area, good for mid-latitude regions like the US - Azimuthal (stereographic) — good for polar regions

Typography - Label placement rules (water features follow the flow, political regions use arched text) - Font hierarchy — a map should have 2–3 font sizes max - Serif vs. sans-serif conventions for different feature types

Practical Things to Do

• Daily map critiques — find a map online (news, government reports, Reddit r/MapPorn) and write 5–10 sentences on what works and what doesn't. Focus on classification, color choice, projection, and layout balance. This is the single highest-ROI habit for this course.
• Read the Axis Maps Cartography Guide — free at axismaps.com/guide. It's concise, well-designed, and covers the course topic list almost exactly.
• Practice in QGIS print layout — download some public data (Census, CalEnviroScreen, USGS) and build thematic maps in each of the five techniques above. QGIS is great for this and the open-source constraint forces you to think about the design rather than relying on defaults.
• Study historical maps — the course covers history of cartography. Look at David Rumsey Map Collection (davidrumsey.com) and try to identify techniques and design choices that have evolved.

Esri Courses to Add

Since you've already done a lot of the analytical courses, shift toward design and presentation: - Cartographic Design in ArcGIS Pro (Esri Learn) - Creating Charts in ArcGIS Pro - Working with Map Series in ArcGIS Pro

GEOG 344 — Spatial Analysis & Modeling

This is the most analytically heavy course, and your Esri Learn work has given you a real head start. You've already completed overlay, proximity, spatial relationships, and summarizing by spatial relationships — those are core topics. Now build on them.

What You've Already Covered (Don't Repeat)

• Overlay analysis (intersect, union, clip, erase) ✅
• Proximity analysis (buffers, near, point distance) ✅
• Spatial relationships (contains, intersects, within) ✅
• Summarizing data by spatial relationships ✅
• Raster display basics ✅
• 3D analysis and viewshed/line of sight ✅

Core Concepts Still to Build

Cluster Analysis - Hot spot analysis (Getis-Ord Gi*) — identifies statistically significant spatial clusters of high and low values - Spatial autocorrelation (Moran's I) — measures whether nearby features are more similar than expected by chance - Know the difference between spatial clustering of locations vs. clustering of attribute values

Distance & Density Surfaces - Euclidean distance — straight-line distance raster from a set of source features - Cost-weighted distance — distance through a resistance surface (slope, land cover, roads) - Kernel Density Estimation (KDE) — smoothed surface showing concentration of point events; understand bandwidth selection

Network Analysis - Routing (shortest path, traveling salesman) - Service areas (drive-time polygons / isochrones) - Closest facility analysis - ArcGIS Pro's Network Analyst extension is where you'll do this — spend time with the tutorial datasets

Map Algebra - Local operations (cell-by-cell math between rasters) - Focal operations (neighborhood statistics — mean of surrounding cells) - Zonal operations (statistics for zones defined by another raster) - This underpins hydrologic analysis and suitability modeling

Regression Analysis - OLS regression in a spatial context - Why standard regression assumptions are violated by spatial data (spatial autocorrelation in residuals) - Geographically Weighted Regression (GWR) — allows coefficients to vary spatially

Model Building The course emphasizes building models (automated geoprocessing workflows). Practice chaining 4–5 tools together in ModelBuilder to answer a real question.

Practical Things to Do

• Build a suitability model in ModelBuilder — pick a scenario (where should a new park go? where is wildfire risk highest?) and chain together raster operations, weighted overlay, and vector outputs. This directly mirrors the kind of assignments you'll get.
• Your Python course connection — after doing something in ArcGIS Pro, replicate the logic in Python with GeoPandas/Shapely. You won't always get identical results, but the conceptual mapping will deepen your understanding of what the tools are actually doing.
• Work through the Spatial Statistics tutorials on Esri Learn — specifically the ones covering Hot Spot Analysis and Spatial Autocorrelation. These build directly on your completed courses.

Esri Courses to Add

• Performing Spatial Statistics (Esri Learn)
• Performing Network Analysis Using ArcGIS Pro
• Understanding Map Algebra
• Introduction to Hydrologic Analysis (connects your viewshed/3D work to surface analysis)

GEOG 350 — Spatial Data Acquisition

This course is about where data comes from — acquisition, conversion, creation, and GPS collection. A very practical, hands-on subject where you can build real momentum.

Core Concepts to Build

Data Formats & Conversion You've covered this well in your Python course (Ch. 6 — GDAL/OGR). Know the practical tradeoffs: - Shapefile — ubiquitous but limited (255-char field names, 2GB limit, no datetime, multiple files) - GeoJSON — web-friendly, human-readable, always WGS 84 per RFC 7946 - GeoPackage — single-file, SQLite-based, modern shapefile replacement - File Geodatabase — Esri's format, feature-rich but proprietary - KML/KMZ — Google format, limited attributes, good for visualization

Metadata - FGDC (Federal Geographic Data Committee) standard — still common in US government data - ISO 19115 — international standard, richer schema - Know the key elements: who created it, when, what CRS, what accuracy, what's the lineage - Practice writing metadata entries, not just reading them

Remote Sensing Fundamentals - Raster concepts: bands, resolution types (spatial, spectral, temporal, radiometric) - Key satellite sources: Landsat (30m, free, 1972–present), Sentinel-2 (10m, free, ESA), NAIP (1m, USDA, aerial) - Understand what each band represents and why band combinations matter (NDVI uses NIR + Red)

GPS & Field Data Collection - Datum vs. CRS — WGS 84 is the datum GPS uses; understand how this relates to projected coordinate systems - Accuracy vs. precision — and sources of GPS error (multipath, atmospheric, satellite geometry/DOP) - WAAS/SBAS correction for improved accuracy without differential GPS equipment

Cloud Mapping Services & APIs - ArcGIS Online feature services and REST API - OpenStreetMap + Overpass API for querying OSM data programmatically - USGS National Map services

Your Python course's ETL chapter (Ch. 9) is directly relevant — data acquisition is the Extract phase.

Practical Things to Do

• Navigate real data portals — download data from:
  • The National Map (USGS elevation, transportation, hydrography)
  • CalFire FRAP (fire history perimeters — great for CA-focused analysis)
  • Census TIGER (boundaries, roads, demographics)
  • Cal-Atlas / CA Open Data
• Format conversion practice — use ogr2ogr (GDAL command line) to convert between shapefile, GeoJSON, and GeoPackage. This is exactly Chapter 6 of your Python course.
• Survey123 field collection — install Survey123 Connect, design a simple form, collect some points around your neighborhood, and pull the data back into ArcGIS Pro. This is the hands-on GPS/field collection component the course covers.
• Download and display a Landsat scene — EarthExplorer (earthexplorer.usgs.gov) is the portal. Download a scene for your area, load it into QGIS or ArcGIS Pro, and experiment with band combinations (natural color, false color, NDVI).

Esri Courses to Add

• Imagery and Remote Sensing Fundamentals (Esri Learn)
• Working with Multispectral Imagery in ArcGIS Pro
• Collecting Field Data Using Survey123
• Working with GPS Data in ArcGIS Pro

GEOG 360 — Spatial Database Design

You have the biggest head start here. Your PostgreSQL/PostGIS background from your Python course, your dev experience with relational databases and SQL, and your understanding of schema design are all directly applicable. The main gap is the Esri-specific geodatabase model, which has its own vocabulary and constraints on top of standard relational concepts.

What You Already Know That Applies

• Relational database principles (tables, keys, normalization)
• SQL — SELECT, JOIN, WHERE, GROUP BY, spatial predicates
• PostGIS spatial types, ST_* functions, spatial indexing (GiST)
• Schema design and ER modeling
• REST API patterns for serving spatial data

The Esri Geodatabase Model (Your Main Gap)

This is the proprietary layer you need to learn on top of your existing database knowledge:

Geodatabase Types - File geodatabase (.gdb) — single-user, folder-based, up to 1TB per dataset, most common in coursework - Mobile geodatabase (.geodatabase) — SQLite-based, newer format - Enterprise geodatabase — ArcGIS Server + a real RDBMS (SQL Server, PostgreSQL, Oracle) underneath

Geodatabase Components - Feature class — equivalent to a PostGIS table with a geometry column; stores points, lines, or polygons - Feature dataset — a container for feature classes that must share the same CRS; enables topology - Table — non-spatial attribute table, can be related to feature classes - Relationship class — enforces one-to-many or many-to-many relationships between tables/feature classes (like a FK with rules) - Domain — a constraint on attribute values; coded value domains (like an ENUM) or range domains; equivalent to CHECK constraints - Subtype — divides a feature class into categories, each with its own default values and domain assignments

Topology - Rules that enforce spatial integrity between features (e.g., parcels must not overlap, roads must not dangle) - Topology errors are flagged and can be corrected — this is a workflow, not just a schema concept

Schema Management - Adding/deleting fields, changing domains, versioning — these have restrictions in geodatabases that don't exist in PostgreSQL

Publishing & Sharing - Publishing a feature class as a feature service in ArcGIS Online or Portal - The REST endpoint structure of ArcGIS feature services - You've already completed "Sharing Maps and Layers with ArcGIS Pro" — build on that

Practical Things to Do

• Side-by-side schema comparison — design the same schema (e.g., a parks and facilities dataset) in both a file geodatabase (ArcGIS Pro) and a PostGIS database. Map the concepts: feature class ↔ spatial table, domain ↔ CHECK/ENUM, relationship class ↔ FK. This is the single most efficient study technique for this course given your background.
• Build a complete geodatabase from scratch — design an ER diagram, create the file geodatabase in ArcGIS Pro, add domains and subtypes, add a topology rule, and validate it. Do this for a realistic scenario (parcel data, trail network, utility infrastructure).
• Practice the full publishing workflow — build a geodatabase → publish to ArcGIS Online as a feature service → query it via the REST API endpoint in a browser or Postman. This connects your dev background to the Esri world.
• Write definition queries — ArcGIS Pro's SQL dialect for file geodatabases is limited (no subqueries, limited functions). Practice writing queries that work within those constraints.

Esri Courses to Add

• Designing Geodatabases (Esri Learn)
• Creating and Managing Geodatabases
• Building Geodatabase Topology
• Working with Relationship Classes in ArcGIS Pro

Suggested Weekly Rhythm

Given your daily availability and the fact that you're juggling four courses' worth of prep, a loose rotation works better than dedicating entire weeks to one course:

Key Free Resources

Generated May 2026 — based on ARC course descriptions for GEOG 340, 344, 350, 360 and completed Esri Learn coursework.