What Actually Goes Into a Geography Template
A Quick Geography Template is just a structured layout you drop your spatial data into so you stop reinventing the file format every time someone asks for coordinates, boundaries, or regional breakdowns. The name sounds slicker than it is. It is not a software package. It is a set of column headers, a few validation rules, and a way to keep latitude, longitude, timezone offsets, and geopolitical codes from drifting apart when three people edit the same workbook. I was wiring up a regional sales report for a logistics client and someone handed me a sheet that called itself a Quick Geography Template. Five hundred rows, no headers on columns four through twelve, and three separate coordinate formats mashed together — decimal degrees, DMS, and something that looked like UTM but wasn't. I spent twenty minutes figuring out which numbers were lat and which were lon before realizing the file had been edited on an Excel for Mac and a Windows box and they both used different decimal separators. The workaround was to strip every column down to a single format, force a locale lock with LC_ALL=C on the export side, and add a silent validation pass that rejected any value outside the valid ranges. Lat between -90 and 90. Lon between -180 and 180. If either slipped, the row went into an error sheet instead of the main dataset. That saved us from shipping garbage to the mapping engine. You start with the mandatory fields. Everything else is nice to have. The minimum set that actually keeps projects from falling apart is:
Record ID — a stable, non-moving primary key. Do not use the address or the place name. Those change. The ID does not. Latitude and Longitude in decimal degrees. One field each. No combined fields. Combined fields are a trap that bites you when you need to filter by hemisphere or run a distance calculation later. Coordinate System — a text field naming the datum. WGS84 is the default assumption, but assuming it without stating it is how people end up using NAD83 data in a WGS84 pipeline and wonder why everything is off by a few meters.
Place Name — the human-readable label. Keep it short. Long place names break layout engines in GIS viewers and make printed reports ugly. Admin Levels — at least country and first-level subdivision. ISO 3166-2 if you want to be clean about it. Otherwise two free-text columns labeled admin1 and admin2 will work until they do not. Timezone — IANA name, not offset. The offset changes with daylight saving. The IANA name does not. America/New_York is safer than UTC-5 when you are dealing with historical data or future projections.
Get the Full Details
Source — where the data came from. This is the field everyone skips and then nobody trusts the dataset. Put the provider, the version, and the retrieval date in one cell. It takes six seconds and saves six hours later.
Columns that feel optional but are not
Elevation comes up constantly. You think you do not need it until you are dropping points into a flight simulator or a terrain analysis and every point sits at zero meters because nobody filled the column. Add it early. Same thing with confidence score. When you merge datasets from openstreetmap, geonames, and a proprietary CRM, you need a way to flag which records are high certainty and which are guessed. A simple integer from one to ten works fine. Do not overthink it. Geometry flag is another quiet winner. Boolean. True means the point has a clean coordinate pair. False means you have an approximation, a centroid, or a fuzzy match. This field alone cuts your error rate in mapping queries by roughly half because consumers stop treating every row as equally reliable.
Validation rules that actually matter
Range checks on coordinates are obvious. Beyond that, you need: A check that lat and lon pairs resolve to land or water, not mid-ocean when the place name says Switzerland. A simple reverse geocode pass against a public boundary dataset catches the stupid mismatches. I usually run it with Natural Earth 10m boundaries because it is fast enough for a few thousand rows and small enough to download without asking permission from anyone. A uniqueness check on the combination of admin1 plus place name. Duplicate entries at the regional level are how you get two records for Bavaria, Germany that conflict on timezone.
A timezone consistency check. If two points sit within fifty kilometers of each other but claim different timezones, flag it. Half the time it is a real administrative anomaly. The other half it is a data entry error.
The edge case nobody writes about
Kazakhstan and Indonesia both cross the international date line in ways that make timezone assignment weird. Kazakhstan switched from three timezones to one in 2024. Indonesia has three IANA zones that overlap with older data sources still publishing two. If your template does not allow for timezone revision history, you will ship stale offsets to downstream systems and get blamed for scheduling errors. Add a timezone note column for cases like this. Three words there prevent three support tickets. CSV is the default. It works everywhere. But if you are moving more than a thousand rows between team members, use GeoJSON. The overhead is tiny and you get geometry baked into the file instead of hoping the consumer remembers to interpret the lat/lon columns correctly. For internal dashboards, stick with CSV and a sidecar schema file that documents every column. The schema file is what lets a new analyst join the project without asking you seventeen questions about what admin_ref_code means. If you are feeding a PostGIS database, export as a tab-separated file with explicit NULL markers. PostgreSQL's COPY command handles TSV better than CSV when you have mixed types and occasional empty cells. I have seen people try to import messy CSV into GeoServer and watch it reject half the rows because a trailing comma tripped the parser. Tab separation avoids that.
When this approach breaks
It breaks when you need polygon or linestring geometry. Point-only templates cannot represent a county boundary, a river, or a delivery route. If your use case requires area or distance calculations across non-point features, move to a full GIS schema with a geometry column and a supported CRS. Quick Geography Template is not designed for that workload. It also breaks under high merge frequency. If five teams are editing the same file and pushing changes hourly, you need version control or a database. Spreadsheets do not merge well. Conflict resolution in Excel is basically a guess. Use a lightweight Postgres table with row-level locking or switch to a tool like Overpass Turbo if you are pulling from OpenStreetMap repeatedly. Lastly, it struggles with multilingual place names. If your data needs to support Chinese, Arabic, and Latin scripts for the same location, you need separate name columns per script instead of one generic Place Name field. I learned that the hard way when a Shanghai warehouse team could not find their own depot because the romanized name did not match the local label in the routing system.
A practical download structure
If you want a starter file you can drop into a project today, build it as a single workbook with three sheets. Sheet one holds the raw data. Sheet two holds the validation log with row numbers and error descriptions. Sheet three holds the schema documentation with column names, data types, allowed values, and examples. Name the file quick_geography_template_v1.xlsx and include a README.txt in the same folder describing the version, the intended use, and the last update date. That README is what stops people from using a 2022 template in a 2026 project and wondering why the timezone column is wrong. The template works because it forces discipline early. You spend an extra ten minutes setting up headers and validation rules and you save an afternoon of debugging coordinates that ended up in the wrong place. That is the actual value proposition. Nothing dramatic about it.
