Map a Seattle listing sheet
Turn an address spreadsheet into a listing map you can discuss with a client. This Seattle example uses 42 public OpenStreetMap address locations with fictional Active, Pending and Sold statuses, prices and bedroom counts. Open the sample in Studio, inspect a property popup, then replace the sample fields with your own reviewed data.
Start an editable copy with this data and your current map styling.
Public locations + sample listing fields
Data: public OSM address locations + simulated listing attributes. Retrieved 2026-10-25. Status, sample_price_usd and sample_bedrooms are fictional; these are not actual listings, availability, valuations or sale records. Address pins are approximate OSM nodes or building centers, not parcel boundaries. No owner, tenant or contact information is included.
Start with the example data
Download the input files, inspect their sources and sample fields, then import them or open the styled example in Studio.
- Download sample listing CSV
42 importable records with coordinates, addresses and explicitly fictional status/price fields.
- Download listing GeoJSON
The same public locations and sample listing attributes as the live example.
What to look at
- Active, Pending and Sold records have distinct colors and a matching legend.
- Click a listing marker to see its address, sample price and sample bedrooms.
- The CSV already includes coordinates; your own address-only sheet needs an explicit lookup and a review of matches.
Source columns
These fields describe the example data. CSV and Excel use first-row headers; GIS formats store geometry and feature properties. Required geometry depends on the map type.
| Column | Type | Example |
|---|---|---|
| address | text | Public OSM address in Seattle |
| latitude / longitude | coordinates | 47.6225 / -122.3375 |
| status | category | Active / Pending / Sold |
| sample_price_usd | number | 715000 (fictional) |
| sample_bedrooms | number | 3 (fictional) |
Rebuild it with your own data
- 1
Get your CSV file ready
The example fields are address, latitude / longitude, status, sample_price_usd, sample_bedrooms. For CSV or Excel, put headers in the first row. GIS files carry geometry and feature properties. Prepare any grid, catchment or route geometry before import.
- 2
Drop it into the studio
MapCrafter detects the geometry, counts the features, and shows the layers it found.
- 3
Style the imported layer
Pick which column drives colour, and which drives size or width where relevant.
- 4
Pick a basemap theme
Positron is used here. 15 other basemaps are available, including official OpenFreeMap styles. Switch using Layers & basemap.
- 5
Share or export
Publish a link, embed the map, or export a PNG at print resolution.
Questions about this example
- What file format does this sample listing locations use?
- This example illustrates CSV data. The live preview uses GeoJSON geometry. MapCrafter reads CSV directly, along with Excel, CSV, KML, KMZ, GPX, GeoJSON, TopoJSON and zipped Shapefile. No conversion step and no desktop software.
- What columns does the source data need?
- The file used here has 5 columns: address, latitude / longitude, status, sample_price_usd, sample_bedrooms. For tabular imports, supply coordinate columns, WKT or GeoJSON geometry, or address columns for an explicit geocoding step. Polygon and line files carry their geometry separately from these attributes.
- Can I use my own data with this map type?
- Yes. Open the studio, drop in your file, and MapCrafter detects the geometry and applies a starting style. Then style the layer and choose which property drives colour, size or width. The studio can render a point heatmap, but grids, routes and catchment polygons must be prepared before import.
- Can I export or share the finished map?
- Each map can be published to a short share link, embedded in another page with an iframe, or exported as a print-resolution PNG. The cleaned-up geometry can also be exported as GeoJSON. All of that is available on the free tier.
- Where does the data in this example come from?
- Data: public OSM address locations + simulated listing attributes. Retrieved 2026-10-25. Status, sample_price_usd and sample_bedrooms are fictional; these are not actual listings, availability, valuations or sale records. Address pins are approximate OSM nodes or building centers, not parcel boundaries. No owner, tenant or contact information is included.
More pin maps examples
Gallery (322)- GeoJSONKeep a property portfolio in viewSee property locations together instead of as spreadsheet rows. These fictional Seattle building records illustrate a portfolio of units or rentals, with sample inspection statuses and unit counts. Landlords can replace the records with their own portfolio; agents can adapt the fields for listings or open the dedicated Seattle listing-sheet example for Active, Pending and Sold sample statuses.
- GeoJSONExplore a Seattle site contextBuild a site-context figure from real OpenStreetMap features around a reference coordinate in Seattle. Transit platforms, education locations, parks and retail form four independent layers inside a 900 metre straight-line reference area. Toggle categories, inspect source records and open the example in Studio to prepare your own annotated report workflow.
- CSVA tree inventory as a categorical point mapPoint data coloured by species category. Categorical colour is the hardest palette to get right: two categories that look alike are not a cosmetic problem, they are two categories the reader will merge into one.
Last updated 2026-10-25