Ogr split by attribute

Gist 745c7c03b5bf6b6f59d9

This example splits a single ESRI Shapefile into multiple new Shapefiles, one for each unique value found in a specified attribute field. It demonstrates how to use the OGR library from GDAL to read the original dataset, extract all distinct attribute values, and then create filtered copies. The process first enumerates unique values, then iterates through them to create new Shapefile layers, applies an SQL attribute filter to exclude features not matching the current value, and deletes the remaining unwanted features.

83% match

Shp download

This example demonstrates a Shiny app feature that lets users download the current map view as a Shapefile and CSV. It shows how an interactive R-based mapping interface can package its underlying geographic data for external use. The code defines a `downloadHandler` that cleans up old temporary files, uses `writeOGR` from the `rgdal` library to create the Shapefile, writes a CSV with `write.csv`, and compresses everything into a ZIP archive with the `zip` function. The handler dynamically generates the download by referencing the `getGeoContent()` object, which contains both spatial and attribute data.

48% match

Gist f3b3c7d224f1701a9d7f

This Shiny application lets a user upload a CSV file of XY coordinates, which are then read with the built-in `read.table` function and converted into a spatial points object using `SpatialPointsDataFrame` from the `sp` package. The app renders a basic scatterplot of these points via `plot()` and offers a download button that exports the coordinates as an ESRI Shapefile using `writeOGR` from `rgdal`, zipping the resulting files for delivery. The interface is built with `bootstrapPage` and `fileInput`, and it validates that data has been selected before plotting.

48% match

Shiny / R : csv to shapefile

This Shiny/R application demonstrates a straightforward conversion of CSV point data into an interactive map and downloadable ESRI shapefile. Users upload a CSV file, which is read via `read.table` and converted into a spatial points data frame using `SpatialPointsDataFrame` from the `sp` package, with optional CRS assignment commented out. The coordinates are plotted using base R’s `plot` function, while the `writeOGR` function from `rgdal` exports the data as a shapefile, which is then zipped and served through a `downloadButton` wrapped in a `downloadHandler`.

48% match

GeoJOIN

This example shows how to merge external attribute data from CSV or TSV files into a GeoJSON FeatureCollection, using a property or feature ID as the join key. It accepts multiple property files and can transform selected fields, including converting strings to numbers with a leading plus sign. The script runs on Node.js and uses the `optimist` library for command-line parsing, the `dsv` library for delimited file parsing, and standard `fs` and `JSON` modules for file I/O and serialization. The output is written either to a specified file or to standard output.

Mmbostock

Gist 9587685

This example maps user-uploaded shapefiles by plotting their polygon boundaries in a main panel. It uses R’s `shiny` framework with `server.R` and `ui.R` files, relying on the `maptools` library’s `readShapePoly` function to load the spatial data. A reactive `uploadShpfile` expression handles file renaming and directory switching for multiple uploaded components, while `renderPlot` and `plotOutput` display the resulting map. The interface provides a sidebar `fileInput` for selecting shapefiles and a central plot area for visualization.

Gist 652595e55960a8e04a29

This example visualizes the spatial relationships between metropolitan statistical areas and their constituent counties, townships, and other administrative geometries. The code first builds a combined geometry table using SQL, merging overlapping regions across multiple spatial layers while excluding special cases like Boston. It then renders these shapes using D3.js, likely leveraging `d3.geoPath` for projection and canvas or SVG drawing. The query explicitly handles hierarchical and overlapping geographies, prioritizing township-level detail where available and falling back to county unions for other areas. The final visualization shows how different government boundaries interlock within U.S. metropolitan regions, with the SQL preprocessing ensuring no duplicated or missing polygons.

CCaged

Data wrangling for ArcGIS to Cypher

This example shows how raw tabular GIS data is wrangled into a node-and-edge format suitable for importing into a graph database like Neo4j via Cypher. It extracts distinct pairs of connected points from an ArcGIS feature table by computing start and stop coordinates from X/Y fields and their offsets, then filters out self-loops where start equals stop. The code uses the XQuery language with the `fn:distinct-values`, `fn:data`, and `string-join` functions to build a comma-separated list of connected node IDs and edge lengths. It explicitly demonstrates the data-cleaning step—removing duplicate or invalid connections—before the result is ready for Cypher’s `LOAD CSV` or `MERGE` commands.

CCliffordAnderson