GeoJSON Tutorial

examples

Files Size Format Created Updated License Source
2 20kB zip geojson 11 hours ago
This is an example dataset that demonstrates how to package up GeoJSON data and display it on the map. We are using GeoJSON data for United Kingdom. Views We assume that you are familiar with what datapackage.json is and its specifications. To display your GeoJSON data on the map you should read more
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Data Files

File Description Size Last changed Download
geojson-tutorial_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 3kB zip (3kB)
example 8kB geojson (8kB)

geojson-tutorial_zip  

This is a preview version. There might be more data in the original version.

example  

This is a preview version. There might be more data in the original version.

Read me

This is an example dataset that demonstrates how to package up GeoJSON data and display it on the map. We are using GeoJSON data for United Kingdom.

Views

We assume that you are familiar with what datapackage.json is and its specifications.

To display your GeoJSON data on the map you should define path to your data inside resources and set format attribute to geojson. See example datapackage.json:

{
  "name": "geojson-tutorial",
  "title": "GeoJSON Tutorial",
  "version": "0.1.0",
  "resources": [
    {
      "name": "example",
      "path": "data/example.geojson",
      "format": "geojson",
      "mediatype": "application/json"
    }
  ]
}

Note: We are currently not supporting the TopoJSON format. You can use “Vega Graph Spec” and display you TopoJSON data using Vega specification. See our example dataset

Import into your tool

If you are using R here's how to get the data you want quickly loaded:

install.packages("jsonlite", repos="https://cran.rstudio.com/")
library("jsonlite")

json_file <- 'https://datahub.io/examples/geojson-tutorial/datapackage.json'
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# get list of all resources:
print(json_data$resources$name)

# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
  if(json_data$resources$datahub$type[i]=='derived/csv'){
    path_to_file = json_data$resources$path[i]
    data <- read.csv(url(path_to_file))
    print(data)
  }
}

Note: You might need to run the script with root permissions if you are running on Linux machine

In order to work with Data Packages in Pandas you need to install the Frictionless Data data package library and the pandas extension:

import datapackage
import pandas as pd

data_url = 'https://datahub.io/examples/geojson-tutorial/datapackage.json'

# to load Data Package into storage
package = datapackage.Package(data_url)

# to load only tabular data
resources = package.resources
for resource in resources:
    if resource.tabular:
        data = pd.read_csv(resource.descriptor['path'])
        print (data)

For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):

pip install datapackage

To get Data Package into your Python environment, run following code:

from datapackage import Package

package = Package('https://datahub.io/examples/geojson-tutorial/datapackage.json')

# get list of all resources:
resources = package.descriptor['resources']
resourceList = [resources[x]['name'] for x in range(0, len(resources))]
print(resourceList)

# print all tabular data(if exists any)
resources = package.resources
for resource in resources:
    if resource.tabular:
        print(resource.read())

If you are using JavaScript, please, follow instructions below:

Install data.js module using npm:

  $ npm install data.js

Once the package is installed, use the following code snippet:

const {Dataset} = require('data.js')

const path = 'https://datahub.io/examples/geojson-tutorial/datapackage.json'

// We're using self-invoking function here as we want to use async-await syntax:
;(async () => {
  const dataset = await Dataset.load(path)
  // get list of all resources:
  for (const id in dataset.resources) {
    console.log(dataset.resources[id]._descriptor.name)
  }
  // get all tabular data(if exists any)
  for (const id in dataset.resources) {
    if (dataset.resources[id]._descriptor.format === "csv") {
      const file = dataset.resources[id]
      // Get a raw stream
      const stream = await file.stream()
      // entire file as a buffer (be careful with large files!)
      const buffer = await file.buffer
      // print data
      stream.pipe(process.stdout)
    }
  }
})()
Datapackage.json