UK Properties For Sale By Ministry Of Defense 2017


Files Size Format Created Updated License Source
2 162kB csv zip 2 weeks ago John Snow Labs Standard License John Snow Labs Ministry of Defence, UK

Data Files


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

Field information

Field Name Order Type (Format) Description
Original_ID 1 integer The ID which is associated in the original file with a specific property
Disposal_Status 2 string Status of disposal one the report was published
Forecast_FY 3 date (%Y-%m-%d) The year part of a financial year(FY) which represent the dead line for disposal process
Primary_Establishment_Name 4 string The name of total area (used by MOD) the property (parcel) is beloging to
Primary_Parcel_Name 5 string The name of property for sale (used by MOD)
Address 6 string The address of the property planned for disposal
Town 7 string The town on which territory the property is located
County 8 string The country where the property is located
Country 9 string The UK country where the property is located
Total_Area_Size_In_Hectares 10 number The property size in hectares (10,000 m2)
Housing_Unit_Potential 11 integer The estimated housing capacity
Constituency 12 string The electoral area/division where the property is located


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

Read me

Import into your tool

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


json_file <- ""
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# access csv file by the index starting from 1
path_to_file = json_data$resources$path[1][1]
data <- read.csv(url(path_to_file))

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

pip install datapackage
pip install jsontableschema-pandas

To get the data run following code:

import datapackage

data_url = ""

# to load Data Package into storage
storage = datapackage.push_datapackage(data_url, 'pandas')

# data frames available (corresponding to data files in original dataset)

# you can access datasets inside storage, e.g. the first one:

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('')

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

data = package.resources[0].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 = ''

// We're using self-invoking function here as we want to use async-await syntax:
(async () => {
  const dataset = await Dataset.load(path)

  // Get the first data file in this dataset
  const file = dataset.resources[0]
  // Get a raw stream
  const stream = await
  // entire file as a buffer (be careful with large files!)
  const buffer = await file.buffer

Install the datapackage library created specially for Ruby language using gem:

gem install datapackage

Now get the dataset and read the data:

require 'datapackage'

path = ''

package =
# So package variable contains metadata. You can see it:
puts package

# Read data itself:
resource = package.resources[0]
data =
puts data