Using SDS AddressBase API as a UK-wide Geocoder by Robert Spiers on 12 May 2017 -

This post shows to use Python to geocode a list of UPRNs using SDS AddressBase API.

Update

The formatting is not great on the blog so check out the post directly!

Query the API using Python Requests

Lookup a single UPRN - try this out in python console to see how it works:

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# import the requests module
import requests

# set the endpoint url to the api
url = 'https://address.digitalservices.surreyi.gov.uk/addresses'

# set the header to include authorization method and the api_key 
headers = {"Authorization":"Bearer my_secret_api_key"}

# set parameters to add to url
params = {
	"format":"all", 
	"query":"all", 
	"uprn":10007088276
}

# make the request
r = requests.get(url,params=params,headers=headers)

# view the response using requests json method
print(r.json()) # returns an unsorted list of data

Dealing with the response

To sort the returned json into something more readable use the python json module:

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import json

# print entire json response
print(json.dumps(r.json()[0], indent=2, sort_keys=True)) 

The result is presented like so:

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{
  "country": "England",
  "createdAt": "Oct 14, 2016",
  "details": {
    "blpuCreatedAt": "Sep 3, 2007",
    "blpuUpdatedAt": "Feb 10, 2016",
    "classification": "CL06",
    "custodian": "Guildford",
    "file": "part-0120.csv",
    "isCommercial": true,
    "isElectoral": false,
    "isHigherEducational": false,
    "isPostalAddress": false,
    "isResidential": false,
    "primaryClassification": "Commercial",
    "secondaryClassification": "Leisure",
    "state": "approved",
    "usrn": "16000364"
  },
  "gssCode": "E07000209",
  "location": {
    "easting": 501423.0,
    "lat": 51.2606291,
    "long": -0.5478142,
    "northing": 152276.0
  },
  "ordering": {
    "paoText": "SUTHERLAND MEMORIAL PARK",
    "saoText": "PAVILION",
    "street": "CLAY LANE"
  },
  "postcode": "gu47ju",
  "presentation": {
    "area": "Surrey",
    "postcode": "GU4 7JU",
    "property": "Pavilion Sutherland Memorial Park",
    "street": "Clay Lane",
    "town": "Guildford"
  },
  "uprn": "10007088276"
}

To print a single key:value pair:

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# first parse the response json so we can access the keys
json_parsed = json.loads(json.dumps(r.json()))

# set keys to variables
uprn = json_parsed[0]['uprn']
easting = json_parsed[0]['location']['easting']
northing = json_parsed[0]['location']['northing']

# print the values
print(uprn, easting, northing)
# returns:
# 10007088276 501423.0 152276.0

Automate Geocoding by UPRN

Here’s a script ready to go which will geocode a csv list of UPRNs, adding the easting & northing values, and appending the successful results into another csv.

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## UPRN Geocoder Script ##

## import python library
import csv, json, requests

## Read csv of UPRNs
def import_uprn():
	with open('uprn_targets.csv') as import_csv:
		reader = csv.reader(import_csv)
		import_list = list(reader)
		uprn_targets = []
		uprn_targets.extend(import_list)
	return uprn_targets

## http GET each UPRN
def query_SDS_api(uprn):
	params = {
		"format":"all", 
		"query":"all", 
		"uprn":uprn
	}
	url = 'https://address.digitalservices.surreyi.gov.uk/addresses'
	headers = {"Authorization":"Bearer my_secret_api_key"}
	r = requests.get(url,params=params,headers=headers)
	if len(r.json()) == 0:
		print(uprn + ' not found')
	elif len(r.json()) > 0:
		parse_json_response(r)
		return r

## parse json data
def parse_json_response(r):
	json_parsed = json.loads(json.dumps(r.json()))
	uprn = json_parsed[0]['uprn']
	easting = json_parsed[0]['location']['easting']
	northing = json_parsed[0]['location']['northing']
	append_result_to_csv(uprn, easting, northing)

## append to csv
def append_result_to_csv(uprn, easting, northing):
	with open('uprn_geocoded.csv', 'a') as outcsv:
		writer = csv.writer(outcsv, delimiter=',', lineterminator='\n')
		writer.writerow([uprn, easting, northing])
		print(uprn + ' appended to csv')

## Run the geocoder
def Geocode():
	uprn_targets_tuple = import_uprn()
	uprn_targets = [i for sub in uprn_targets_tuple for i in sub]
	for uprn in uprn_targets:
		query_SDS_api(uprn)
	
Geocode()

To use it copy the contents into a file called

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Geocoder.py

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my_secret_api_key

Create a csv of UPRNs called

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uprn_targets.csv

Open a command window in the directory which has the

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Geocoder.py

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uprn_targets.csv

c:\python27\python.exe Geocoder.py

Here you can see I used python 2.7 but the script works with python 3.5 too.

Example Results

Results will be printed to the cmd window and a csv file

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uprn_geocoded.csv

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Geocoder.py

Here’s an example of the response printed to the command window. Notice some fake UPRNs to show the report when they are not found:

V:\Projects\sds_api>c:\python27\python.exe Geocoder.py
100061380353 appended to csv
100061380354 appended to csv
100061382605 appended to csv
100061382614 appended to csv
100061382615 appended to csv
10006138261 not found
100061382616 appended to csv
100061382617 appended to csv
10006138261 not found
100061382618 appended to csv
100061382619 appended to csv
10006138262 not found
100061382620 appended to csv
100061382621 appended to csv

Here’s the

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uprn_geocoded.csv

100061380353,501535.07,152651.47
100061380354,501531.29,152648.93
100061382605,501501.0,152525.0
100061382614,501467.0,152515.0
100061382615,501519.0,152498.0
100061382616,501451.53,152499.74
100061382617,501513.46,152498.45
100061382618,501449.88,152494.31
100061382619,501499.37,152493.56
100061382620,501450.46,152490.07
100061382621,501489.52,152487.36

Adding Other Key:Value Pairs to the Geocoder

Add other key:value pairs from the response by modifying the

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parse_json_response()

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Geocoder.py

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append_result_to_csv()

Example adding postcode to the csv writer:

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## parse json data
def parse_json_response(r):
	json_parsed = json.loads(json.dumps(r.json()))
	uprn = json_parsed[0]['uprn']
	easting = json_parsed[0]['location']['easting']
	northing = json_parsed[0]['location']['northing']
	postcode = json_parsed[0]['postcode']
	append_result_to_csv(uprn, easting, northing, postcode)

## append to csv
def append_result_to_csv(uprn, easting, northing, postcode):
	with open('uprn_geocoded.csv', 'a') as outcsv:
		writer = csv.writer(outcsv, delimiter=',', lineterminator='\n')
		writer.writerow([uprn, easting, northing, postcode])
		print(uprn + ' appended to csv')

What Next? Some ideas of how to use this

• Use the UPRN to join values extracted from the geocoding back to your data.
• Instead of writing to csv create a function to update the results back into your data. To connect python to PostGIS use psycopg2.
• With QGIS 2.16 you can run this script direct from the python console. The requests, json, and csv modules are installed by default. Really easy for GIS users to have a UK wide UPRN AddressBase Geocoder now.

Automated map production with QGIS by Robert Spiers on 5 Sep 2016 -

This post shows how to use QGIS and python to automate map exports to PDF.

Purpose

I needed to create create standardised PDF maps on a regular basis. For this I usually use a template set up in QGIS then export PDFs using the atlas feature. However… I found this can be entirely automated so you don’t even need to open the QGIS program to export the product. This can be used to automate map production as a scheduled job or batch produce maps when new data is available.

How it works

Using python the QGIS libraries can be imported and executed. This process can be triggered in windows using a batch file which runs the python script. There’s some guidance for linux too in the super useful qgis cookbook.

I modified the python script from Tim Sutton to be specific to my qpt template. I also added a way to specify coords to focus the map canvas on and apply a filter to a layer. Layers which are added to the

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.qgs

I thought this was worth saving and sharing so the files are available here with an explanation of how to get these example files working on windows in the readme.

Example uses

• Use psycopg2 to check for new data to export to PDF.
• Add more layers to the

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  .qgs

  file or load and style the directly in the script. They will be printed to the PDF.
• Modify the script to accept variables and make a map requester with FME

Improving your tertiary classifications with OS MasterMap and FME by David McDermott on 4 Aug 2016 -

In 2012, Northumberland County Council made a submission to GeoPlaces’ Citizen Award for their work in identifying properties. While there submission did not win, it was highly commended, and it certainly caught my eye. I was particularly interested in how they used building polygons in the OS MasterMap Topographic Layer to determine tertiary BLPU classification. This outcome is achieved in two phases. First, determine the relationship between the building polygons, then assign these polygons with a UPRN.

Commodity GIS by James Rutter on 11 Mar 2016 -

…so at Surrey Heath, we (the GIS team…all two of us) find ourselves in the nice position of knowing a bit about data and how to handle it. By handle it I mean how to obtain it, how to transform or do something with it and how to put it somewhere else or publish it.

QGIS Rooftop Aspect Analysis by Robert Spiers on 3 Mar 2016 -

This post shows how we have used lidar data to calculate the aspect and estimate the south facing area of a rooftop.