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Neighbourhood Income and Safety Score Map - West Yorkshire

Other articles in "Living in the UK - Housing" Section

Check score for certain area

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Calderdale is a little known region hidden between Manchester and Leeds. It contains a few valleys, heaven for mountain bikers. It's popular among Manchester commuters for its affordable house price and nature beauty. Commuting traffic is a drawback.

City of Bradford

Bradford doesn't tend to go to the candidate list when checking where to live. It has some most deprived areas around the town centre, but also some nice areas when move a bit from the town such as Baildon and Oxenhope.
City of Bradford

City of Leeds

Leeds is a hot spot for students and young professionals in West Yorkshire for its affordable housing and thriving job market. Home to over 55,000 students, people are spoilt with dining and entertainment choices here.
City of Leeds

City of Wakefield

Wakefield is increasingly popular among young professionals commuting to Leeds. Less than an hour drive to Leeds, surrounding green belt and rich cultural heritage, it is one of the happiest places in Yorkshire.
City of Wakefield


Huddersfield offers a good balance between convenience and affordability for Manchester and Leeds commuters. Like other Northern towns, its town centre requires regeneration. But check villages around like Kirkburton.

How those maps were created

  • Colour coding based on total score = safety score * 0.5 + income score * 0.5

  • Score is exponentially transformed between 0 - 100 based on ranking among all England neighbourhoods. 100 means the best performing neighbourhood in England.

  • Data Granularity: LSOA level

Raw Data Sources:

For example, Safety Score was developed following the steps below:

  1. Map all reported crime case between Dec'17 - Nov'18 into LSOA based on their lat/lng;

  2. Categorise crime in each LSOA into Violent Crime, Criminal Damage, Property Damage, Other Crime four subdomains and obtain count under each subdomain;

  3. Calculate count/population for each subdomain for each LSOA;Rank each LSOA in each subdomain;

  4. Obtain normal distributed transformed score for each LSOA at subdomain level;

  5. Summing up all subdomain scores for each LSOA;Rank LSOAs based on the summed up scores;

  6. Exponentially transformed crime scores based on ranking