Shapefile mapping Shapefile mapping
Posted: Sun Dec 22, 2024 4:46 am
The ONS boundary files website (Office of National Statistics, Open Geography Portal (statistics.gov.uk)) curates a wide range of useful geographical shapefiles from a range of different domain areas (administrative, health, political amongst others). Within this they have available shapefiles in multiple formats that represent police force areas.
Linking a shapefile to an existing variable in a FastStats system is now straightforward. In the Territories wizard we specify the shape file and the feature within the file email list providers in france that contains the ‘names’ of each of the shapes within it. Any discrepancies between the categories in the variable and the shapefile (e.g. spelling differences) are then flagged up for the user to match them up. In the screenshots below the user chooses the name field from the shapefile and then resolves any unmatched variable categories to the names in the shapefile. In this example, the system contains Scottish data, but the shapefile only covers England, so there are some categories which needed linking up, and others which cannot be visualised using this shapefile.
Then the user can drag the variable onto the map and see the breakdown of it.
Also within the data is information on casualties and vehicles involved in the accidents. Using the Mean (Number of Casualties) and Mean (Number of Vehicles) as the thematic statistics and plotting by police force area allow us to see if there any interesting geographic relationships between these variables.
From the right-hand panel we can see that there are a higher number of vehicles involved in accidents in areas with dense populations and larger motorway networks. From the left-hand panel we can see that the areas with higher numbers of casualties tend to be areas covered by more rural police forces.
Linking a shapefile to an existing variable in a FastStats system is now straightforward. In the Territories wizard we specify the shape file and the feature within the file email list providers in france that contains the ‘names’ of each of the shapes within it. Any discrepancies between the categories in the variable and the shapefile (e.g. spelling differences) are then flagged up for the user to match them up. In the screenshots below the user chooses the name field from the shapefile and then resolves any unmatched variable categories to the names in the shapefile. In this example, the system contains Scottish data, but the shapefile only covers England, so there are some categories which needed linking up, and others which cannot be visualised using this shapefile.
Then the user can drag the variable onto the map and see the breakdown of it.
Also within the data is information on casualties and vehicles involved in the accidents. Using the Mean (Number of Casualties) and Mean (Number of Vehicles) as the thematic statistics and plotting by police force area allow us to see if there any interesting geographic relationships between these variables.
From the right-hand panel we can see that there are a higher number of vehicles involved in accidents in areas with dense populations and larger motorway networks. From the left-hand panel we can see that the areas with higher numbers of casualties tend to be areas covered by more rural police forces.