You can use attribute data attached to individual layers to find out about information relating to projects within your GIS. Additionally, you can also find answers to questions regarding the spatial relationships of the features within a selection from within the layers.
This ability to query spatial relationships enables you to answer questions such as, how many airports are within a region, which rivers cross boundaries, which countries have a common border or how many cities have a population in excess of two million within a specific country.
In the same way GIS can also answer questions about health, income, crime levels and employment within a certain geography.
Looking at the London boroughs we could ascertain, with the help of GIS, which are the poorest and which are the most affluent.
Here is a map of the London wards. There are 4765 ward entries in the attribute table for this map layer.
This ability to query spatial relationships enables you to answer questions such as, how many airports are within a region, which rivers cross boundaries, which countries have a common border or how many cities have a population in excess of two million within a specific country.
In the same way GIS can also answer questions about health, income, crime levels and employment within a certain geography.
Looking at the London boroughs we could ascertain, with the help of GIS, which are the poorest and which are the most affluent.
Here is a map of the London wards. There are 4765 ward entries in the attribute table for this map layer.
If we just wanted to query data at a borough, rather than the ward level, we could use GIS to create a new layer. We could achieve this by using a geoprocessing tool to dissolve the current 4,765 wards into 33 boroughs. This results in a map layer similar to the following screen shot.
If we also download spreadsheets containing information about health, education, income and employment they could be joined to our London borough map attribute table to give us a picture of each boroughs’ performance. .
Here is a map showing crime levels across the 33 London boroughs. The darker the colour the higher the crime levels.
Here is a map showing crime levels across the 33 London boroughs. The darker the colour the higher the crime levels.
The next map shows employment prospects across London.
Employment is greatest in the lighter areas. The darker the colour the less likely for employment prospects.
The next map shows health prospects across the London boroughs.
The next map shows health prospects across the London boroughs.
Here the darker the colour the better your health prospects are.
The next map looks at income levels across London. Here the darker the colour the higher the income levels for that borough.
The next map looks at income levels across London. Here the darker the colour the higher the income levels for that borough.
The next map examines educational levels achieved across the London boroughs.
The darker the colour the lower is the educational achievement.
Looking at these maps should give an indication of which areas of London are most affluent and which are the poorest. However, we can use GIS to confirm which are the most affluent or which are the poorest across the criteria of crime, employment, health, education and income.
And here is the result of using an SQL statement within a GIS to find the London boroughs with the best average scores on crime, employment, health, education and income levels.
Looking at these maps should give an indication of which areas of London are most affluent and which are the poorest. However, we can use GIS to confirm which are the most affluent or which are the poorest across the criteria of crime, employment, health, education and income.
And here is the result of using an SQL statement within a GIS to find the London boroughs with the best average scores on crime, employment, health, education and income levels.
Out of the 33 London boroughs only three met the criteria and are highlighted on the map above.
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