GIS and Remote Sensing

Geographical Information Systems (GIS) is a technique used to analyze data that is connected to a specific location. Shelter Associates uses GIS to integrate mapping and socio-economic data that can be analyzed spatially and becomes critical in planning for the poor. This data includes overall census information as well as information about infrastructure and access to facilities such as toilets and water etc. The socio-economic data that is collected includes employment, caste, education and family size. This information can be graphically overlaid onto the remote sensing image to get a slum-wide or city-wide view of the data. Below are three examples, for Pune, Sangli and Solapur, of how Google Earth can be used to present GIS data. Several slums are outlined, the blue bubbles contain information about the slums, and the yellow bubbles contain information as well as a link to a detailed factsheet (pdf) for that slum.

Why Shelter Uses GIS

Shelter Associates pioneered the use of GIS in the late 1990's for poverty mapping. The broad objective was to create critical spatial data about the degraded areas that would aid inclusive city planning. Moreover this helped validate the existence of the poor in the city.

Examples of GIS use with Slums

In order to use the interactive Google Earth tool, you will need to install the Google Earth Plugin. The Google Earth Plugin is currently supported in the following web browsers: Google Chrome, Firefox and Internet Explorer. Click on the image to open the Google Earth window and follow the instructions to install:

Pune

Pune has around 560 slums split up into 14 wards. This is an example of the remote sensing of the three slums in Bhawanipeth. Some GIS data ia available in the yellow bubbles.

This is an example of a water supply GIS query for Chitraban in Pune. Click on the image to see an enlarged view. You can see the primary water supply for each household in the slum. 366 houses have their own connection (dark blue):

In addition, this query shows the house type for each house in Chitraban:

Sangli

Sangli had 78 slums. This is an example of the remote sensing of five of these slums. Indira Nagar Zopadpatti is the largest slum in Sangli. Some GIS data is available in the yellow bubbles.

This is an example of a caste code GIS query for Indira Nagar Zopadpatti in Sangli. Click on the image to see an enlarged view. You can see the caste of the occupants in each house. For example, light blue represents Scheduled Caste (SC) with 413 houses in Indira Nagar Zopadpatti:

In addition, this query shows whether or not each household owns a vehicle:

Solapur

Solapur has 220 slums. This is an example of the remote sensing of four of these slums: Bagle Vasti, New Jagjivanram, Rahul Gandhi Nagar and Bhagwannagar. Some GIS data is available in the yellow bubbles.

This is an example of a sanitation GIS query for Bagle Vasti in Solapur. Click on the image to see an enlarged view. You can see the toilet type for each house in the slum. For example, 654 houses use the public toilets in Bagle Vasti (light blue):