A peep into a different world- Dharavi

The use of Geographic Information Technologies (GIT) such as
high resolution satellite imagery and aerial photographs
at scale 1:10,000 is very helpful in going deep interior to the slum like dharavi.
This approach could be an alternative to overcome the
lack of conventionally available data for local upgrading strategies.


　
The approach offers several advantages
more technocratic approaches such as:
1. its timeliness and low cost;
2. the increased ownership of the data collection process and
the revealed problems ; and
3. a greater ease of embedding locally generated information
in institutions such as city and sub-city authorities and
community-based organisations (CBOs) .
This region was selected to demonstrate the different levels
of information that can be obtained from local knowledge in
combination with satellite imagery and aerial photos.
Acquiring comprehensive slum information for slum improvement
in cities like Mumbai is entwined with issues of
extreme resource constraints, data limitations and the heterogeneous
characteristics of the city.
Here appraisal techniques, integrating local knowledge with GIT
using a participatory approach is being employed.
This approach is preferable as it is cheap to build, easy to use,
robust and flexible in its application. Through focus group discussions,
direct field observation, and visual image interpretation
complemented by secondary data we are able to generate
both spatial and non-spatial information on slums in the form
of thematic layers in a GIS environment.
　
As you can see in the given maps of dharavi which shows the deep interior of different locations separated by a boundary.
You can easily distinguish one region from another and can carry out study of different locations with much ease.
In the other image in which the roads flowing across the densily populated dharavi are shown.
This type of satellite imagery is very helpful in developing the slums like dharavi because its not easy to locate different places easily due to its high compactness and very high density of population.

Visual image interpretation
Image interpretation was very effective for capturing data
that could not be easily captured using field observations or
in focus groups. The focus group discussions revealed key
visual image interpretation elements for slum identification
and delineation (i.e. irregular street and building pattern and
small, densely distributed houses). As DHARAVI has been
growing spontaneously without any significant guiding plan
or standards for many years, irregular layout and high density
are two key manifestations of poor living conditions in the
built environment of the city.
Based on two elements of image interpretation pattern
and size, we identified groups of buildings with an irregular
layout and lacking open space (as shown in the image below). We also used
the QuickBird image and aerial photographs when clarification
was needed. This process of data capture has helped in
filling the data gaps that remained after the focus groups and
field observation (e.g. due to poor accessibility).