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Spotting noise risk zones in Karachi Pakistan :
A GIS Perspective
4. METHDOLOGICAL FRAMEWORK
The techniques described below have been applied to acquire the appropriate results. Folgert, (1995)
and Jian (1997) had applied varied GIS models and techniques for air pollution dispersion. Attempt
has been made by the authors to illuminate noise risk zones in old city / core area of Karachi. Flow
diagram demonstrating the GIS conceptual framework is illustrated as figure2.
4.1 Cartographic Techniques
To develop any GIS model it is essential to make a suitable base map in vector format so that there can
be attached attribute data to the geographical entities. Taking available Karachi metropolis guide map
(on appropriate scale) and SPOT 10m-resolution image the enclosed figure 1 for Karachi has been
produced. This map is consisting of the major infrastructure of the city urban area such as main roads,
localities, railway-line, rivers, administrative divisional boundaries and Sample points.
4.2 Database Development and Integration
One of the most important components of a GIS is the development of database. The initial stages of
GIS development entailed a need assessment. Data sets from specific purpose are taken. The most
significant available data includes geographical entities (map objects) and attribute data (intensity of
noise). To understand the noise risk zones in a better way data pertaining to peak, average and low
trends was analyzed. The database was later integrated with spatial objects on the map.
4.3 Spatial Modeling
There are a number of spatial modeling methods available with respect to application, by virtue of
efficacious GIS tools. Here the purpose of building spatial models is to demarcate the areal
distribution of the noise pollution.
4.3.1 Surface Interpolation via IDW
Surface Interpolation uses a defined or selected set of all the samples to estimate each of the output
grid’s cell values. Inverse distance weighted (IDW) interpolation determines cell values using a
linearly weighted combination of a set of sample points (Keith, 1997). The weight is a function of
inverse distance. The surface being calculated should be a locationally dependent variable. IDW
allows controlling the significance of known points, upon the interpolated values, based upon their
distance from the output point. This method provided accurate weighted interpolated surface grid as
well as isonoises.
4.4 Risk evaluation Criteria
For the purpose of spotting Noise Risk Zones (NRZ) in old city of Karachi metropolis, the following
evaluation criteria was adopted which resulted in Six Zones based on noise intensity levels.
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