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2011

Shereen Iram

Roll #

10/25/2011

Assignment # 1

Applications of GIS


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Tracking Bangalore Buses on Google MapsBangalore Live Bus Tracking

This site is tracking buses of the Bangalore Metropolitan Transport Corporation live on a Google Map. The buses all carry GPS units so

the map is able to update locations once a minute.

It is possible to track all BMTC buses at once or select an individual bus to track. If you click on a bus map marker you can get the

route information and speed of the bus. There is also a mobile phone version of this map, so it is possible to work how long you willhave to wait for a bus when you are out and about in Bangalore.

This map comes from the same developer as theIndian Train Tracker.

Google Latitude Added to OrkutGoogle Latitude Orkut Application

Google have created a Google Latitude application for the social networking site orkut. The Google Latitude application lets orkut

members share their location and see their friends' locations on a Google Map inside of orkut.

If members use Google Latitude from a mobile phone, they can even send updates about their location to their friends on orkut

while they are out and about.
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Application of Geostatistical Methodsin 3-D Modeling of Coal Resources,

The creation of accurate three-dimensional models of coal beds withan associated, calculated error was a high priority of this project. To createreliable coal surfaces, we used Surfer 8, a contouring and surface mappingprogram, which allows for variogram modeling and application of krigingtheory to the elevation data collected (Figure 6).Kriging is a geostatistical gridding method for constructing aminimum-error-variance linear estimate at a location where the true value isunknown. This method produces accurate maps from irregularly spaced data,such as coal bed elevation points derived from underground mines. Krigingattempts to express trends suggested in the data, so that, for example, high

points might be connected along a ridge rather than isolated by bull's-eye typecontours. Kriging is a very flexible gridding method that can be custom-fit toany data set by specifying the appropriate variogram model. It incorporatesanisotropy and underlying trends in an efficient and natural manner.When computing the interpolation weights, the kriging algorithmconsiders the inherent length scale of the data. For example, the topographyin the Virginia Coastal Plain varies much more slowly in space than does thetopography in southwestern Virginia. Consider two observed elevationsseparated by 5 miles. In the Coastal Plain, it would be reasonable to assume a

linear variation between these two observations, while in southwesternVirginia such an assumed linear variation would be unrealistic. Thealgorithm adjusts the interpolation weights accordingly.Natural phenomena often exhibit preferred orientations. For example,coal beds in southwestern Virginia are rarely horizontal, but instead have aslight dip. Therefore, when interpolating at a point, an observation 100meters away but in a direction parallel to the strike of the bed is more likely tobe similar to the value at the interpolation point than is an equidistantobservation in a direction perpendicular to the strike of the bed. Whencomputing the interpolation weights, the algorithm incorporates this natural

anisotropRecent compilations of coal mine maps, core hole logs, and geologicmaps of the Southwest Virginia Coalfield have enabled the creation of anew three-dimensional model of the coal-bearing section in BuchananCounty, Virginia. This model is composed of 12 layers, each representing amined coal bed with its respective thickness and elevation data and minedout areas. Over 1,500 georeferenced coal mine maps and geologic maps ofthe area have yielded close to 150,000 elevation and thickness data pointsthat were used in the generation of coal bed surfaces. The coal bed


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surfaces for this model were generated using geostatistical methodsenabling error and confidence levels to be established for any location. Thehigh level of detail and spatial accuracy in this model exceeds previousattempts to generate a regional integration of coalfield data and permit abetter understanding of the local stratigraphy. Application of this modelwill not only improve estimates of the remaining coal resources, but willhave the added benefit of enhancing mine safety.Conclusion

The Buchanan County coal modeling process shows how coal minemaps and geologic maps can be used to better understand how the structureand thickness of a coal bed varies on a small scale. Applying thisinformation across areas that have been mined out will increase theaccuracy of the model as it predicts the structure and thickness of a coalbed across remaining unmined areas. This model can therefore enablebetter estimates of remaining coal resources.

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