Live Data Trend Models

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The temporal data models on this website were developed by the WEACT Website Manager and Geographic Information Systems Mapping Specialist, Carlos M. Jusino, and have been exclusively established here for West Harlem Environmental Action. They take advantage of the simplest principle of a Temporal Information System (TIS): time.

A low-end TIS can be quickly adapted to web technologies without any additional software purchases. They can take advantage of any of a number of programming languages (JavaScript, CGI, PERL, etc.) that are easily integrated into standard web page designs. For years I've tried to maintain a simple HTML and pictures facade to our website without incorporating these features since their use might hinder certain individuals whose computers might not be equipped to interpret them (i.e. JavaScript disabled browsers).

A Geographic Information System (GIS) is based on a number of geographic element: images, polygons, points, and lines.

  • Images are most often comprised of satellite photos that provide a basemap for the other three elements below.
  • Polygons can be composed of ZIP codes, Census Tracts, State Boundaries, Land Parcels, etc.
  • Points can depict the locations of facilities such as bus depots, subway stations, hospitals, etc.
  • Lines can show the streets and highways, bus routes, subway routes, etc.
A Temporal Information System (TIS) is based on the fundamentals of time (months, weeks, days, hours, minutes, seconds) and any specific data that might be attributed to it. For example, in the New York City's Waste Disposal Model it is known that the city generates about 47,000 tons of trash every day, and that it has caused an estimated 264,000 outbound truck trips per year to export the trash produced. Both of the figures stated are linked to a specific time element, thus we can mathematically deduce that 47,000 tons of trash per day is the same as nearly 33 tons per minute; 264,000 truck trips per year is the same as approximately 30 trucks per hour or 1 truck every 2 minutes. These linearly derived values can be seeded into a time counting engine to count the rates of garbage production and vehicular traffic over an elapsed period of equal time.

If the general numbers known are more specific, i.e. 200,000 trucks per year during rush hour (9AM - 5PM) and only 64,000 trucks per year during off hours (5PM - 9AM) during weekdays only, then this model can be designed accordingly to increase the predicted figures at a pace that is consistent to the time of day and the figures available for that time. In this hypothetical situation, it would equate to the counter being augmented at a speed of 96 vehicles per hour during rush hour, and only 15 vehicles per hour during off hours. The counter would remain unchanged during the weekend period.

The methods described above are those that have been adopted by the Live Data Trend Models available on this website, but each model is designed differently around the information that is available to generate it.

The technologies behind GIS and TIS are not mutually exclusive; rather, they often complement one another to such a degree that TIS is not really perceived as an independent technology. A TIS is an indispensable facet of data analysis in certain GIS's, such as in their daily use in incorporating local historic meteorological data for forecasting weather conditions for a particular geographic area.

In much this same way, the Asthma Hospital Admissions Trend Model was used to analyze historic data for the previously available years of data on hospital case admissions for asthma in order to predict the values for 2003 given the continuing trend. These predicted values for the current year were then incorporated into the model's time-based incrementing counter.

This Site Affiliated With The:
Community/University Consortium
for Regional Environmental Justice (CUCREJ)

This Site Developed & Maintained By:
West Harlem Environmental Action, Inc.
Copyright © 1998-2007. All Rights Reserved.
Web Manager: Carlos M. Jusino
Content Manager: Carlos M. Jusino

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