Google Earth & Public Health
Instructor: Frank Boscoe, Assistant Professor, Albany School of Public Health
Research Scientist, Cancer Registry, New York State Department of Health
May 6th Morning Training Session
Time: 9AM-NOON
Google Earth is the most powerful of several free programs that can interpret KML, an
open-source language for web-based geographic visualization. KML has rapidly become
a preferred means of generating, storing, sharing and displaying geographic data. This
three-hour workshop provides an introduction to Google Earth and how it may be used in
public health programs. Some basic GIS knowledge is helpful, though not necessary.
Learning objectives:
1. Students will learn to use Google Earth to locate geographic features
and landmarks.
2. Students will learn to create, store, and share their own
geographically referenced data using Google Earth.
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Using the NYSDOH Geographic Aggregation Tool (GAT)
Instructor: Tom Talbot, Assistant Professor, Albany School of Public Health
Chief, Environmental Health Surveillance Section,
New York State Department of Health
May 6th Afternoon Training Session
Time: 1PM-3PM
Health outcome maps, with fine geographic resolution, can inadvertently disclose
confidential data. In addition, rates are often misleading due to random fluctuations in
disease rates due to small numbers. To overcome these limitations, the NYSDOH
Environmental Health Surveillance Section developed a Geographic Aggregation Tool
(GAT) which aggregates neighboring geographic areas until a user defined population
and/or user defined number of cases is reached.
The GAT requires SAS version 9.1 or 9.2 and uses GIS boundary files (shape files). The
aggregated areas are mapped in SAS or can be exported to mapping applications such
ArcGIS, MapInfo or Google Earth. The software works with census layers of geography
such as census tracks or census blocks as well as postal service areas such as ZIP Codes.
The training session will provide examples using simulated birth outcome and cancer
data coded to the ZIP code and census block level. Each user will be provided a beta
version of the GAT which can be adapted to the user's needs.
Learning objectives:
1. Students will learn how confidentiality can be compromised with maps.
2. Students will learn how to geographically aggregate health and population data to
avoid compromising confidentiality.