icegov2012 proceedings

2. OVERVIEW OF DISTRICT MAPPING
The mapping of the Sample Electoral Districts in New York State
creates a number of districts on coordinate plain by taking into
account a number of user controllable weights on variables. As
shown in Figure 1 various data are collated and mapped to create
districts. The mapping algorithm requires that the data that is
being used have the ability to be in a coordinate plain. The
original data that was being used contained 13 million data points,
in which the location was an Address. Dan Goldberg provides a
service, geocoding, which is able to convert the data from
addresses to latitude and longitude. We expounded on the data by
later adding, racial make up, economic factors and assessed house
values.
The mapping uses a Recursive Bisection algorithm, which creates
rectangles on a coordinate plain according to user-weighted
variables. Recursive Bisection is a graph, in the demo the graph is
a set of data points on a coordinate plain, portioning method in
which equal points are assigned to either side of a bisector, in the
demo we modify the algorithm to take into account minority and
political affiliation. A total of two and half million plus data
points are used to construct this mapping. Which is lower than the
total amount of registered voters because of diminishing returns
from using more data points. The Recursive Bisection Algorithm
being used in the Mapping uses the address of registered New
York State voters; the addresses are converted into a Latitude and
Longitude format that is used in a coordinate system. Each
coordinate has a certain amount of data connected to it. Including
County data on racial makeup of the county, GDP Per Capita and
unemployment the point itself stores the political affiliation and
cost of the house, if available.
Figure 3. An early user interface utilizing Google Maps
The output is then shown in an interface like that in Figure 3.
Data for a district is outputted when a user clicks on a district.
Allowing for the user to explore what the algorithm has created.
To evaluate the demo, according to the United States census there
are roughly 19.5 million citizens of New York State. The Demo
uses about 13 million data points. Which would mean the
majority of all New York State citizens are taken into account
even with migration. The Algorithm produces only rectangular
shapes, which may fail to take into account political and natural
barriers. However, because the algorithm group’s data points
together these shapes usually avoid doing so. The rectangular
shapes may be very oblong, but this is not necessarily good or
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bad, for example, many of the runs of the algorithm produced a
long, slender district in the north east portion of New York, which
may seemingly be a obstruction to proper political management,
until the geographic location of the northern portion of the
Hudson and Champlain Valley’s are taken into account.
3. DATA
Economic, Voter Registration, and Minority data are accessible at
varying granularities for the State of New York. The mapping of
Electoral Districts utilizes voter registration data collected from
the New York State Board of Elections. The address of registered
voters in the state of New York were transformed to latitude and
longitude points by Dan Goldberg 2 at the Spatial Sciences
Institute, USC Dana and David Dornsife College of Letters arts
and Sciences, University of Southern California. Economic data is
also utilized in the mapping an index is created from the county
unemployment and GDP Per Capita. The US census has the data
for governments minority data, it also has political boundaries and
shape files. This creates areas where the racial makeup of a point
can be guessed. These averages can later be taken into account.
4. CONCLUSIONS AND FUTURE WORK
We demonstrated an open data approach to creating Electoral
Districts by using Recursive Bisection in Mapping. Data points
where the registered voters of New York. Economic and Racial
Data added to scope of controllable variables in the Mapping
A number of extensions to the mapping algorithm and the sources
of data used are ongoing. The leveraging of semantic
technologies, such as a owl ontology and storage of the voter
registration data. Average personal and business income and
religious affiliation would increase the base of data being drawn
on. If the mapping toke into account current political boundaries it
would be further improved, this could be done using census
boundaries and shape files for towns and counties.
5. ACKNOWLEDGEMENTS
The authors would like to thank Jim Hendler, Tetherless World
Constellation at Rensselaer Polytechnic Institute, Dan Goldberg 3 ,
University of Southern California, and Francine Berman,
Rensselaer Polytechnic Institute and Aaron Tobais, Rensselaer
Polytechnic Institute.
6. REFERENCES
[1] Vickrey, William. On the Prevention of Gerrymandering.
Poltical Science Quarterly, 76, 1 (Mar. 1961), 1
2 https://webgis.usc.edu/
3 Dan W. Goldberg PHD, Spatial Sciences Institute, University of
Southern California, dwgoldbe@usc.edu