GIA Core Functions
The GIA Core provides services in support of population science at
Penn State. Our services emphasize spatial statistics, advanced spatial
analysis methods, exploratory spatial data analysis and customized
GIS/spatial analysis programming. In addition the GIA Core provides
service in support of the collection of intensive longitudinal geospatial
data and the building of contextual and ecological databases as well
as the traditional services associated with mapping/cartography (including
web-based mapping) and geospatial acquisition, archiving and management.
These core service functions are briefly introduced below, including
examples from recent and ongoing research projects. PRI faculty affiliates
are identified in bold.
Function 1: Expert advice on geospatial data and advanced spatial
analysis.
One of the most important contributions that the GIA Core has
made to advance the knowledge in population science is to provide
expert advice on geospatial data and advanced spatial analysis. Concepts
such as spatial dependence, nonstationarity and scale are not new
but within demography there has been relatively little emphasis on
spatial thinking and the use of explicitly spatial methods. The GIA
Core has been at the forefront of training in spatial methods (see
below) and has had some success advocating explicitly spatial approaches
in some areas of population studies. Matthews (GIA
Core Faculty Director) uses personal communication and the Research
Activity Tracking System (RATS) to identify and approach PRI researchers
planning grant submissions. Early in the proposal development process,
both Matthews and Yang meet with
researchers to discuss proposal-related scientific, personnel, workload,
budget and timing issues. These discussions help PRI researchers identify
where appropriate the potential in their study design for adding a
spatial component or advanced spatial analysis. That is, are there
research hypotheses that could not be examined without a spatial perspective?
Furthermore, the GIA Core provides advice in creating or acquiring
geospatial data, including discussions of privacy and confidentiality
concerns with the use of geocoded data on individuals and institutions.
All the GIA Core staff have completed the IRB training at Penn State.
Function 2: Training in advanced spatial methods.
Since its establishment, the GIA Core has continually emphasized GIS
training for population scientists at Penn State and elsewhere. Matthews
has coordinated GIS workshops at national conferences (PAA) and locally
at Penn State. Matthews received funding through
two R25 grants promoting the use of GIS in population science and
in advanced spatial analysis respectively (see http://csiss.ncgia.ucsb.edu/GISPopSci/about/proposal.php).
Both R25’s involve collaboration with the Center for Spatially
Integrated Social Science (UC Santa Barbara). The GIA Core hosts week-long
workshops at Penn State and facilitates others in Santa Barbara on
the following topics: spatial regression modeling, geographically
weighted regression, the integration of multilevel and spatial modeling,
and spatial pattern analysis. The objective of these workshops is
two-fold. One is to provide intensive training for young researchers
with a special interest in incorporating a spatial perspective into
their current or future projects. The other is to develop resource
materials on advanced spatial analysis methods that can be accessed
by not only the workshop participants but also by a broader audience
in population science. In addition, the GIA Core has offered targeted
workshops for PRI faculty and students interested in both substantive
demographic and methodological issues. Graduate students in the Demography
Training Program are exposed to GIS, spatial analysis, and statistical
methods through a course taught regularly by Matthews
(Demography 597: Spatial Demography) and via guest lectures by Yang.
In addition, the GIA Core routinely offers a variety of workshops
and/or software demonstrations. GIA Core workshops continue to be
offered on average three times each semester, focusing on both basic
and practical use of ArcGIS 9.x as well as the specialized extraction
of census data and boundary files. Moreover, the GIA Core staff have
authored and maintained a collection of GIS Resource Documents via
the website (http://www.pop.psu.edu/gia-core/gis_rd_listing.htm).
These documents offer abundant information on numerous GIS-related
concepts and methods and are extremely useful to non-GIS experts.
Function 3: Advanced spatial statistics.
The GIA Core has kept abreast of the latest trends in spatial statistics,
including, but not limited to, spatial econometrics in MATLAB, spatial
analysis packages in R, WinBUGS, GeoBUGS, GeoDa, GWR, CrimeStat, and
STARS (see http://www.pop.psu.edu/gia-core/software2.htm)
Advanced spatial statistical techniques range from constructing spatial
weights according to adjacency and/or geographic distance for sophisticated
regression models, i.e. conditional autoregressive (CAR) and simultaneous
autoregressive (SAR) modeling. These techniques allow for appropriate
model specification and can lead to new research questions and directions.
For example, the growing interest in using locally linear spatial
modeling (i.e. GWR) can provide novel insights. Drs. Matthews
and Yang are experienced in applying advanced spatial
statistics (spatial econometrics, geographically weighted regression,
Bayesian methods), have collaborated with other researchers (e.g.,
Jensen and Ruback), and co-authored several research
papers that shed new light on studies of mortality, self-rated health,
stress, and crime. Several faculty with projects or proposals that
are in development or under review have consulted the GIA Core (e.g.,
Bodovski and Greenman; Yang, Haran,
and Matthews; Weisman and Hillemeier; Iceland,
Lee, Firebaugh and Matthews) about the contributions of advanced
spatial statistics. We anticipate growth in the use of advanced spatial
statistical methods by PRI faculty and affiliates. Drs. Matthews
and Yang will work closely with others on campus
including geographers and spatial statisticians to provide both appropriate
methodological advice and access to state-of-the-art software to faculty.
Function 4: Exploratory Spatial Data Analysis (ESDA).
The traditional approach to exploring geospatial and/or ecological
data is to describe the distribution of the variable of interest with
numbers, such as mean or standard deviation. However, in so doing,
other important features, such as spatial association within each
variable or among variables, may be overlooked. The objectives of
ESDA are to detect spatial association in data and assess their fitness
for advanced spatial modeling. The ESDA results are intended to allow
researchers to observe what may be happening over and above what has
already been known or described. ESDA embraces a range of techniques
to visualize data, capture spatial autocorrelations, reveal spatial
clusters, and offer insight into complex models. Specifically, using
geographic information systems (GIS) software to visualize geospatial
and/or ecological data may reveal spatial trends that are not obvious
through traditional nonspatial approaches. ESDA can further determine
if spatial dependence is a problem existing in the data by calculating
various global measures of spatial autocorrelation, i.e. Moran’s
I or Geary’s C. Should spatial dependence be found, a detailed
spatial clustering pattern can be generated with the local indicator
of spatial association (LISA). The GIA Core staff is versed in the
ESDA software, e.g. GeoDA, and the spdep package in R, and hence can
provide assistance at different stages of proposal development and
analysis. Collaborating with Matthews and Yang,
several PRI faculty members have integrated ESDA into their research
(Jensen; Ruback).
Function 5: Intensive longitudinal geospatial data collection.
Human activities or events occur in a specific spatial and temporal
context. The past decade has witnessed a reemergence of time geography,
which leads to an increasing demand for intensive longitudinal geospatial
data collection. The objectives of obtaining space-time data are to
describe the trajectory of a subject in physical space over time,
to summarize the trajectory patterns, and to predict future space-time
movement. The GIA Core has invested in new tracking technologies that
may facilitate efficient demographic fieldwork, such as GPS and GPS-enabled
PDAs. GPS devices have been utilized to support fieldwork in the U.S.
(Burton and Matthews) as well as international field
research by faculty in Anthropology (Johnson and
Wood), Agricultural Sciences (Findeis)
and in Human Development and Family Studies (Jayakody).
Recently, the GIA Core has developed several PDA-based data entry
applications for the study of food environment and obesity (Matthews)
and will be working with faculty for applications in international
fieldwork. Some of the latter projects are interested in the integration
of network and spatial data. Recent development in information and
communication technologies, i.e. cellular phones and internet have
been used as tools for collecting longitudinal geospatial data. As
part of an NSF-IGERT application, Matthews has collaborated
with others on campus in Computer Science and Engineering and across
the social sciences on data collection via mobile computing, sensor
networks, and wireless networks. This core function service will likely
generate as many challenges as opportunities in areas such as research
design, data collection, IRB issues, as well as analyses associated
with massive amounts of new types of data.
Function 6: Customized programming (including Web-based Mapping).
The GIA Core provides customized programming for funded projects.
For example, an ArcGIS script or tool was developed for the Measuring
Spatial Segregation Project (PI, Reardon at Stanford, Matthews,
Firebaugh, and Lee). The Spatialseg tool
allows researchers to calculate several spatial segregation indexes
for nominal (race/ethnicity) and ordinal (income) variables. The Spatialseg
tool is free and available for download from the project website (http://www.pop.psu.edu/mss/)
along with technical documentation and sample data. The GIA Core staff
provides full technical support to scholars who are interested in
applying this tool to their own research. The GIA Core has experience
in web-based mapping applications. Recently we have worked with the
Prevention Research Center at Penn State (Bumbarger) utilizing ESRI’s
ArcServer to create online interactive mapping applications to view
and query their data. With respect to statistical programming, the
GIA Core has contributed significantly to projects by implementing
sophisticated spatial and multilevel modeling. We have utilized Bayesian
approaches to understand the impact of spatial structure on property
crime and geographically weighted regression to explore the associations
between food environment and individual obesity status. In the future,
the GIA Core will provide customized programming to projects associated
with one of PRI’s signature themes on Changing American Neighborhoods
and Communities (Iceland et al.).
Function 7: Building Contextual and Ecological Databases.
Often, pre-award projects led by PRI faculty ask the GIA Core to extract
data from a variety of different data sources, including but not limited
to the Census, ESRI Street Map, and the Area Resource File. Some demographers
are interested in obtaining such data for ecological modeling. GIA
Core personnel regularly create ecological and contextual data to
this end. For example, the National Education Longitudinal Study (Bodovski
and Greenman), National Health Study (Rovniak),
Incarceration, Segregation and Health project (Massoglia),
Health Care Study (BeLue), and Family Life Project (Matthews)
all made use of ecological data prepared by GIA Core staff. In addition
to extracting data from existing sources, the GIA Core is often utilized
to build/create hierarchical (both individual and contextual level)
databases for funded projects through geocoding. Several projects
conducted their own surveys and obtained respondents’ addresses
(e.g. Neighborhood Food Environment Project (Matthews))
and the National Children Study-Westmoreland County, PA site (PI,
Cauley at U. Pitt with Weisman, Hillemeier and
Matthews at Penn State). The GIA Core converts the non-spatial
information into spatial points by matching the addresses to street
data. These individual-level spatial point data can be further embedded
into higher areal units (e.g., census tracts, school districts, counties
etc.) resulting in a hierarchical database. Beyond individuals’
addresses, the GIA Core is also experienced in geocoding institutional
data, such as on schools (Kempinen), alcohol outlets (McCarthy and
Matthews), and health facilities (PI Anderson at
Penn State Hershey with Weisman, Hillemeier, Matthews and
Yang). These contextual/ecological and hierarchical databases
enable sophisticated spatial and multilevel analysis, which helps
to advance the knowledge in population science.
Function 8: Mapping/Cartography.
One of the strengths of the GIA Core is in visualizing data for PRI
researchers for a variety of end-products (e.g., publications, presentations
and posters, and proposal development). The objective of mapping is
to visually demonstrate how a set of attribute data is distributed
across a research area. Mapping is regarded as a powerful way to convey
information about a single theme as related to research questions
or on a focused population. Many PRI researchers have consulted GIA
staff regarding the optimal ways to display their data cartographically.
Some projects have extensively used this core service, such as the
Measuring Spatial Segregation Project, the Neighborhood Food Environment
Project, and the North Orkney Population History Project (Wood,
Johnson, Murtha, and Matthews). In contributing
to the National Children’s Study the GIA Core developed numerous
cartographic products that provided new insight into the makeup and
functioning of communities within the project’s research area
and ultimately generated a three volume atlas of reference maps paired
with satellite images to assist in validating fieldwork data. In the
future, we anticipate extending our mapping experience from static
to dynamic geovisualization and establishing closer links with the
Penn State’s GeoVISTA Center.
Function 9: Geospatial Acquisition, Archiving and Management.
OThe GIA Core regularly updates its holdings of public domain and/or
commercial geospatial data pertaining to the U.S. Census (current
and historical) as well as data on education (National Center for
Education Statistics), health (including SEER data), crime (FBI Uniform
Crime Report data), and transportation (TeleAtlas). In addition to
maintaining these nationwide datasets, the GIA Core also helps PRI
researchers to acquire geospatial data specific to their projects.
For example, we have integrated land-use and public transportation
data to explore the role of bus ridership in property crime. During
pre-award stages, researchers may find geospatial data via the GIA
Core webpage (http://cairo.pop.psu.edu/gia/linksnew.cfm?type=Data).
If the geospatial data are not accessible or readily available, the
GIA Core can facilitate searches and if necessary scan and digitize
data elements as needed. With respect to data archiving and management,
the GIA Core houses geospatial data for all PRI projects using its
services. Geospatial data are unique and therefore not like the demographic
databases stored in PRI's data archive. All geospatial data are stored
on a secure, firewall protected server. The GIA Core’s well
managed data archives allow PRI faculty to retrieve their data anytime
when needed.
For more information on the GIA Core and the services provided please
contact one of the people listed below:
Dr. Stephen Matthews (Faculty Director) is available
for consultation with faculty about how GIS may contribute to their
research and should be the first point of contact regarding all GIS-related
requests and activities. Stephen can be reached via e-mail at matthews[at]pop.psu.edu
or at ext. 3-9721. His office is 507 Oswald.
Dr. Tse-Chuan (TC) Yang (Research Associate in Spatial Social
Science and research affiliate of PRI) is the point of contact
regarding technical background and the application of advanced spatial
analysis methods and spatial statistical software. TC can be reached
via e-mail at tuy11 [at] psu.edu or at ext. 5-5553. His office is
803 Oswald.
Mr. Dan Meehan (GIS Manager) is the primary point
of contact regarding GIS technical issues (hardware and software)
within the GIA Core. Dan can be reached via e-mail at meehan[at]psu.edu
or at ext. 3-8320. His office is 802 Oswald.
Faculty (PRI and non-PRI) without funding are encouraged to pursue
internal pilot grants to support activities such as GIS programmer
time from SSRI (Level II funding is available up to $20,000 - see
http://www.ssri.psu.edu/).
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