=Paper=
{{Paper
|id=Vol-1486/paper_78
|storemode=property
|title=Semantic Composition of Disparate Data in GeoHealthUS for Navigation, Display and Analysis
|pdfUrl=https://ceur-ws.org/Vol-1486/paper_78.pdf
|volume=Vol-1486
|dblpUrl=https://dblp.org/rec/conf/semweb/Wood15
}}
==Semantic Composition of Disparate Data in GeoHealthUS for Navigation, Display and Analysis==
https://ceur-ws.org/Vol-1486/paper_78.pdf
Semantic Composition of Disparate Data in
GeoHealthUS for Navigation, Display and
Analysis
David Wood
GeoHealth US Corp, Arlington, VA 22209, USA
{david}@geohealth.us
Abstract. GeoHealth US Corp produces environmental monitoring data
in the United States, and aggregates that data with historical environ-
mental data from US Government sources. Data sources include five
programs that track pollution history at the US Environmental Protec-
tion Agency, and air quality data collected by GeoHealth US Corp itself.
Environmental monitoring data is combined with information from the
US Department of Health & Human Services, including the US National
Library of Medicine, to automatically associate environmental conditions
with diseases and symptoms. Semantic Web techniques are used to per-
form the data integration, navigate the data for analysis, and to drive
the display of contextually relevant data in a Web user interface.
GeoHealth US Corp is a Virginia Benefit Corporation (or “B-corporation”),
a for-profit corporate entity that includes positive impacts on society and
the environment in its core mission.
Keywords: Environment, Health, Environmental Data, Linked Data,
Linked Data Platform, LDP, RDF, Linked Data, W3C, Callimachus,
Semantic Web, US EPA, GeoHealthUS
1 Introduction
Human health is impacted by at least three classes of information: Lifestyle,
genetics, and environment. Of these, environmental conditions remain the least
poorly integrated in the US healthcare system. Environmental health informa-
tion in the US is sparse, scattered, and in forms that make it difficult to find,
to combine, and to relate to individual patients. In some cases, it is of ques-
tionable quality. We set out to address those problems in order to provide a
more complete picture of the environmental impacts on US public health. The
potential benefits are significant given the size of the US healthcare market: Ap-
proximately 3 trillion USD is spent annually on healthcare[2], with more than
1.3 trillion USD spent by government entities[1].
GeoHealthUS approached this problem by relating multiple US Government
datasets in Linked Data formats (mostly from the US Environmental Protection
Agency) with pseudo-real-time environmental air quality data collected by the
company. Additional Linked Data was created to describe diseases and chemical
2
substances related to them. Such data was previously available only in traditional
(XML) formats from the US Department of Health & Human Services.
The evolving GeoHealthUS Web site is available at http://geohealth.us.
Individuals may currently view historic pollution report data while the newer
environmental data is being integrated1 . The site complies with the Linked Data
Platform v1.0 specification[3], and is based on the Callimachus Project’s Linked
Data platform[4]. A SPARQL endpoint is available that allows for querying both
RDF and non-RDF data, although it is currently restricted to authenticated
users.
2 Current Air Quality Data
GeoHealthUS has created mobile sensor packages called GeoHealthBoxesTM to
rapidly collect air quality information. GeoHealthBoxes are typically mounted on
vehicles and measurements taken. GeoHealthBoxes use a combination of Open
Hardware, software written by GeoHealth US Corp and a number of proprietary
environmental sensors.
Newly collected environmental data from mobile sensors was considered too
voluminous for RDF modeling to make sense. Instead, RDF summaries were
created to facilitate the location, and querying of such data from larger relational
databases. RDF and non-RDF data is presented in a single Web user interface.
Contextual subsets of data are also presented for download in various reports and
formats (such as Turtle, JSON-LD, and CSV). Contextual subsets of non-RDF
data are converted to RDF at download time, as requested.
3 Historic Environmental Data
Historic environmental data from the US Environmental Protection Agency
(EPA) was available from the programs summarized in Table 1.
Historical data from government sources represents a 25-year history of envi-
ronmental pollutants, and some of their effects. All government data was either
available in RDF formats or converted into RDF models for the purpose of sim-
ple composition. Vocabulary mapping of government data sets was undertaken
as necessary.
The US EPA operates a Linked Open Data service in a quality assurance
mode. This data service is not yet publicly available. The source data for the
programs is generally available via http://data.gov, but in some cases EPA
must be contacted directly to acquire information. This situation highlights the
relative immaturity of US Government data sources when users desire to combine
arbitrary data sets for further analysis and/or repurposing.
Table 2 list the namespaces of some of the common Semantic Web vocabu-
laries used to represent the RDF portion of the data. Core vocabularies included
the rdf, rdfs, owl, skos, and xsd namespaces.
1
A prototypical, and temporary, interface for a portion of air quality data is available
at http://geohealth.us/home/pages/livedata.xhtml?view
3
Table 1. EPA Programs Available as Linked Data
Abbreviation Program Name Purpose
FRS Facilities Registry System Facilities and locations
SRS Substance Registry System Chemical substances
TRI Toxics Release Inventory Air, water pollution reports
RCRA Resource Conservation and Recovery Act Solid & hazardous waste
CDR Chemical Data Registry Manufacturing, importation
Table 2. Common Vocabularies
Namespace URI Purpose
foaf http://xmlns.com/foaf/0.1/ Nearness, depictions
geo http://www.w3.org/2003/01/geo/wgs84 pos# Locations
place http://purl.org/ontology/places# Locations
dbpedia http://dbpedia.org/resource/ Units of measure, companies
vcard http://www.w3.org/2006/vcard/ns# Addresses
We made use of a number of RDF vocabularies specific to the EPA datasets,
which include definitions such as the classification of facilities, substances, and
reports. These vocabularies are under the base URI of http://opendata.epa.gov,
but have not yet been formally published by the US EPA. We know of them
through our prior work with the agency, and look forward to them being publicly
available soon. Location information in EPA datasets was augmented by the use
of a custom vocabulary created to represent US postal (ZIP) codes to facilitate
the creation of maps and other geographic displays.
4 Relating Diseases and Symptoms
Additionally, a number of custom vocabularies were developed to represent infor-
mation specific to the GeoHealthUS application, such as the description of dis-
eases (extracted from traditional data formats available from the US Department
of Health & Human Services). This data was mapped to chemical substances via
SRS identifiers and to clinical findings using SNOMED-CT identifiers.
5 Data Architecture
Figure 1 presents a simplified architecture diagram for the GeoHealthUS cloud-
based service. Updates to the historic data are periodically uploaded to the RDF
databases. Air quality data collected directly by GeoHealthUS is uploaded to re-
lational databases for summarization, analysis and querying. Callimachus acts
as a data hub to dynamically generate both Web pages for human consump-
tion and RDF- and CSV-formatted data extracts upon demand. The service is
naturally distributed, and spread across many virtual machines.
4
Mobile Sensors
Relational Data Layer
DB DB DB DB DB
SQL
Callimachus HTML
(Presentation/Query Layer)
SPARQL
ETL DB DB DB DB DB
DB RDF Data Layer
Historical Data
Fig. 1. Simplified architecture, showing dynamic data aggregation at Callimachus.
6 Conclusions
The GeoHealthUS poster illustrates three benefits of a Semantic Web approach
to data integration:
1. Conversion to RDF formats facilitated data integration across many data
sets by the simple mechanism of identifier alignment.
2. Presentation to end users is via a small number of SPARQL v1.1 queries,
simplifying maintenance requirements, and reducing maintenance costs over
traditional approaches.
3. The approach was shown to function in a large, real-world use case with
significant economic potential.
References
1. Chantrill, C. US Health Care Spending. http://www.usgovernmentspending.com/
us\_health\_care\_spending\_10.html accessed 30 June 2015.
2. Centers for Medicare & Medicaid Services. National Health Expenditures 2013
Highlights. http://www.cms.gov/Research-Statistics-Data-and-Systems/
Statistics-Trends-and-Reports/NationalHealthExpendData/Downloads/
highlights.pdf accessed 30 June 2015.
3. Speicher, S., Arwe, J., and Malhotra, A. (eds). Linked Data Platform 1.0.
W3C Recommendation, 26 February 2015. Retrieved 19 April 2015 from
http://www.w3.org/TR/2015/REC-ldp-20150226/.
4. Wood, D. and Leigh, J.: The Callimachus Project. Retrieved 19 April 2015 from
http://callimachusproject.org.