=Paper=
{{Paper
|id=Vol-2977/paper14
|storemode=property
|title=Geospatial Reasoning with Shapefiles for Supporting Policy Decisions (short paper)
|pdfUrl=https://ceur-ws.org/Vol-2977/paper14.pdf
|volume=Vol-2977
|authors=Henrique Santos,James P. McCusker,Deborah L. McGuinness
|dblpUrl=https://dblp.org/rec/conf/esws/0002MM21
}}
==Geospatial Reasoning with Shapefiles for Supporting Policy Decisions (short paper)==
https://ceur-ws.org/Vol-2977/paper14.pdf
Geospatial Reasoning with Shapefiles for
Supporting Policy Decisions
Henrique Santos, James P. McCusker, and Deborah L. McGuinness
Rensselaer Polytechnic Institute, Troy NY, USA 12180
{oliveh,mccusj2}@rpi.edu,dlm@cs.rpi.edu
Abstract. Policies are authoritative assets that are present in multiple
domains to support decision-making. They describe what actions are al-
lowed or recommended when domain entities and their attributes satisfy
certain criteria. It is common to find policies that contain geographical
rules, including distance and containment relationships among named lo-
cations. These locations’ polygons can often be found encoded in geospa-
tial datasets. We present an approach to transform data from geospatial
datasets into Linked Data using the OWL, PROV-O, and GeoSPARQL
standards, and to leverage this representation to support automated
ontology-based policy decisions. We applied our approach to location-
sensitive radio spectrum policies to identify relationships between ra-
dio transmitters coordinates and policy-regulated regions in Census.gov
datasets. Using a policy evaluation pipeline that mixes OWL reasoning
and GeoSPARQL, our approach implements the relevant geospatial re-
lationships, according to a set of requirements elicited by radio spectrum
domain experts.
1 Introduction
Policies are commonly defined as decision-making assets that express one or more
actions allowed or recommended under certain conditions. In the radio communi-
cations domain, policies are created to help manage the use of a limited electro-
magnetic spectrum. Many policies are location-specific, meaning that they are
only applicable when the usage of the radio spectrum is to occur in specific ge-
ographic locations, as dictated by the policy. In the United States, the National
Telecommunications and Information Administration Manual of Regulations and
Procedures for Federal Radio Frequency Management 1 (NTIA Redbook ) is a com-
pilation of regulatory policies that define the conditions organizations, systems,
and devices must satisfy to compatibly share radio spectrum while minimizing
interference. Because policies in the NTIA Redbook regulate both commercial
and federal spectrum usage, it is common to find military facilities, as well as
regulations covering domestic and international locations.
Copyright ©2021 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
1
http://bit.ly/NTIA_Redbook
The US Census Bureau publishes geospatial datasets about the United States,
its territories, and points of interest, in its Census.gov data portal. The datasets
contain high-definition polygons, usually in the shapefile [4] format, of many
locations referred by radio spectrum policies. Although this format is a popular
choice for encoding geographical entities, its main use-case is to support data
interchange among geographic information systems (GIS). The shapefile for-
mat usually requires the use of a GIS to allow operations over the data, including
calculations and queries. Therefore, it is not very suitable for integrating with
ontology-based applications.
We present an approach to allow ontology-based applications to leverage
geospatial data in formats not easily accessible or referred from within ontol-
ogy constructs, and to use this data to perform geospatial calculations. We
implemented the approach to support automated radio spectrum policy deci-
sions. This is accomplished by representing Census.gov relevant polygons in the
GeoSPARQL [5] vocabulary, and by defining OWL [9] classes that encode the
policies’ location rules. A policy evaluation pipeline that mixes OWL reasoning
and GeoSPARQL leverages this model to elicit spatial relationships, providing
high-definition spatial calculations. This approach was evaluated to perform well
in terms of coverage of geospatial requirements, as elicited by domain experts. It
is implemented as part of the Dynamic Spectrum Access (DSA) Policy Frame-
work [14], which was developed to serve as a machine-readable policy repository
to support increased automation of policy evaluations.
2 Transforming Census.gov shapefiles
The majority of location-sensitive policies in the NTIA Redbook refer to these
types of locations: military facilities, States, or Country. Because the policies
are originally authored in natural language, and targeted at spectrum man-
agers, they refer to locations by their names (e.g. “Fairbanks”, “Camp Parks”),
without a comprehensive definition of the boundaries of such regions. To sup-
port automation of policy decisions, it becomes crucial to encode and leverage
the polygons for these relevant locations.
The Census Bureau is the United States agency that serves as the nation’s
leading provider of quality data about its people and economy. Yearly, the agency
publishes updated and authoritative geospatial datasets to provide meaning and
context to statistical data the bureau produces. Largely published in the shape-
file format, the published data2 does include State boundaries and military
installations, conveniently supporting our policy use-case. The State dataset is
composed of 56 polygons, representing the 50 U.S. States, District of Columbia,
plus 5 U.S. territories. The military installation dataset has 859 polygons, de-
scribing information about airports, laboratories, training areas, etc. In addition
to the polygons, the datasets contain some minimal metadata about the loca-
tions, including a unique ID, and a legal name.
2
https://www.census.gov/programs-surveys/geography/geographies/
mapping-files.html
We have applied the Semantic Extract, Transform, and Load-r (SETLr [12])
to these datasets. SETLr orchestrates ETL pipelines by the use of a script in
Turtle format that defines data sources, extract and transform processes, and
destination formats. SETLr was executed in both geospatial datasets to ex-
tract shapes’ information and transform them into geographical features us-
ing the PROV-O [3] (prov:Location) and GeoSPARQL (geo:Feature,
sf:Geometry) ontologies. Because each phase of the ETL pipeline in SETLr
is defined as an RDF resource, the complete provenance of how these geograph-
ical features came to be is maintained, thereby supporting the explanation of
policy decisions in more complex scenarios where multiple locations sources are
involved.
3 Geospatial Reasoning on Radio Spectrum Policies
Geospatial reasoning is a crucial capability when evaluating policies. Many poli-
cies, including those that regulate radio spectrum usage, are only applicable
when their specified location rules are satisfied. These locations include named
locations that can be mapped to features from geospatial datasets, and poly-
gons defined directly in the policy’s rules. Either way, location rules need to be
correctly evaluated, taking into consideration which polygons the policy regu-
lates, as well as coordinates that are subject to evaluation (e.g. where a radio
transmission is to occur).
We designed the DSA Policy Framework [14] to serve as a machine-readable,
radio spectrum policy repository that can be used to automatically process ra-
dio transmission requests. The framework utilizes the World Wide Web Con-
sortium’s (W3C) OWL 2 and PROV-O, and the Open Geospatial Consortium’s
(OGC) GeoSPARQL 1.0 standards as a modeling foundation of radio spectrum
policies and involved entities. Figure 1 shows the RDF model of a transmission
request within the DSA Policy Framework. Transmission requests are defined as
prov:Activity, with the associated requester as a prov:Agent. Attributes
that further characterize the transmission are represented using either PROV-O
(including the location attribute) or a domain ontology. Coordinates in which
requesters are located are represented as Well-Known Text (WKT) [2] string,
and expressed using the geo:asWKT predicate.
Agent (Requester) Activity (Action)
wasAssociatedWith
GenericJTRS_Radio Transmission
hasAttribute atLocation startedAtTime endedAtTime
xsd:dateTime
Frequency asWKT
Location POINT
Range (-114.23 33.20)
Fig. 1. The DSA request model
To allow the evaluation of the relationships between coordinates from trans-
missions and policy-regulated locations, we have pursued the representation of
these locations as an OWL ontology where classes represent policy locations. To
exemplify this approach, we will use the second provision of the US91 policy
from the NTIA Redbook, which reads (with adaptations):
“In the sub-band 1761-1780 MHz, Federal earth stations in the space
operation service may transmit at the following 25 sites and non-Federal
base stations must accept harmful interference caused by the operation
of these earth stations: Fairbanks, Camp Parks, ... .”
Besides the policy text itself, which explicitly lists 25 sites where the policy
is applicable, US91 is listed in the NTIA Redbook under the United States table,
because it is only applicable in the US and not internationally. Listing 1 shows
the representation of the involved locations for supporting this policy. Lines 1-7
define the USLocation class for expressing the entire United States land. This
class is defined as a prov:Location and is a union of all States, District of
Columbia, and territories from the appropriate Census.gov dataset, using the
geo:sfWithin predicate from GeoSPARQL. Similarly, lines 9-16 extend this
class to express the specific locations the above policy regulates, this time using
features from the military facilities Census.gov dataset.
1 Class: USLocation
2 EquivalentTo:
3 prov:Location and (geo:sfWithin STATE_01 or
4 geo:sfWithin STATE_02 or
5 ...
6 SubClassOf:
7 prov:Location
8
9 Class: US91-2-c_Location
10 EquivalentTo:
11 USLocation and (
12 (geo:sfWithin value Fairbanks) or
13 (geo:sfWithin value CampParks) or
14 ...
15 SubClassOf:
16 USLocation
Listing 1. OWL expression of part of the US91 policy in Manchester syntax
3.1 Evaluating geospatial rules in policies
To evaluate coordinates in transmission requests with policy-regulated locations,
we used the GeoSPARQL function predicates embedded in SPARQL queries, as
seen in Listing 2. The implemented queries focus on the within and dis-
tance relationships. The queries infer triples in the format :req location
geo:sfWithin :NAMED LOCATION, or as a distance attribute with the nu-
merical distance as a value and in relation to some named location.
1 FILTER(geof:sfWithin({{WKT_STR}}ˆˆgeo:wktLiteral, ?wkt))
2 BIND(geof:distance({{WKT_STR}}ˆˆgeo:wktLiteral, ?wkt,
3 units:kilometer) AS ?distance)
Listing 2. GeoSPARQL statements to elicit select geospatial relationships.
The inferred triples are asserted back into the transmission request RDF
model, which then gets reasoned over by an OWL reasoner. Using those inferred
assertions, the location specified in the request can now be correctly reasoned
to belong to one or more location classes, such as those in Listing 1. To ex-
emplify this process, the coordinates for the request in Figure 1 are located in
Arizona. Because US91’s second provision does not include any Arizona loca-
tions, no triple linking the request location to one of the policy’s locations would
be inferred. But, a triple linking the request location to the State of Arizona
would exist (:req location geo:sfWithin :STATE 04). In this setting,
the request location would be reasoned to belong to the USLocation class, but
not to the US91-2-c Location class, indicating that the transmission is to
occur in the United States, but the second provision of US91 is not applicable.
Conversely, if the request in Figure 1 is modified to a coordinate within
the “Fairbanks” named location, a triple :req location geo:sfWithin
:Fairbanks will exist. Therefore, the request location will be reasoned to be-
long to the US91-2-c Location class, making the second provision of US91
applicable.
4 Evaluation
We worked with radio spectrum domain experts to elicit a set of geographical re-
quirements that a machine-readable policy model needs to support. They appear
in bold in the first column of Table 1. The table contains columns for Policy Rep-
resentation and Request Evaluation. “Yes” indicates that the policy construct
is either Relevant or it has been fully addressed and Implemented. “Partial”
indicates that the current implementation meets a simplified requirement.
Policy Representation Request Evaluation
Locations Relevant Implemented Relevant Implemented
Named locations yes yes yes yes
Relative locations yes partial yes partial
Polygons/Circles yes yes yes yes
Geographical rules
Specific location yes yes yes yes
List of locations yes yes yes yes
Table 1. Geospatial semantics coverage
Most policies refer to locations by names or by coordinates (points, polygons,
and circles), but sometimes a location is expressed in relation to another loca-
tion. Currently, relative locations have been constrained to the ones expressed
using the distance relationship. Geographical rules are defined in terms of the
requester being in a location or a list of locations. Our approach implements
these constructs using the geo:sfWithin predicate and OWL unions.
5 Related Work
The works in [11,13] proposed approaches for converting geospatial content to
RDF, using mapping languages and ETL pipelines. The work in [6] allows the
access of geospatial datasets, including shapefiles, using an ontology-based
data access approach. Our conversion relied on SETLr, which enables the data
conversion of geospatial data to RDF similar to the first two approaches, but also
allows the maintenance of data transformation provenance. This maintenance is
important in this use-case for supporting the explanation of policy decisions.
XACML 3.0, the eXtensible Access Control Markup Language [1], is a well-
known policy language and de facto standard for representing attribute-based
access control (ABAC) [10] policies and requests. Importantly, XACML pro-
vides a reference architecture for centralizing access control and a process model
for evaluating requests against existing policies that inform the design of access
control systems across domains and technologies. Thi [15] proposes an OWL-
based extension to XACML to support a generalized, context-aware, role-based
access control (RBAC) model, providing Spatio-temporal restrictions and con-
forming with the NIST RBAC standard [8]. Their work augments the XACML
architecture with new functions and data types.
Our approach combines OWL, PROV-O, and GeoSPARQL to encode geospa-
tial features, and an OWL reasoner to realize location class memberships. Our
representation builds on previous work by matching the cross-domain policy ex-
pression semantics of XACML, extending it with the capacity to express rich
Spatio-temporal restrictions, enabling the implementation of a wide variety of
attribute-based policies across domains.
6 Conclusion
This paper presents an approach for leveraging geographical features, originally
in shapefiles, to support policy decisions. In the radio spectrum domain, it
is commonplace for policies to regulate the usage of the spectrum in specific
locations, therefore requiring spatial reasoning to identify relationships between
radio transmitters’ coordinates and policy-regulated regions. This approach is an
integral part of the DSA Policy Framework, which is functioning as a prototype
policy management system in support of spectrum sharing operations.
Future work involves the research and development of the application of
more spatial relationships, including relative locations. Besides, in other policy
publications, we have encountered locations that are expressed in unusual shapes.
These include paths, cones, and altitudes. More research is necessary to assess
the impact in both modeling and reasoning, should we pursue this line of work.
Finally, we are generalizing the approach to beyond radio spectrum policies
by initially supporting practitioners from multiple domains in the creation of
policies utilizing terminology and entities in domain knowledge graphs [7].
Acknowledgements. This work is partially funded through the National Spec-
trum Consortium (NSC) project number NSC-17-7030.
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