Policies are authoritative assets that are present in multiple domains to support decision-making. They describe what actions are allowed or recommended when domain entities and their attributes satisfy certain criteria. It is common to nd policies that contain geographical rules, including distance and containment relationships among named locations. These locations' polygons can often be found encoded in geospatial 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 locationsensitive radio spectrum policies to identify relationships between radio 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 relationships, according to a set of requirements elicited by radio spectrum domain experts.
Policies are commonly de ned as decision-making assets that express one or more actions allowed or recommended under certain conditions. In the radio communications domain, policies are created to help manage the use of a limited electromagnetic spectrum. Many policies are location-speci c, meaning that they are only applicable when the usage of the radio spectrum is to occur in speci c geographic 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 compilation of regulatory policies that de ne 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 nd military facilities, as well as regulations covering domestic and international locations.
Copyright ©2021 for this paper by its authors. Use permitted under Creative Commons 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-de nition polygons, usually in the shapefile [
We present an approach to allow ontology-based applications to leverage
geospatial data in formats not easily accessible or referred from within
ontology constructs, and to use this data to perform geospatial calculations. We
implemented the approach to support automated radio spectrum policy
decisions. This is accomplished by representing Census.gov relevant polygons in the
GeoSPARQL [
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 managers, they refer to locations by their names (e.g. \Fairbanks", \Camp Parks"), without a comprehensive de nition of the boundaries of such regions. To support 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 shapefile 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, describing information about airports, laboratories, training areas, etc. In addition to the polygons, the datasets contain some minimal metadata about the locations, 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 [
Geospatial reasoning is a crucial capability when evaluating policies. Many policies, including those that regulate radio spectrum usage, are only applicable when their speci ed location rules are satis ed. These locations include named locations that can be mapped to features from geospatial datasets, and polygons de ned directly in the policy's rules. Either way, location rules need to be correctly evaluated, taking into consideration which polygons the policy regulates, as well as coordinates that are subject to evaluation (e.g. where a radio transmission is to occur).
We designed the DSA Policy Framework [
Transmission xsd:dateTime
POINT (-114.23 33.20) Agent (Requester)
Activity (Action) wasAssociatedWith
GenericJTRS_Radio hasAttribute atLocation startedAtTime endedAtTime Frequency
Range
Location
asWKT
Fig. 1. The DSA request model
To allow the evaluation of the relationships between coordinates from transmissions 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 de ne the USLocation class for expressing the entire United States land. This class is de ned 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 speci c locations the above policy regulates, this time using features from the military facilities Census.gov dataset. 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 distance relationships. The queries infer triples in the format :req location geo:sfWithin :NAMED LOCATION, or as a distance attribute with the numerical distance as a value and in relation to some named location.
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 speci ed in the request can now be correctly reasoned to belong to one or more location classes, such as those in Listing 1. To exemplify this process, the coordinates for the request in Figure 1 are located in Arizona. Because US91's second provision does not include any Arizona locations, 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 modi ed 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 belong to the US91-2-c Location class, making the second provision of US91 applicable. 4
We worked with radio spectrum domain experts to elicit a set of geographical requirements that a machine-readable policy model needs to support. They appear in bold in the rst column of Table 1. The table contains columns for Policy Representation 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 simpli ed requirement.
Locations
Named locations Relative locations
Polygons/Circles Geographical rules
Speci c location List of locations
Policy Representation Request Evaluation Relevant Implemented Relevant Implemented yes yes yes yes yes partial yes partial yes yes yes yes yes yes yes yes yes yes yes yes
Most policies refer to locations by names or by coordinates (points, polygons, and circles), but sometimes a location is expressed in relation to another location. Currently, relative locations have been constrained to the ones expressed using the distance relationship. Geographical rules are de ned 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
The works in [
XACML 3.0, the eXtensible Access Control Markup Language [
Our approach combines OWL, PROV-O, and GeoSPARQL to encode geospatial features, and an OWL reasoner to realize location class memberships. Our representation builds on previous work by matching the cross-domain policy expression 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
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 speci c 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 [