A grammar for interpreting geo-analytical questions as concept transformations

Figures & data

Figure 4. Annotating GIS operations with concepts. (a) Annotate Euclidean distance operation with input object and output field. (b) Annotate Zonal statistic operation with input object, field, and output content amount.

Figure 5. Mapping question components to input and output of GIS operations.

Figure 6. An abstract workflow formed by concepts.

Figure 7. A DAG of concept transformations in Figure 1(c). Each vertex represents a concept of the data, and each directed edge refers to an abstract GIS operation (concrete tools can be found in Table 1). Concept transformation models abstract information transformations in questions and GIS workflows, and thus are different from GIS workflows.

Table 1. Mapping abstract GIS operations in Figure 7 to GIS tools.

Abstract operations	ArcGIS tool	QGIS tool
Overlay	Identity	Intersection
Add geometry	Add geometry attribute	Add geometry attributes
Calculate attribute	Calculate field	Field calculator
Aggregate attribute	Dissolve	Aggregate
Aggregate geometry	Dissolve	Aggregate

Figure 8. Methodology of developing a grammar for interpreting geo-analytical questions as concept transformations.

Figure 9. Named entity recognition in a geo-analytical question. The place name is captured by the Elmo-based NER model from AllenNLP. Entities, such as time, quantity, and date are recognized by the SpaCy Python package. Concepts, such as object, network are recognized by a pre-defined concept dictionary.

Table 2. Recognition results of the question ‘What buildings are within 1 minute of driving time from fire stations that are within 60 meters of rivers in Houston in 2010?’.

Recognition classes	Question snippets
Object	[‘buildings’, ‘fire stations’, ‘rivers’]
Network	[‘driving time’]
Etime	[‘1 minute’]
Equantity	[‘60 meters’]
Placename	[‘Houston’]
Edate	[‘2010’]

Figure 10. The parse tree of the question ‘What buildings are within 1 minute of driving time from fire stations that are within 60 meters of rivers in Houston in 2010?’.

Table 3. Extraction results of the question ‘What buildings are within 1 minute of driving time from fire stations that are within 60 meters of rivers in Houston in 2010?’.

Functional roles	Concepts	Question snippets
Measure	[‘object0’]	[‘buildings’]
MeasureCondition	[‘serviceObj’, ‘networkC’, ‘network’, ‘object1’]	[‘’, ‘driving time’, ‘road network’, ‘fire stations’]
Subcondition	[‘distField’, ‘object2’]	[‘’, ‘rivers’]
Extent		[‘Houston’]
TemporalExtent		[‘2010’]

Table 4. Question categories in GeoAnQu with concept transformation models.

Category	Concept transformation model	Question example
Field-based category		‘What is the buffer zone^(Field) within 5 kilometers of parks^(Object) in Amsterdam?’
Object-based category		‘Which fire station^(Object) is closest to each fire call^(Event) in Fort Worth?’
Event-based category		‘Which migratory tracks of birds^(Event) cross national parks^(Object) in the US in 2010?’
Network-based category		‘What is the shortest route^(Network) from centers of municipalities^(Object) to outpatient services^(Object) in Tuscany in Italy?’
		‘What is the shortest route^(Network) from hospitals^(Object) to a traffic accident^(Event) in New York?’
Discrete core concept based category		‘Which shops^(Object) are open at 9 am^(Object Quality) in Happy Valley ski resort?’
Discrete quality based category		‘What is the walkability^(Object Quality) for each neighborhood^(Object) in Ghent?’
Content amount based category		‘What is the number^(Content Amount) of traffic accidents^(Event) in Amsterdam?’
		‘What is the elderly population^(Content Amount) in the Oost district^(Object) in Amsterdam?’^a
Coverage amount based category		‘What is the total area^(Coverage Amount) of agriculture land^(Field) in Netherlands?’
		‘What is the mean center^(Coverage Amount) of accidents^(Event) for each alarm territory^(Object) in Fort Worth?’
Proportion based category		‘What is the proportion^(Proportion) of Hispanic population^(Content Amount) to the total population^(Content Amount) in Tarrant County in Texas?’
		‘What is the density^(Proportion) of green space^(Field) in Amsterdam^(Object)?’^b
		‘What is tree^(Object) density^(Proportion) for each PC4 area^(Object) in Amsterdam?’^c

Yellow nodes represent optional input concepts.

^aThe elderly population can be calculated by taking the elder population density to multiply the area of the Oost district. Thus, the content amount can also be generated from proportion and coverage amount.

^bDensity equals green space area divided by Amsterdam area. Therefore, the field and object were transformed into coverage amount and then transformed into proportion.

^cTree density equals the number of tree divided by the acreage of PC4 areas. Therefore, the proportion here is transformed from the content amount of tree and the coverage amount of PC4 areas.

Figure 11. ER diagram of the functional roles, representing the constrained relationship among functional roles. For example, each measure can be constrained by ‘zero, one, or many’ measureConditions, while each measureCondition can constrain ‘only one’ measure.

Figure 12. Transformations of the question ‘What buildings are within 1 minute of driving time from fire stations that are within 60 meters of rivers in Houston in 2010?’.

Figure 13. Frequency of questions for concept transformation models in GeoAnQu. Models are named according to output concepts in the leftmost column. The numbers (e.g. Network1, Network2) refer to different input concepts for a given output.

Table 5. Recall, precision and f1 score for 134 test geo-analytical questions.

Dimension	True positive	False positive	False negative	Recall	Precision	f1
Extent	147	0	18	89%	100%	94%
Temporal extent	14	0	7	67%	100%	80%
Types	356	9	55	87%	98%	92%
Transformations	166	27	55	75%	86%	80%

Table 6. The number of correctly and falsely interpreted test questions with error sources.

		Error sources
Total count	Correct count	Recognition models	Functional grammar	Transformation extractor
134	101	7	19	7

Data and codes availability statement

The data and codes that support this study are available in ‘figshare.com’ with DOI https://doi.org/10.6084/m9.figshare.17009003.v7.