GeospatRE: extraction and geocoding of spatial relation entities in textual documents

Figures & data

Figure 1. Level-1 spatRE.

Figure 2. Level-2 spatRE.

Figure 3. Level-3 spatRE.

Figure 4. Compound spatRE.

Figure 5. spatRE pipeline.

Table

Text: Significant number of COVID-19 cases are reported close to Paris border, whereas other areas in the region are safe due to preventive safety measures. Clause 1: Significant number of COVID-19 cases are reported close to Paris border Clause 2: whereas other areas in the region are safe due to preventive safety measures.

Table

Text: Significant number of COVID-19 cases are reported close to Paris border, north of Lyon whereas other areas in the closer region are safe due to preventive safety measures. Clause 1: Significant number of COVID-19 cases are reported close to Paris border Clause 2: north of Lyon Clause 3: whereas other areas in the region are safe due to preventive safety measures. Spatial Entity 1: Paris Spatial Entity 2: Lyon Spatial Entity Span 1: Paris border Spatial Entity Span 2: north of Lyon

Table 1. Spatial relation entities (spatRE).

Text	Before Extraction Phase	After Extraction Phase
	DOC (Element)	DOC (Element)
Significant number of	Significant number of	Significant number of
COVID-19 cases are	COVID-19 cases are	COVID-19 cases are
reported close to	reported close to	reported close to
Paris border,	Paris(GPE) border,	Paris border(spatRE),
north of Lyon	north of Lyon(GPE)	north of Lyon(spatRE)
whereas other areas	whereas other areas	whereas other areas
in the closer region	in the closer region	in the closer region are
are safe due to preventive	are safe due to preventive	safe due to preventive safety
safety measures.	safety measures.	measures.

Algorithm 1 Procedure/Pipeline to extract spatREs

Algorithm 2 Algorithm for Extraction Phase

Figure 6. Level-1 & Level-2 spatial relations.

Figure 7. Level-3 & compound spatial relations.

Algorithm 3 Procedure to extract Cardinal Coordinates

Table 2. Extraction phase results (spatRE extraction).

Disease Name	No. of Articles	spatRE Extracted	spatRE Actual	Precision	Recall	F-Score
Antimicrobial resistance (AMR)	25	4	5	1	0.80	0.88
COVID-19	100	100	92	0.87	0.94	0.90
Avian-Influenza	150	57	68	0.87	0.83	0.84
Lyme	29	10	10	0.83	1	0.90
Tick-borne Encephalitis (TBE)	73	73	81	0.93	0.83	0.87
Aggregate	377	244	256	0.9	0.88	0.88

Algorithm 4 Procedure to extract Ordinal Coordinates

Table 3. Qualitative evaluation of spatial relation by city.

City	Aggregate Score	Total Score	Accuracy(100%)	Mean(4)	Remarks
Paris	136	152	89.5	3.6	Excellent
London	142	152	93.4	3.7	Excellent
Milan	106	152	69.7	2.8	Good
Madrid	77	152	50.7	2	Weak
Zagreb	116	152	76.3	3.1	Excellent
Utrecht	105	152	69.1	2.8	Good
Delft	121	152	79.6	3.2	Excellent
Lyon	114	152	75	3	Good
Florence	477	608	78.5	3.1	Excellent

Table 4. Qualitative evaluation of Level-1 spatial relations.

	N	S	E	W	NE	NW	SE	SW	Central
Score	67	61	82	73	75	78	81	78	82
Total Score	96	96	96	96	96	96	96	96	96
Accuracy(100%)	69.8	63.5	85.4	76	78.1	81.3	84.4	81.3	85.4
Mean(4)	2.9	2.4	3.5	3.1	3.1	3.4	3.3	3.2	3.4
Remarks	Good	Good	Excellent	Excellent	Excellent	Excellent	Excellent	Excellent	Excellent

Table 5. Qualitative evaluation of Level-2 and Level-3 spatial relations.

	Border	NE Border	NW Border	SE Border	SW Border	N Border	S Border	E Border	W Border	2 Miles+Border
Score	87	65	63	72	77	59	61	78	71	84
Total Score	96	96	96	96	96	96	96	96	96	96
Accuracy(100%)	90.6	67.7	65.6	75	80.2	61.5	63.5	81.3	74	87.5
Mean(4)	3.6	2.7	2.7	3	3.3	2.6	2.5	3.3	2.9	3.5
Remarks	Excellent	Good	Good	Good	Excellent	Good	Good	Excellent	Good	Excellent

Algorithm 5 Procedure to extract Level-1 Coordinates

Algorithm 6 Procedure to extract Level-2 Coordinates

Algorithm 7 Procedure to extract Level-3 Coordinates

Algorithm 8 Algorithm for Geocoding Phase

Data availability statement

The code and data that support the findings of this study are openly available in GitHub repositories dedicated to GeospatRE tool⁴ and the associated dataset.⁵