G eospa C y: A tool for extraction and geographical referencing of spatial expressions in textual data (2024)

Abstract: Spatial information in text enables to understand the geographical context and relationships within text for better decision-making across various domains such as disease surveillance, disaster management and other location based services. Therefore, it is crucial to understand the precise geographical context for location-sensitive applications. In response to this necessity, we introduce the GeospaCy software tool, designed for the extraction and georeferencing of spatial information present in textual data. GeospaCy fulfils two primary objectives: 1) Geoparsing, which involves extracting spatial expressions, encompassing place names and associated spatial relations within the text data, and 2) Geocoding, which facilitates the assignment of geographical coordinates to the spatial expressions extracted during the Geoparsing task. Geoparsing is evaluated with a disease news article dataset consisting of event information, whereas a qualitative evaluation of geographical coordinates (polygons/geometries) of spatial expressions is performed by end-users for Geocoding task.

## introduction in recent years, spatial information recognition from textual data has gained more attention in the natural language processing (nlp) field. the importance and relevance of the work can be strengthened by highlighting the potential impact and benefits of accurate spatial information extraction in various domains, i.e., disaster management, disease surveillance. for instance, in disease surveillance, a disease outbreak in 'central paris' is not the same as one in the 'southern part of paris'. moreover, the extraction and georeferencing of spatial information have significant implications in various domains, including healthcare, financial markets, and e-learning @xcite . therefore, the extraction and interpretation of spatial information from textual data play a fundamental role in understanding geographical contexts. the spatial information can be expressed in the textual documents in both simple and complex ways, depending on the syntax and semantic of expression. this geospatial information is available in the form of absolute spatial information (precise location names, e.g., milan) and relative spatial information (spatial relations associated with the location name, e.g., north milan). both absolute and relative spatial information are essential in providing accurate context for locations in the text, ensuring precision in understanding and responding specific to geographical sensitive applications. while absolute spatial data offers concrete locations, relative spatial information provides contextual references that help to refine and to detail the specific area of interest, resulting into more accurate geographical reference in the text. therefore, a possible research question is: "can we develop an efficient and accurate algorithm for extracting spatial relations from textual data and transforming them into valid geospatial representations"? traditional methods of text mining often overlook important geographical details by ignoring the complex spatial information found within the text. the motivation behind the development of geospacy is to overcome this limitation and provide a robust tool specifically tailored to identify and georeference spatial expressions in textual data. the main purpose of geospacy software tool is to address the demand of precise geographical insights, which are essential for making informed decisions in various domains such as disease surveillance, disaster management, and other locationbased services. geospacy performs two main tasks, i.e., 1) geoparsing and 2) geocoding. geoparsing within the context of the geospacy tool involves the identification and extraction of spatial expressions embedded within unstructured textual data. this task primarily revolves around recognizing spatial expressions such as place names, and spatial relations associated with the place names from the text. geoparsing task provide a foundation for subsequent geocoding to understand the geographical context of the textual data. in contrast, geocoding within the geospacy tool represents the process of assigning precise geographical coordinates to the spatial expressions identified during the geoparsing task. after the spatial information such as place names or locations have been extracted, geocoding works to convert these textual references into geographical coordinates. the remainder of this article is structured as follows: section 2 provides the related work associated to geospacy. subsequently, section 3 describes the software overview, section 4 briefly details the methodology, section 5.2 explains the real world use cases and section 6 presents the conclusion. ## related work different research studies have been carried out for both geoparsing and geocoding process. the details of the related work are discussed in subsequent sections. ## geospacy overview geospacy have the capabilities to precisely extract and reference spatial information within unstructured textual data. the main purpose of this software tool is to provide a comprehensive understanding of geographical contexts within textual information. this software tool has a set of features and functionalities which are as follows: ## methodology & implementation our methodology @xcite is divided into two main phases: 1) extraction phase (geoparsing), 2) geocoding phase respectively. the process workflow of extraction of rse and its georeferencing is shown in figure 2. the details of the two phases of are explained in the subsequent sections. ## experiments geospacy results are evaluated for each phase of the software. these two main phases are: 1) geoparsing i.e., the extraction of rse from text and 2) geocoding i.e., the computed geographical coordinates for rse. the evaluation of these phases are as follows: ## conclusion geospacy focused on extracting spatial expressions such as gpe, loc and the primary contribution of rse extraction from text, subsequently translating the geographic coordinates of the identified rse. we proposed a combination of nlp techniques to extract rse from unstructured text. the results of the rse extraction are evaluated with news article disease dataset having a precision of 0.9, recall of 0.88 and micro f-score of 0.88. sub-sequently, we conducted a qualitative assessment of rse geographical coordinates (shapes) with an observed accuracy of 75%.