1. INTRODUCTION

In the scientific world and in society in general there is currently a great interest in geolocation for various purposes. Geolocation of documentation is not exclusive to science but rather has occurred previously in other areas and types of documentation such as news (Buscaldi and Magnini, 2010Buscaldi, D., & Magnini, B. (2010). Grounding toponyms in an italian local news corpus. Proceedings of the 6th workshop on geographic information retrieval, 1-5. DOI: https://doi.org/10.1145/1722080.1722099 ; D’Ignacio et al., 2014D’Ignazio, C., Bhargava, R., Zuckerman, E., & Beck, L. (2014). Cliff-clavin: Determining geographic focus for news articles. DSpace@MIT. Available at: https://hdl.handle.net/1721.1/123451.; Imani et al., 2017Imani, M. B., Chandra, S., Ma, S., Khan, L., & Thuraisingham, B. (2017). Focus location extraction from political news reports with bias correction. 2017 IEEE International Conference on Big Data (Big Data) 1956-1964. DOI: https://doi.org/10.1109/BigData.2017.8258141. ; Teitler et al., 2008Teitler, B. E., Lieberman, M. D., Panozzo, D., Sankaranarayanan, J., Samet, H., & Sperling, J. (2008). NewsStand: A new view on news. Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems, 1-10. DOI: https://doi.org/10.1145/1463434.1463458.), historical documents through digital humanities (Cascón-Katchadourian et al., 2019Cascón-Katchadourian, J., López-Herrera, A. G., Ruiz-Rodríguez, A. Á., & Herrera-Viedma, E. (2019). Proyecto Histocarto: aplicación de SIGs (georreferenciación y geolocalización) para mejorar la recuperación de la documentación histórica gráfica. Profesional de la Información, 28(4). DOI: https://doi.org/10.3145/epi.2019.jul.16.), social media content (Dredze et al., 2013Dredze, M., Paul, M. J., Bergsma, S., & Tran, H. (2013). Carmen: A twitter geolocation system with applications to public health. Workshops at the twenty-seventh AAAI conference on artificial intelligence. ; Zhang and Gelernter, 2014Zhang, W., & Gelernter, J. (2014). Geocoding location expressions in Twitter messages: A preference learning method. Journal of Spatial Information Science, 9, 37-70. DOI: http://dx.doi.org/10.5311/JOSIS.2014.9.170.; Middleton et al., 2018Middleton, S. E., Kordopatis-Zilos, G., Papadopoulos, S., & Kompatsiaris, Y. (2018). Location extraction from social media: Geoparsing, location disambiguation, and geotagging. ACM Transactions on Information. Systems (TOIS), 36(4), 1-27. DOI: https://doi.org/10.1145/3202662.) and many others. One of the causes of interest in geolocation is due to the great documentary explosion that has occurred in recent decades. With so much information available comprehension has become difficult, leading to the search for alternatives, many of them graphical, to visualize and filter the information. Science does not escape this global trend. Its recent popularization is linked to the appearance and massive use over the last decade of websites, apps and social networks that offer services depending on the user’s needs and surroundings. Examples of such resources, among others, are Foursquare, TripAdvisor, GoogleMaps.

Geolocation involves determining the geographical location of information so that it can be processed (Ramos-Vacca and Bucheli-Guerrero, 2015Ramos-Vacca, I. D., & Bucheli-Guerrero, V. A.. (2015). Automatic geolocation of the scientific knowledge: Geolocarti. 10th Computing Colombian Conference (10CCC), 416-424. ). Advancements in aerial photogrammetry and digitized cartography (Cortés-José, 2001Cortés-José, J. (2001). El documento cartográfico. In J. Jiménez-Pelayo, J. Monteagudo-López-Menchero, & F. J. Bonachera-Cano, La documentación cartográfica : Tratamiento, gestión y uso, 37-113. Huelva: Universidad de Huelva. ), as well as new map printing methods, have contributed to this. The advent of programs such as Google Maps, Bing Maps, and OpenStreetMap, along with mapping creation tools like OpenLayer, Leaflet, CartoDB, and MapTiler (Cascón-Katchadourian and Ruiz Rodríguez, 2016Cascón-Katchadourian, J., & Ruiz Rodríguez, A. Á. (2016). Descripción y valoración del software MapTiler: Del mapa escaneado a la capa interactiva publicada en la web. Profesional de la Información, 25(6), 970978. DOI: https://doi.org/10.3145/epi.2016.nov.13.), has further advanced the field. In scientific evaluation and bibliometrics, geolocation is often used to identify the geographical origin of scientific articles based on their authors (Catini et al., 2015Catini, R., Karamshuk, D., Penner, O., & Riccaboni, M. (2015). Identifying geographic clusters: A network analytic approach. Research policy, 44(9), 1749-1762. DOI: https://doi.org/10.1016/j.respol.2015.01.011.). However, few studies have analyzed scientific publications based on their geographical coordinates or toponyms and mapped them.

The aim of this article is to design an algorithm-based tool that helps scientists to classify scientific articles with geographic information. With current search systems, a manual filter is necessary in order to identify the locations they are interested in, which may be needlessly time consuming. An algorithm is needed to improve the accuracy of information retrieval systems in terms of geographic information. Likewise, being able to visualise scientific corpus would facilitate geographic meta-analysis (Gerstner et al., 2017Gerstner, K., Moreno-Mateos, D., Gurevitch, J., Beckmann, M., Kambach, S., Jones, H. P., & Seppelt, R. (2017). Will your paper be used in a meta-analysis? Make the reach of your research broader and longer lasting. Methods in Ecology and Evolution, 8(6), 777-784. DOI: https://doi.org/10.1111/2041-210X.12758.). It would also facilitate the discovery of areas of the earth that have previously been studied to varying degrees (Fisher et al., 2011Fisher, R., Radford, B. T., Knowlton, N., Brainard, R. E., Michaelis, F. B., & Caley, M. J. (2011). Global mismatch between research effort and conservation needs of tropical coral reefs. Conservation Letters, 4(1), 64-72. DOI: https://doi.org/10.1111/j.1755-263X.2010.00146.x.; Martin et al., 2012Martin, L. J., Blossey, B., & Ellis, E. (2012). Mapping where ecologists work: Biases in the global distribution of terrestrial ecological observations. Frontiers in Ecology and the Environment, 10(4), 195-201. DOI: https://doi.org/10.1890/110154. ). In this work we analyze the geolocation of the content of the document, that is, geolocate the documentation according to the places or locations that are observed in it. We are not interested in the geolocation of the address of the authors of the articles.

Here a subsection called Related Works has been added where initiatives similar to ours and the differences they have with our project are explained. Secondly, the three fundamental objectives pursued by this research are given as well as a justification for it being carried out. Thirdly, the methodology explains step by step the search process, collection processing, as well as everything related to the development of the algorithm and its application to the database. Fourthly, in the results section, we present the main achievements of this research with the support of two tables with the success rates and frequency of place names and a figure to show the index of the website created for this purpose. Finally, in the discussions we reflect the limitations, a comparison of the results and future lines of research.

2. OBJECTIVES

Taking into account, as just analyzed, that there is a growing interest in mapping and geolocating science, either from a private perspective or from an institutional perspective, this article proposes new forms of geolocation that serve as a tool for analysis, visualization and retrieval of a line of research. More specifically, there are three main objectives for this project:

Therefore, we will present the results of three objectives applied to a set of scientific studies concerning the Sierra Nevada mountain range (Granada, Spain). Our study is original and useful because it aims to geolocate the scientific production in an automatic way by means of algorithms created for this purpose and not in a manual or semi-automatic way.

3. MATERIAL AND METHODS

It should be mentioned that in this paper we apply a work on a specific line of research or a specific geographic collection. Thus, as opposed to the traditional analysis based on topics or subjects, such as those generated by bibliometric software like VosViewer (Van Eck and Waltman, 2010Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping, Scientometrics, 84(2), 523-538. DOI: https://doi.org/10.1007/s11192-009-0146-3.) and Scimat (Cobo et al., 2012Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2012). SciMAT: A new science mapping analysis software tool. Journal of the American Society for Information Science and Technology, 63(8), 1609-1630. DOI: https://doi.org/10.1002/asi.22688.), we propose to visualize a line of research through a map using coordinates and toponyms, using a geographic search engine for this purpose.

To create a collection of documentary information about Sierra Nevada, a search was done in the Scopus database. The Scopus database has been chosen for the facilities it offers for exporting full-text documents, thanks to Scopus Document Download Manager. The first step was to perform a search in the Scopus database with the following search equation: (TITLE-ABS-KEY (“sierra nevada”) AND TITLE-ABS-KEY (spain OR granada)) AND (LIMIT-TO (DOCTYPE, “article”)). With this type of search, the objective was to find scientific articles that dealt with the Sierra Nevada mountain range located in the province of Granada, Spain. As there were other mountain ranges with the same name in other parts of the world, the term Spain or Granada was added to the search. It searched through scientific articles, without a time limit. This search found 623 articles. The second step was the processing of the information for storage. After the pertinent manual verification work (the geolocation of the documents is automatic, not the selection of the sample), there are 447 documents with a complete associated pdf (not only the abstract or title) and which are openly accessible, the other 176 (623-447) records do not have an associated pdf, are incomplete or are not an open publication, of which 424 are about the Spanish Sierra Nevada and not about the Sierra Nevada of California or Peru.

The third step was the identification of the coordinates. Although there are many types of coordinate systems, the most common that this project has found in the sample are the following types and subtypes of coordinates: the geographic coordinate system and the coordinate system UTM (Universal Transverse Mercator). The geographic coordinate system is subdivided into sexagesimal (degrees, minutes, and seconds) and decimal (degrees and decimals). The UTM coordinate has multiple variants in that the authors express them in different ways, with or without the letters of the cardinal points, specifying the use or not (in our case it is 30S). Finally, in environmental studies, they usually use a subdivision of the use 30S, which is used in army maps, and which is reflected in articles with VG, VF, WG and WF. Finally, our database contains 424 bibliographic references linked to their corresponding PDFs. This database has been used for the design and training of the algorithm. The database is accessible at https://doi.org/10.5281/zenodo.5633963

For the geolocation of the contributions, we have developed a Text Mining Algorithm based on the extraction of information through regular expressions and toponyms - keywords (see Figure 1). To carry out this automatic geolocation process, we design an algorithm based on 4 stages:

I-Preprocessing: in this stage, the contributions of the sample are processed in PDF format, converting the text of each one to a processable format (plain text), normalized following the next steps:

For the conversion of PDF files to plain text, we have used pdftotext which is an open-source command-line utility and Python (version 3.10.0) as programming language.

II-Extraction: search and extraction of geographical references in the text using two methods, (i) regular expressions for the search of geographical coordinates in their different formats (decimal, sexagesimal and UTM) and (ii) search for the frequency of appearance of place names. The database of toponyms for this study has been downloaded from the Nomenclator Geográfico de Andalucía (NGA) of the Instituto de Estadística y Cartografía de Andalucía (Instituto de Estadística y Cartografía de Andalucía, 2022Instituto de Estadística y Cartografía de Andalucía. (2022). Toponimía-Nomenclátor. Available at: https://www.juntadeandalucia.es/institutodeestadisticaycartografia/prodCartografia/toponimia/index.htm#:~:text=El%20Nomencl%C3%A1tor%20Geogr%C3%A1fico%20de%20Andaluc%C3%ADa,%2C%20extractivas%2C%20servicios%20y%20equipamientos. ). This database has been downloaded using the interoperable Web Feature Server (WFS) services. With the help of the QGIS software and using a SQL-like WHERE, the toponyms that interest us have been downloaded and exported in xlsx spreadsheet format.

III-Transformation: to georeferenced the results obtained on an interactive map; all the results are processed to unify them in decimal format (latitude, longitude). To convert between the different geographic representations, we have used Proj4js which is a library to transform point coordinates from one coordinate system to another, including datum transformations.

IV-Analysis: finally, once we have all the information that has been extracted in the same format, a small analysis is carried out to decide, through heuristic behavior, if it is possible to geolocate the contribution. For this, we have mainly followed three criteria: (i) if geographic coordinates and place names have been found, we combine this information to select the coordinate that is textually closest to the most frequent place name; (ii) if only geographic coordinates have been identified, it is geolocated within the most referenced area; and (iii) if only toponyms have been recognized, we geolocate it in the one with the highest frequency as long as it exceeds a certain threshold (this threshold is calculated as the simple mean of the frequencies of the toponyms in the entire sample of this study).

It is important to note that, in this first version of the algorithm, we base the geographic reference extraction process on the combination of information from two methods:

Once this automatic process is finished and in order to check if there is a relationship between the georeferencing of contributions and their bibliometric information, we have included all the available metrics through an Almetrics report.

To do this we have linked the results of the bibliometric report with the dataset of contributions through its DOI, presenting special interest to the variables: (i) Altmetric Attention Score, (ii) Twitter mentions, (iii) Mendeley readers and (iv) citations. This information aggregation process has been carried out with automatic processing that matchs the Altmetrics report with the contributions of the initial dataset through its DOI and with the help of the Python programming language.

4. RESULTS

Table I shows a summary of the results of the algorithm of automatic detection of geolocations being applied to the collection of 424 scientific articles on Sierra Nevada. In total, the algorithm has been able to geolocate 157 articles with coordinates, 37% of the total number of articles analysed in the study. In this research, all the articles that contain geographic coordinates (205) have been manually identified to test the platform, in their different variables, included in text or image. In table 1 we can see the performance of our algorithm in detail on the total number of articles with geographic coordinates. One of the tools used to increase the number of geolocated articles and elements has been the use of place names that have allowed us to geolocate 512 place names of 220 different articles. There are 5.84 place names on average per scientific article. Finally, the number of works that have been geolocated using the algorithm is 377, that is to say 88.9% of the original document collection has been located, either by coordinates or toponyms. It should be mentioned that one of the fundamental aspects to improve the success rate of the algorithm has been the use of place names.

Cases for which an article may not be georeferenced in our proposed pipeline:

With the completion of objective 1, the team shifted their focus to developing a software application that would allow users to view scientific articles on a digital map based on the geographical information they contain. The resulting portal, which is currently in beta format and can be accessed at https://geoacademy.everyware.es/ (Geoacademy, 2022Geoacademy. (2022). Geoacademy. Available at: https://geoacademy.everyware.es/.), features the collection of documents about Sierra Nevada that was analyzed in the study. By clicking on each marker on the map (as shown in Figure 2), users can access information about the paper and a link to view it. The filter function allows users to search for articles based on type (such as articles, conference papers, or theses), or by keyword. The portal also includes a traditional search engine with filters and a search radius, as well as tabs that describe the project and its members and provide a contact form. In addition to the metadata from the databases (in this case, Scopus), each processed scientific work includes metadata generated by the algorithm, such as the decimal format coordinates and a complete list of identified place names, which are added to the bibliographic description.

The third objective of this work is to provide the platform with a layer of information capable of representing information of a bibliometric and altmetric nature, that is, of scientific and social impact. In this sense, the GeoAcademy platform has been provided with the following functionalities 1) Filter locations based on indicators (Number of citations, Altmetric Attention Score) 2) Use of geoposition markers differentiated according to values of the indicators. 3) The platform will present a bibliometric summary of the different coordinates and locations, offering bibliometric information at a level not currently seen.

An example of this last point can be seen in the following table. Table 2 shows the main toponyms ordered by frequency of occurrence in our database. These toponyms have been taken from the public databases of the Instituto Andaluz de Estadística y Cartografía. With the visualization of the table we can see the most studied toponyms of Sierra Nevada.

5. DISCUSSION

We believe that in this study a profound approach has been taken for the creation of an algorithm that is useful for researchers and society in general. With the chosen sample, its operation has been tested intensively, however we are aware there is still room for improvement.

This study contains a number of limitations that we proceed to list and which have mainly to do with processing. The first problem relates to the processing of PDF documents. We found several that did not have an associated pdf, for others the pdf requires payment, or only the title or the title and abstract are available. This limits the number of results you can cover. We hope that in the future the current trend of improvements in open science will continue, overcoming this limitation. Secondly, the coordinates have some formal standardization, whereas in practice each field of knowledge has different guidelines for writing the coordinates. There is greater variability with the UTMs that the algorithm has overcome without problems. In future updates of the algorithm we hope to resolve all the multiple variables found. Another problem is that several studies show the coordinates in image form, that makes them more difficult to extract, although in the future through OCR technology they could be captured. In future updates of the algorithm we will incorporate two Python libraries that are demonstrating promising results to extract text from images; OpenCV (Open Source Computer Vision Library) (https://opencv.org/) and Tesseract (https://github.com/tesseract-ocr/tesseract). At the linguistic level, words with multiple means in the toponymy can be problematic, which can give both false positives and false negatives. Also, the approach does not allow more generic descriptions like ‘mountain ranges in France’ or ‘forests in Spain’ to be included while these such studies may still be relevant. The most effective way to solve these last two problems is through semantic searches, disambiguation, and the creation of semantic networks.

Regarding the comparison with other proposals, the difference with our project is that in GEOUP4 each document is geolocated manually, this is done by the library services. Although they are studying ways to do it automatically with natural language processing, so far it is a manual process. JournalMap, on the other hand, uses a dual approach, geolocating with automatic and manual techniques to determine geographic locations. If the coordinates can be detected, it generates the location automatically; if they cannot be detected or there are only place names, they are geotagged manually by the authors who upload the paper to the platform. With respect to our project there are two differences, firstly our algorithm also detects place names and secondly our project does not need the authors to upload their papers, rather they are downloaded by us from the database. The success rate of the Geoacademy algorithm on all documents is 88.9%, a high percentage for this type of study, especially if we consider that it is made on a general and interdisciplinary collection of documents (within our database there are studies of earth sciences, anthropology, physics and geography, amongst others), not only on a specific and well-defined scientific field. This is one of the main criticisms of Gritta et al. (2018)Gritta, M., Pilehvar, M. T., Limsopatham, N., & Collier, N. (2018). What’s missing in geographical parsing? Language Resources and Evaluation, 52(2), 603-623. DOI: https://doi.org/10.1007/s10579-017-9385-8. about the tendency to develop tools for very specific tasks and corpus. Putting to one side the distances and taking into account the different techniques developed and forms of evaluation of each study and software, the research of Gritta et al. (2018)Gritta, M., Pilehvar, M. T., Limsopatham, N., & Collier, N. (2018). What’s missing in geographical parsing? Language Resources and Evaluation, 52(2), 603-623. DOI: https://doi.org/10.1007/s10579-017-9385-8. that compares 5 software programs offers accuracy of between 81 and 21 percent when extracting place names. Something similar happens with Acheson’s research (Acheson and Purves, 2021Acheson, E., & Purves, R. S. (2021). Extracting and modeling geographic information from scientific articles. PloS one, 16(1), e0244918. DOI: https://doi.org/10.1371/journal.pone.0244918.), with a toponym extraction rate of between 81 and 86 percent depending on the corpus.

We will continue to train the algorithm with much larger documentary collections related to other geographical features in order to reduce its limitations. A potential improvement would be to create a directory of place names for each search or study, making the process semi-automatic, however there are geocoding services such as Google Geocoding API, GeoNames or OpenStreetMap (OSM) Nominatim that would help us with this task, in addition to NER (Named-Entity Recognition) that would facilitate extracting information from the previous geocoding services. Likewise, it will be applied in other contexts, such as archaeological where most of the articles include precise geographic coordinates. It could also be applied to digital humanities, since interesting projects are being carried out where historical works are geolocated through the place names that appear in them.

Future developments could include its inclusion with an industry tool such as Bibliometrix. It would also be very interesting if users of large scientific databases such as Scopus or Web of Science, having performed a search with a list of results, could see said list geographically on a map, applying our algorithm for a better user experience. That is to say, to integrate our project in their platforms. Finally, numerous studies do not work with geographical points, we are currently working on how to show these studies on our platform.