Learning and Teaching with Geomedia

International audienceThis book presents a range of educational approaches and ideas around learning with and about geomedia, taking into account the potential as well as the risks and challenges associated with the use geomedia in education. This includes understanding the inherently biasednature of maps and other spatial representations; the power of geomedia produced by institutions, as well as the power of geomedia produced by lay persons; the use of geoinformation in the control and surveillance of individuals and groups, and the opportunities of georeferenced data to foster innovative knowledge. Addressing these aspects in essence means preparing students for an emerging Geoinformation society

Proceedings of the Academic Track at State of the Map 2019 - Heidelberg (Germany), September 21-23, 2019

State of the Map featured a full day of academic talks. Building upon the motto of SotM 2019 in "Bridging the Map" the Academic Track session was aimed to provide the bridge to join together the experience, understanding, ideas, concepts and skills from different groups of researchers, academics and scientists from around the world. In particular, the Academic Track session was meant to build this bridge that connects members of the OpenStreetMap community and the academic community by providing an open passage for exchange of ideas, communication and opportunities for increased collaboration. These proceedings include 14 abstracts accepted as oral presentations and 6 abstracts presented as posters. Contributions were received from different academic fields, for example geography, remote sensing, computer and information sciences, geomatics, GIScience, the humanities and social sciences, and even from industry actors. We are particularly delighted to have included abstracts from both experienced researchers and students. Overall, it is our hope that these proceedings accurately showcase the ongoing innovation and maturity of scientific investigations and research into OpenStreetMap, showing how it as a research object converges multiple research areas together. Our aim is to show how the sum total of investigations of issues like Volunteered Geographic Information, geo-information, and geo-digital processes and representation shed light on the relations between crowds, real-world applications, technological developments, and scientific research

Suomenkielisen geojäsentimen kehittäminen: kuinka hankkia sijaintitietoa jäsentelemättömistä tekstiaineistoista

Alati enemmän aineistoa tuotetaan ja jaetaan internetin kautta. Aineistot ovat vaihtelevia muodoiltaan, kuten verkkoartikkelien ja sosiaalisen media julkaisujen kaltaiset digitaaliset tekstit, ja niillä on usein spatiaalinen ulottuvuus. Teksteissä geospatiaalisuutta ilmaistaan paikannimien kautta, mutta tavanomaisilla paikkatietomenetelmillä ei kyetä käsittelemään tietoa epätäsmällisessä kielellisessä asussaan. Tämä on luonut tarpeen muuntaa tekstimuotoisen sijaintitiedon näkyvään muotoon, koordinaateiksi. Ongelmaa ratkaisemaan on kehitetty geojäsentimiä, jotka tunnistavat ja paikantavat paikannimet vapaista teksteistä, ja jotka oikein toimiessaan voisivat toimia paikkatiedon lähteenä maantieteellisessä tutkimuksessa. Geojäsentämistä onkin sovellettu katastrofihallinnasta kirjallisuudentutkimukseen. Merkittävässä osassa geojäsentämisen tutkimusta tutkimusaineiston kielenä on ollut englanti ja geojäsentimetkin ovat kielikohtaisia – tämä jättää pimentoon paitsi geojäsentimien kehitykseen vaikuttavat havainnot pienemmistä kielistä myös kyseisten kielten puhujien näkemykset. Maisterintutkielmassani pyrin vastaamaan kolmeen tutkimuskysymykseen: Mitkä ovat edistyneimmät geojäsentämismenetelmät? Mitkä kielelliset ja maantieteelliset monitulkintaisuudet vaikeuttavat tämän monitahoisen ongelman ratkaisua? Ja miten arvioida geojäsentimien luotettavuutta ja käytettävyyttä? Tutkielman soveltavassa osuudessa esittelen Fingerin, geojäsentimen suomen kielelle, ja kuvaan sen kehitystä sekä suorituskyvyn arviointia. Arviointia varten loin kaksi testiaineistoa, joista toinen koostuu Twitter-julkaisuista ja toinen uutisartikkeleista. Finger-geojäsennin, testiaineistot ja relevantit ohjelmakoodit jaetaan avoimesti. Geojäsentäminen voidaan jakaa kahteen alitehtävään: paikannimien tunnistamiseen tekstivirrasta ja paikannimien ratkaisemiseen oikeaan koordinaattipisteeseen mahdollisesti useasta kandidaatista. Molemmissa vaiheissa uusimmat metodit nojaavat syväoppimismalleihin ja -menetelmiin, joiden syötteinä ovat sanaupotusten kaltaiset vektorit. Geojäsentimien suoriutumista testataan aineistoilla, joissa paikannimet ja niiden koordinaatit tiedetään. Mittatikkuna tunnistamisessa on vastaavuus ja ratkaisemisessa etäisyys oikeasta sijainnista. Finger käyttää paikannimitunnistinta, joka hyödyntää suomenkielistä BERT-kielimallia, ja suoraviivaista tietokantahakua paikannimien ratkaisemiseen. Ohjelmisto tuottaa taulukkomuotoiseksi jäsenneltyä paikkatietoa, joka sisältää syötetekstit ja niistä mahdollisesti tunnistetut paikannimet koordinaattisijainteineen. Testiaineistot eroavat aihepiireiltään, mutta Finger suoriutuu niillä likipitäen samoin, ja suoriutuu englanninkielisillä aineistoilla tehtyihin arviointeihin suhteutettuna kelvollisesti. Virheanalyysi paljastaa useita virhelähteitä, jotka johtuvat kielten tai maantieteellisen todellisuuden luontaisesta epäselvyydestä tai ovat prosessoinnin aiheuttamia, kuten perusmuotoistamisvirheet. Kaikkia osia Fingerissä voidaan parantaa, muun muassa kehittämällä kielellistä käsittelyä pidemmälle ja luomalla kattavampia testiaineistoja. Samoin tulevaisuuden geojäsentimien tulee kyetä käsittelemään monimutkaisempia kielellisiä ja maantieteellisiä kuvaustapoja kuin pelkät paikannimet ja koordinaattipisteet. Finger ei nykymuodossaan tuota valmista paikkatietoa, jota kannattaisi kritiikittä käyttää. Se on kuitenkin lupaava ensiaskel suomen kielen geojäsentimille ja astinlauta vastaisuuden soveltavalle tutkimukselle.Ever more data is available and shared through the internet. The big data masses often have a spatial dimension and can take many forms, one of which are digital texts, such as articles or social media posts. The geospatial links in these texts are made through place names, also called toponyms, but traditional GIS methods are unable to deal with the fuzzy linguistic information. This creates the need to transform the linguistic location information to an explicit coordinate form. Several geoparsers have been developed to recognize and locate toponyms in free-form texts: the task of these systems is to be a reliable source of location information. Geoparsers have been applied to topics ranging from disaster management to literary studies. Major language of study in geoparser research has been English and geoparsers tend to be language-specific, which threatens to leave the experiences provided by studying and expressed in smaller languages unexplored. This thesis seeks to answer three research questions related to geoparsing: What are the most advanced geoparsing methods? What linguistic and geographical features complicate this multi-faceted problem? And how to evaluate the reliability and usability of geoparsers? The major contributions of this work are an open-source geoparser for Finnish texts, Finger, and two test datasets, or corpora, for testing Finnish geoparsers. One of the datasets consists of tweets and the other of news articles. All of these resources, including the relevant code for acquiring the test data and evaluating the geoparser, are shared openly. Geoparsing can be divided into two sub-tasks: recognizing toponyms amid text flows and resolving them to the correct coordinate location. Both tasks have seen a recent turn to deep learning methods and models, where the input texts are encoded as, for example, word embeddings. Geoparsers are evaluated against gold standard datasets where toponyms and their coordinates are marked. Performance is measured on equivalence and distance-based metrics for toponym recognition and resolution respectively. Finger uses a toponym recognition classifier built on a Finnish BERT model and a simple gazetteer query to resolve the toponyms to coordinate points. The program outputs structured geodata, with input texts and the recognized toponyms and coordinate locations. While the datasets represent different text types in terms of formality and topics, there is little difference in performance when evaluating Finger against them. The overall performance is comparable to the performance of geoparsers of English texts. Error analysis reveals multiple error sources, caused either by the inherent ambiguousness of the studied language and the geographical world or are caused by the processing itself, for example by the lemmatizer. Finger can be improved in multiple ways, such as refining how it analyzes texts and creating more comprehensive evaluation datasets. Similarly, the geoparsing task should move towards more complex linguistic and geographical descriptions than just toponyms and coordinate points. Finger is not, in its current state, a ready source of geodata. However, the system has potential to be the first step for geoparsers for Finnish and it can be a steppingstone for future applied research

Volunteered Geographic Information: a 10-year bibliometric investigation

Volunteered Geographic Information (VGI) has become more evident at the same time as open-source platforms become worldwide popular, both resulting from people easily accessing geographic information on their smartphones. Aiming to investigate the main aspects of this research field, a bibliometric investigation was developed focusing on 10-year period (2011-2020). The analyses were performed based on Scopus database, VOS Viewer and Bibliometrix softwares, approaching: publications over years, document types, subject areas, core sources, main papers, countries, authors and most recurrent keywords. The initial results indicated that: publications have increased at an annual rate of 21.69%, the most published document type was article and only 16 journals were responsible for 33.33% of those 1200 articles published. USA, Germany and UK are major countries researching VGI and the last two are also host countries of the main authors. Although the term VGI has been defined among Citizen Science, the network of keywords occurrence showed that GIS (Geographic Information Systems) is an outstanding study field. However, the network visualization based on average publication per year revealed Citizen Science as a research field still moving forward. Keywords such as OpenStreetMap, data quality, accuracy assessment, social media and crowdsourcing showed to be more widespread among the field, the opposite occurs with applications in urban areas, land use and ecosystem services. Overall, the bibliometric indicators have revealed to be effective in order to access VGI as a research topic and indicated a promising trend in themes involving social media, remote sensing, urban area, crowdsourcing and PPGIS.A Informação Geográfica Voluntária (VGI) tornou-se mais evidente ao mesmo tempo em que as plataformas de código aberto se tornaram populares em todo o mundo, ambas resultantes do fácil acesso das pessoas às informações geográficas em seus smartphones. Com o objetivo de investigar os principais aspectos deste campo de pesquisa, foi desenvolvida uma investigação bibliométrica com foco num período de 10 anos (2011-2020).  A análise foi realizada com base no banco de dados Scopus e nos softwares VOS Viewer e Bibliometrix, abordando: publicações ao longo dos anos, tipos de documentos, campos de estudo, principais periódicos, principais artigos, países, autores e palavras-chave mais recorrentes. Os resultados iniciais indicaram que: as publicações aumentaram a uma taxa anual de 21.69%, o tipo de documento mais publicado foi artigo e apenas 16 periódicos foram responsáveis por 33.33% dos 1200 artigos publicados. EUA, Alemanha e Reino Unido são os principais países que pesquisam VGI e os dois últimos também são países-sede dos principais autores. Apesar do termo VGI ter sido definido em meio a Ciência Cidadã, a rede de ocorrência de palavras-chave mostrou que SIG (Sistema de Informação Geográfica) é um campo de estudo de destaque. Contudo, a rede de visualização com base em média de publicações por ano revelou a Ciência Cidadã como um campo de pesquisa ainda em avanço. Palavras-chave como OpenStreetMap, qualidade dos dados, avaliação da precisão, mídias sociais e coletividade mostraram-se mais difundidas no campo, o oposto ocorre com aplicações em áreas urbanas, uso do solo e serviços ecossistêmicos. No geral, os indicadores bibliométricos revelaram-se eficazes para acessar a VGI como tópico de pesquisa e indicaram uma tendência promissora em temas envolvendo redes sociais, sensoriamento remoto, área urbana, colaboração coletiva e PPGIS

MethOSM: a methodology for computing composite indicators derived from OpenStreetMap data

The task of computing composite indicators to define and analyze complex social, economic, political, or environmental phenomena has traditionally been the exclusive competence of statistical offices. Nowadays, the availability of increasing volumes of data and the emergence of the open data movement have enabled individuals and businesses affordable access to all kinds of datasets that can be used as valuable input to compute indicators. OpenStreetMap (OSM) is a good example of this. It has been used as a baseline to compute indicators in areas where official data is scarce or difficult to access. Although the extraction and application of OSM data to compute indicators is an attractive proposition, this practice is by no means hassle-free. The use of OSM reveals a number of challenges that are usually addressed with ad-hoc and often overlapping solutions. In this context, this paper proposes MethOSM-a systematic methodology for computing indicators derived from OSM data. By applying MethOSM, the computation task is divided into four steps, with each step having a clear goal and a set of guidelines to apply. In this way, the methodology contributes to an effective and efficient use of OSM data for the purpose of computing indicators. To demonstrate its use, we apply MethOSM to a number of indicators used for real estate valuation of properties in Italy

European Handbook of Crowdsourced Geographic Information

This book focuses on the study of the remarkable new source of geographic information that has become available in the form of user-generated content accessible over the Internet through mobile and Web applications. The exploitation, integration and application of these sources, termed volunteered geographic information (VGI) or crowdsourced geographic information (CGI), offer scientists an unprecedented opportunity to conduct research on a variety of topics at multiple scales and for diversified objectives. The Handbook is organized in five parts, addressing the fundamental questions: What motivates citizens to provide such information in the public domain, and what factors govern/predict its validity?What methods might be used to validate such information? Can VGI be framed within the larger domain of sensor networks, in which inert and static sensors are replaced or combined by intelligent and mobile humans equipped with sensing devices? What limitations are imposed on VGI by differential access to broadband Internet, mobile phones, and other communication technologies, and by concerns over privacy? How do VGI and crowdsourcing enable innovation applications to benefit human society? Chapters examine how crowdsourcing techniques and methods, and the VGI phenomenon, have motivated a multidisciplinary research community to identify both fields of applications and quality criteria depending on the use of VGI. Besides harvesting tools and storage of these data, research has paid remarkable attention to these information resources, in an age when information and participation is one of the most important drivers of development. The collection opens questions and points to new research directions in addition to the findings that each of the authors demonstrates. Despite rapid progress in VGI research, this Handbook also shows that there are technical, social, political and methodological challenges that require further studies and research

Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management

Coupling ground-level panoramas and aerial imagery for change detection

International audienceGeographic landscapes in all over the world may be subject to rapid changes induced, for instance, by urban, forest, and agricultural evolutions. Monitoring such kind of changes is usually achieved through remote sensing. However, obtaining regular and up-to-date aerial or satellite images is found to be a high costly process, thus preventing regular updating of land cover maps. Alternatively, in this paper, we propose a low-cost solution based on the use of ground-level geo-located landscape panoramic photos providing high spatial resolution information of the scene. Such photos can be acquired from various sources: digital cameras, smartphone, or even web repositories. Furthermore, since the acquisition is performed at the ground level, the users' immediate surroundings, as sensed by a camera device, can provide information at a very high level of precision, enabling to update the land cover type of the geographic area. In the described herein method, we propose to use inverse perspective mapping (inverse warping) to transform the geo-tagged ground-level 360 • photo onto a top-down view as if it had been acquired from a nadiral aerial view. Once re-projected, the warped photo is compared to a previously acquired remotely sensed image using standard techniques such as correlation. Wide differences in orientation, resolution, and geographical extent between the top-down view and the aerial image are addressed through specific processing steps (e.g. registration). Experiments on publicly available data-sets made of both ground-level photos and aerial images show promising results for updating land cover maps with mobile technologies. Finally, the proposed approach contributes to the crowdsourcing efforts in geo-information processing and mapping, providing hints on the evolution of a landscape. ARTICLE HISTOR

Automatic reconstruction of itineraries from descriptive texts

Esta tesis se inscribe dentro del marco del proyecto PERDIDO donde los objetivos son la extracción y reconstrucción de itinerarios a partir de documentos textuales. Este trabajo se ha realizado en colaboración entre el laboratorio LIUPPA de l' Université de Pau et des Pays de l' Adour (France), el grupo de Sistemas de Información Avanzados (IAAA) de la Universidad de Zaragoza y el laboratorio COGIT de l' IGN (France). El objetivo de esta tesis es concebir un sistema automático que permita extraer, a partir de guías de viaje o descripciones de itinerarios, los desplazamientos, además de representarlos sobre un mapa. Se propone una aproximación para la representación automática de itinerarios descritos en lenguaje natural. Nuestra propuesta se divide en dos tareas principales. La primera pretende identificar y extraer de los textos describiendo itinerarios información como entidades espaciales y expresiones de desplazamiento o percepción. El objetivo de la segunda tarea es la reconstrucción del itinerario. Nuestra propuesta combina información local extraída gracias al procesamiento del lenguaje natural con datos extraídos de fuentes geográficas externas (por ejemplo, gazetteers). La etapa de anotación de informaciones espaciales se realiza mediante una aproximación que combina el etiquetado morfo-sintáctico y los patrones léxico-sintácticos (cascada de transductores) con el fin de anotar entidades nombradas espaciales y expresiones de desplazamiento y percepción. Una primera contribución a la primera tarea es la desambiguación de topónimos, que es un problema todavía mal resuelto dentro del reconocimiento de entidades nombradas (Named Entity Recognition - NER) y esencial en la recuperación de información geográfica. Se plantea un algoritmo no supervisado de georreferenciación basado en una técnica de clustering capaz de proponer una solución para desambiguar los topónimos los topónimos encontrados en recursos geográficos externos, y al mismo tiempo, la localización de topónimos no referenciados. Se propone un modelo de grafo genérico para la reconstrucción automática de itinerarios, donde cada nodo representa un lugar y cada arista representa un camino enlazando dos lugares. La originalidad de nuestro modelo es que además de tener en cuenta los elementos habituales (caminos y puntos del recorrido), permite representar otros elementos involucrados en la descripción de un itinerario, como por ejemplo los puntos de referencia visual. Se calcula de un árbol de recubrimiento mínimo a partir de un grafo ponderado para obtener automáticamente un itinerario bajo la forma de un grafo. Cada arista del grafo inicial se pondera mediante un método de análisis multicriterio que combina criterios cualitativos y cuantitativos. El valor de estos criterios se determina a partir de informaciones extraídas del texto e informaciones provenientes de recursos geográficos externos. Por ejemplo, se combinan las informaciones generadas por el procesamiento del lenguaje natural como las relaciones espaciales describiendo una orientación (ej: dirigirse hacia el sur) con las coordenadas geográficas de lugares encontrados dentro de los recursos para determinar el valor del criterio ``relación espacial''. Además, a partir de la definición del concepto de itinerario y de las informaciones utilizadas en la lengua para describir un itinerario, se ha modelado un lenguaje de anotación de información espacial adaptado a la descripción de desplazamientos, apoyándonos en las recomendaciones del consorcio TEI (Text Encoding and Interchange). Finalmente, se ha implementado y evaluado las diferentes etapas de nuestra aproximación sobre un corpus multilingüe de descripciones de senderos y excursiones (francés, español, italiano)