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ABSTRACT Geographic knowledge graphs (KGs) mainly describe static facts and have difficulty representing changes, greatly limiting their application in geographic information retrieval and geographic spatio‐temporal processes. By analyzing the spatio‐temporal features and evolution of geographic elements, this paper measures the degree of correlation between spatio‐temporal information and tuples and further accurately characterizes the semantic knowledge of tuples to support accurate computation and inference of KGs. This paper proposes a novel knowledge‐guided quantitative measure framework for spatio‐temporal correlation by considering rules and dependency syntax from natural language texts. Firstly, the natural language processing (NLP) stage preprocess the texts and extracts the candidate tuples by dependency syntactic analysis and rule matching. Secondly, we model the spatio‐temporal correlation measures by considering semantic (entity types and tuple predicate) and syntactic features (dependency distance and dependency path). Finally, we establish a specific threshold value with the extracted candidates and performing multiple levels of categorization to form the final spatio‐temporal correlation strength (strong, moderate, and weak). The experimental results with a large dataset indicate that the proposed method achieves an F‐score of over 0.73, which is better than those of the existing methods. The proposed spatio‐temporal correlation framework has more advantages in representing geographic evolutionary knowledge, revealing the evolution mechanism of geographic elements and the evolutionary reasons. [ABSTRACT FROM AUTHOR]

A Minimum Bounding Box (MBB) is a rectangle which bounds a geographic feature or dataset. It is commonly used in spatial information systems as a simplified way of describing the spatial extent of a resource. MBBs are typically indexed for searching and discovering resources relevant to a given geographic area of interest. However, this simplification leads to a loss of precision in the description of the extent and can affect the overall precision of the search results. We propose an alternative technique for describing the spatial extent based on the use of DGGS tiles. To measure the precision improvements offered by our method, we designed and implemented an empirical method for evaluating the average precision, and applied it to three different systems: one based on MBB, another on Convex Hull, and ours based on DGGS. The three methods were evaluated with the same test collection obtained from some of the main European geospatial data catalogues compliant with the INSPIRE directive. The results showed that our method outperformed the other two. Where the catalogue average precision of the MBB search scenarios is between 73% and 97%, the DGGS is between 96% and 99%. Additionally, we propose a realistic method of transitioning from the current technologies to the technology we are proposing, considering the current state of the spatial data infrastructures. [ABSTRACT FROM AUTHOR]

ABSTRACTMultiple tasks within the field of geographical information retrieval and geographical information sciences necessitate toponym matching, which involves the challenge of aligning toponyms that share a common referent. The multiple string similarity approaches struggle when confronted with the complexities associated with unofficial and/or historical variants of identical toponyms. Also, current state-of-the-art approaches/tools to supervised machine learning rely on labeled samples, and they do not adequately address the intricacies of character replacements either from transliterations or historical shifts in linguistic and cultural norms. To address these issues, this paper proposes a novel matching approach that leverages a deep neural network model empowered by geographic language representation model, known as GeoBERT, which stands for geographic Bidirectional Encoder Representations from Transformers (BERT). This model harnesses the groundbreaking capabilities of the GeoBERT framework by extending a generalized Enhanced Sequential Inference Model architecture and integrating multiple features to enhance the accuracy and robustness of the toponym matching. We present a comprehensive evaluation of the proposed method’s performance using three extensive datasets. The findings clearly illustrate that our approach outperforms the individual similarity metrics used in previous studies.

Describing where a place is situated is an innate communication skill that relies on spatial cognition, spatial reasoning, and linguistic systems. Accordingly, textual geolocation, a task for retrieving the coordinates of a place from linguistic descriptions, requires computerized spatial inference and natural language understanding. Yet, machine-based textual geolocation is currently limited, mainly due to the lack of rich geo-textual datasets necessitated to train natural language models that, in-turn, cannot adequately interpret the language-based expressions. These limitations are intensified in morphologically rich and resource-poor languages, such as Hebrew. This study aims to analyze and understand the linguistic systems used for place descriptions in Hebrew, later to be used to train machine learning natural language models. A novel crowdsourced geo-textual dataset is developed, composed of 5,695 written place descriptions provided by 1,554 native Hebrew speakers. All place descriptions rely on memory only, which increases spatial vagueness and requires referring expression resolution. Qualitative linguistic analysis of place descriptions shows that geospatial reasoning is greatly used in Hebrew, while empirical analysis with textual geolocation engines indicates that literal descriptions pose challenges for existing methods, as they require real understanding of space and geospatial references and cannot simply be geolocated by matching gazetteer with textual geo-entity extractions. The findings offer improved understanding of the challenges entailed in natural language processing of Hebrew geolocation, contributing to formalizing computerized systems used in future machine learning models for complex geographic information retrieval tasks.

Describing where a place is situated is an innate communication skill that relies on spatial cognition, spatial reasoning, and linguistic systems. Accordingly, textual geolocation, a task for retrieving the coordinates of a place from linguistic descriptions, requires computerized spatial inference and natural language understanding. Yet, machine-based textual geolocation is currently limited, mainly due to the lack of rich geo-textual datasets necessitated to train natural language models that, in-turn, cannot adequately interpret the language-based expressions. These limitations are intensified in morphologically rich and resource-poor languages, such as Hebrew. This study aims to analyze and understand the linguistic systems used for place descriptions in Hebrew, later to be used to train machine learning natural language models. A novel crowdsourced geo-textual dataset is developed, composed of 5,695 written place descriptions provided by 1,554 native Hebrew speakers. All place descriptions rely on memory only, which increases spatial vagueness and requires referring expression resolution. Qualitative linguistic analysis of place descriptions shows that geospatial reasoning is greatly used in Hebrew, while empirical analysis with textual geolocation engines indicates that literal descriptions pose challenges for existing methods, as they require real understanding of space and geospatial references and cannot simply be geolocated by matching gazetteer with textual geo-entity extractions. The findings offer improved understanding of the challenges entailed in natural language processing of Hebrew geolocation, contributing to formalizing computerized systems used in future machine learning models for complex geographic information retrieval tasks. [ABSTRACT FROM AUTHOR]

Case-based reasoning (CBR) simplifies knowledge acquisition and is suitable for researching complex geographical problems. However, CBR analyses of land-use changes are difficult to apply in the study of urban growth due to shortcomings in the case structure and model algorithms. In response, this study proposes a three-step urban-growth simulation model based on CBR (UGSCBR). First, to adapt the CBR to an urban-growth simulation process, the characteristics of regional differentiation in geographical spaces are determined. Second, a comprehensive retrieval method is developed that improves upon traditional case-retrieval methods by giving full play to the comprehensive function of each component of the case. Third, a quantity demand constraint indirectly adds a time factor to solve the initial blurriness of the traditional CBR-inference cycle. Taking Jixi city as the research area, we test the accuracy of the proposed model. The total accuracy of simulation results is 95.4%, and the Kappa is 87.4%. The figure of merit and Mathews correlation coefficient are 0.151 and 0.23, respectively, indicating that the model can meet the application requirements. The results show that the UGSCBR model has strong flexibility and simplicity, and it provides an effective prediction method for urban growth.

This paper is concerned with interpretation of natural language place descriptions, as they are a rich source of geographic information. A place description is interpreted by matching geographic entities occurring in the text against the OpenStreetMap (OSM) database. This paper is mainly concerned with interpretation of paraphrased places, i.e., entities for which no name is given and which may only by described. Our objective is to determine suitable entity types that allow querying the OpenStreetMap database for the respective place. For example, if we wish to identify a place to eat, we have to check for entities of an a-priori unknown type (cafe, restaurant, etc.). Challenges arise from the open-endedness of language, its ambiguity, and context-sensitivity as well as from mismatches between human conceptualization of place and database ontologies. The contributions of this paper are, first, to present a hard problem that is key to geo-information retrieval beyond named entities. Second, we propose context-sensitive methods for identifying place types based on semantic word similarity. We evaluate the methods on text extracted from Wikipedia and travel blogs, revealing their contribution to advancing automated interpretation of place descriptions to paraphrased places. [ABSTRACT FROM AUTHOR]

Case-based reasoning (CBR) simplifies knowledge acquisition and is suitable for researching complex geographical problems. However, CBR analyses of land-use changes are difficult to apply in the study of urban growth due to shortcomings in the case structure and model algorithms. In response, this study proposes a three-step urban-growth simulation model based on CBR (UGSCBR). First, to adapt the CBR to an urban-growth simulation process, the characteristics of regional differentiation in geographical spaces are determined. Second, a comprehensive retrieval method is developed that improves upon traditional case-retrieval methods by giving full play to the comprehensive function of each component of the case. Third, a quantity demand constraint indirectly adds a time factor to solve the initial blurriness of the traditional CBR-inference cycle. Taking Jixi city as the research area, we test the accuracy of the proposed model. The total accuracy of simulation results is 95.4%, and the Kappa is 87.4%. The figure of merit and Mathews correlation coefficient are 0.151 and 0.23, respectively, indicating that the model can meet the application requirements. The results show that the UGSCBR model has strong flexibility and simplicity, and it provides an effective prediction method for urban growth. [ABSTRACT FROM AUTHOR]

Spatial language incorporates descriptions of locations, routes, and landscapes, and is used by humans daily. Research has addressed a wide range of aspects of spatial language, including its form; the ways in which it is selected and applied; and cognitive, geometric, and functional factors affecting its use. Furthermore, much work has been done on the automation of spatial language extraction, analysis, interpretation, and generation. To introduce the Special Issue on this broad topic, this paper reviews spatial language research framed by an extension to the well-known semantic triangle, the "spatial semantic pyramid," which represents both human spatial language and relevant computational research. By introducing it, we hope to stimulate discussion about gaps and future directions in this important research field. [ABSTRACT FROM AUTHOR]

The availability of videos has grown rapidly in recent years. Finding and browsing relevant information to be automatically extracted from videos is not an easy task, but today it is an indispensable feature due to the immense number of digital products available. In this paper, we present a system which provides a process to automatically extract information from videos. We describe a system solution that uses a re-trained OpenNLP model to locate all the places and famous people included in a specific video. The system obtains information from the Google Knowledge Graph related to relevant named entities such as places or famous people. In this paper we will also present the Automatic Georeferencing Video (AGV) system developed by RAI (Radiotelevisione italiana, which is the national public broadcasting company of Italy, owned by the Ministry of Economy and Finance) Teche for the European Project "La Città Educante" (The Educating City: teaching and learning processes in cross-media ecosystem) Our system contributes to The Educating City project by providing the technological environment to create statistical models for automatic named entity recognition (NER), and has been implemented in the field of education, in Italian initially. The system has been applied to the learning challenges facing the world of educational media and has demonstrated how beneficial combining topical news content with scientific content can be in education. [ABSTRACT FROM AUTHOR]

Many natural language tasks related to geographic information retrieval (GIR) require toponym recognition, and identifying Chinese toponyms from social media messages to share real-time information is a critical problem for many practical applications, such as natural disaster response and geolocating. In this article, we focused on toponym recognition from social media messages in Chinese. While existing off-the-shelf Chinese named entity recognition (NER) tools could be applied to identify toponyms, these approaches cannot address a variety of language irregularities taken from social media messages, including location name abbreviations, informal sentence structures and combination toponyms. We present a deep neural network named BERT-BiLSTM-CRF, which extends a basic bidirectional recurrent neural network model (BiLSTM) with the pretraining bidirectional encoder representation from transformers (BERT) representation to handle the toponym recognition task in Chinese text. Using three datasets taken from lists of alternative location names, the experimental results showed that the proposed model can significantly outperform previous Chinese NER models/algorithms and a set of state-of-the-art deep learning models. [ABSTRACT FROM AUTHOR]

References to geographic locations are common in text data sources including social media and web pages. They take different forms from simple place names to relative expressions that describe location through a spatial relationship to a reference object (e.g. the house beside the Waikato River). Often complex, multi-word phrases are employed (e.g. the road and railway cross at right angles; the road in line with the canal) where spatial relationships are communicated with various parts of speech including prepositions, verbs, adverbs and adjectives. We address the problem of automatically detecting relative geospatial location descriptions, which we define as those that include spatial relation terms referencing geographic objects, and distinguishing them from non-geographical descriptions of location (e.g. the book on the table). We experiment with several methods for automated classification of text expressions, using features for machine learning that include bag of words that detect distinctive words, word embeddings that encode meanings of words and manually identified language patterns that characterise geospatial expressions. Using three data sets created for this study, we find that ensemble and meta-classifier approaches, that variously combine predictions from several other classifiers with data features, provide the best F-measure of 0.90 for detecting geospatial expressions. [ABSTRACT FROM AUTHOR]

We present a new dataset for the task of toponym resolution in digitized historical newspapers in English. It consists of 343 annotated articles from newspapers based in four different locations in England (Manchester, Ashton-under-Lyne, Poole and Dorchester), published between 1780 and 1870. The articles have been manually annotated with mentions of places, which are linked—whenever possible—to their corresponding entry on Wikipedia. The dataset consists of 3,364 annotated toponyms, of which 2,784 have been provided with a link to Wikipedia. The dataset is published in the British Library shared research repository, and is especially of interest to researchers working on improving semantic access to historical newspaper content.

We present a new dataset for the task of toponym resolution in digitized historical newspapers in English. It consists of 343 annotated articles from newspapers based in four different locations in England (Manchester, Ashton-under-Lyne, Poole and Dorchester), published between 1780 and 1870. The articles have been manually annotated with mentions of places, which are linked—whenever possible—to their corresponding entry on Wikipedia. The dataset consists of 3,364 annotated toponyms, of which 2,784 have been provided with a link to Wikipedia. The dataset is published in the British Library shared research repository, and is especially of interest to researchers working on improving semantic access to historical newspaper content. [ABSTRACT FROM AUTHOR]

Several tasks related to geographical information retrieval and to the geographical information sciences involve toponym matching, that is, the problem of matching place names that share a common referent. In this article, we present the results of a wide-ranging evaluation on the performance of different string similarity metrics over the toponym matching task. We also report on experiments involving the usage of supervised machine learning for combining multiple similarity metrics, which has the natural advantage of avoiding the manual tuning of similarity thresholds. Experiments with a very large dataset show that the performance differences for the individual similarity metrics are relatively small, and that carefully tuning the similarity threshold is important for achieving good results. The methods based on supervised machine learning, particularly when considering ensembles of decision trees, can achieve good results on this task, significantly outperforming the individual similarity metrics.

In recent years, researches related to Geographic Information Retrieval Systems as a specific field of Information Retrieval has continued to attract the attention of the research community by holding several assessing forums. However, these forums provide sets of tests comprised of text documents and queries that are ready to evaluate non‐interactive systems. This framework reduces the possibilities of carrying out a more thorough evaluation of these systems because it is not considering several important features such as the diversity provided by different information sources or the human‐computer interaction. The aim of this paper is to describe a new approach to evaluate interactive Geographic Information Retrieval Systems, which main novelty is to consider the user’s knowledge generated by the human‐computer interaction as well as the spatial information provided by different data sources. The proposed method will require generating a set of tests from three main data sources (Geonames, Wikipedia, and OpenStreetMap), as well as a set of queries that will consist of a tuple of three components: the object type, the spatial relationship and the geographic object. As a result, the proposed evaluation approach integrates the two most commonly used strategies to evaluate IR systems, which are focused on the system and the end user, by applying several user satisfaction techniques and usability tests. As a main conclusion, we pointed out that the evaluation process of Geographic Information Retrieval systems should consider the user’s knowledge generated by the human-computer interaction as well as the spatial information provided by different and heterogeneous data sources.

Many solutions for coarse geolocating of users at the time they post a message exist. However, for many important applications, like traffic monitoring and event detection, finer geolocation at the level of city neighborhoods, i.e., at a sub-city level, is needed. Data-driven approaches often do not guarantee good accuracy and efficiency due to the higher number of sub-city level positions to be estimated and the low availability of balanced and large training sets. We claim that external information sources overcome limitations of data-driven approaches in achieving good accuracy for sub-city level geolocation and we present a knowledge-driven approach achieving good results once the reference area of a message is known. Our algorithm, called Sherloc, exploits toponyms in the message, extracts their semantic from a geographic gazetteer, and embeds them into a metric space that captures the semantic distance among them. We identify the semantically closest toponyms to a message and then cluster them with respect to their spatial locations. Sherloc requires no prior training, it can infer the location at sub-city level with high accuracy, and it is not limited to geolocating on a fixed spatial grid. [ABSTRACT FROM AUTHOR]

Geographic text analysis (GTA) research in the digital humanities has focused on projects analyzing modern English-language corpora. These projects depend on temporally specific lexicons and gazetteers that enable place name identification and georesolution. Scholars working on the early modern period (1400–1800) lack temporally appropriate geoparsers and gazetteers and have been reliant on general purpose linked open data services like Geonames. These anachronistic resources introduce significant information retrieval and ethical challenges for early modernists. Using the geography entries of the canonical eighteenth-century Encyclopédie, we evaluate rule-based named entity recognition (NER) systems to pinpoint areas where they would benefit from adjustments for processing historical corpora. As we demonstrate, annotating nested and extended place information is one way to improve early modern GTA. Working with Enlightenment sources also motivates a critique of the landscape of digital geospatial data. [ABSTRACT FROM AUTHOR]

People often communicate with reference to informally agreed places, such as "the city centre". However, views of the spatial extent of such areas may vary, resulting in imprecise regions. We compare perceptions of Sheffield's City Centre from a street survey to extents derived from various web-based sources. Such automated approaches have advantages of speed, cost and repeatability. We show that footprints from web sources are often in concordance with models derived from more labour-intensive methods. Notable exceptions however were found with sources advertising or selling residential property. Agreement between sources was measured by aggregating them to identify locations of consensus. [ABSTRACT FROM AUTHOR]

Geoparser is a fundamental component of a Geographic Information Retrieval (GIR) geoparser, which performs toponym recognition, disambiguation, and geographic coordinate resolution from unstructured text domain. However, geoparsing of news articles which report several events across many place-mentions in the document are not yet adequately handled by regular geoparser, where the scope of resolution is either toponym-level or document-level. The capacity to detect multiple events and geolocate their true coordinates along with their numerical arguments is still missing from modern geoparsers, much less in Indonesian news corpora domain. We propose an event geoparser model with three stages of processing, which tightly integrates event extraction model into geoparsing and provides precise event-level resolution scope. The model casts the geotagging and event extraction as sequence labeling and uses LSTM-CRF inferencer equipped with features derived using Aggregated Topic Model from a large corpus to increase the generalizability. Throughout the proposed workflow and features, the geoparser is able to significantly improve the identification of pseudo-location entities, resulting in a 23.43% increase for weighted F1 score compared to baseline gazetteer and POS Tag features. As a side effect of event extraction, various numerical arguments are also extracted, and the output is easily projected to a rich choropleth map from a single news document.

Geographic information retrieval (GIR) is concerned with returning information in response to an information need, typically expressed in terms of a thematic and spatial component linked by a spatial relationship. However, evaluation initiatives have often failed to show significant differences between simple text baselines and more complex spatially enabled GIR approaches. We explore the effectiveness of three systems (a text baseline, spatial query expansion, and a full GIR system utilizing both text and spatial indexes) at retrieving documents from a corpus describing mountaineering expeditions, centred around fine grained toponyms. To allow evaluation, we use user generated content (UGC) in the form of metadata associated with individual articles to build a test collection of queries and judgments. The test collection allowed us to demonstrate that a GIR-based method significantly outperformed a text baseline for all but very specific queries associated with very small query radii. We argue that such approaches to test collection development have much to offer in the evaluation of GIR.

Abstract Today most of the users on the move require contextualized local and georeferenced information. Several solutions aim to meet these trends, thus assisting users and satisfying their needs and preferences, such as virtual assistants and Location-Aware Recommender Systems (LARS), both in commercial and research literature. However, general purpose virtual assistants usually have to manage large domains, dealing with big amounts of data and online resources, losingfocus on more specific requirements and local information. On the other hand, traditional recommender systems are based on filtering techniques and contextual knowledge, and they usually do not rely on Natural Language Processing (NLP) features on users' queries, which are useful to understand and contextualize users' necessities on the spot. Therefore, comprehending the actual users' information needs and other key information that can be included in the user query, such as geographical references, is a challenging task which is not yet fully accomplished by current state-of-the-art solutions. In this paper, we propose Paval (Location-Aware Virtual Personal Assistant 2 2 The name Paval is chosen as a permutation of the initials of "Location-aware virtual personal assistant".), a semantic assisting engine for suggesting local points of interest (POIs) and services by analyzing users' natural language queries, in order to estimate the information need and potential geographic references expressed by the users. The system exploits NLP and semantic techniques providing as output recommendations on local geolocated POIs and services which best match the users' requests, retrieved by querying our semantic Km4City Knowledge Base. The proposed system is validated against the most popular virtual assistants, such as Google Assistant, Apple Siri and Microsoft Cortana, focusing the assessment on the request of geolocated POIs and services, showing very promising capabilities in successfully estimating the users' information needs and multiple geographic references. [ABSTRACT FROM AUTHOR]

In this paper a system providing an efficient integration between Content-Based Image Retrieval (CBIR) and Geographic Information Retrieval (GIR) is presented. Over the years, many CBIR systems have been proposed to give a solution for an efficient use of multimedia/visual contents and other issues as performance, quality of retrieval, data heterogeneity, and multimodal information integration. The aim of the proposed approach is to prove that the use of geographic data can improve the results obtained by an image matching system based only on visual data. Our framework is composed of three parts, each of them described in detail in this paper: the first part is dedicated to CBIR, with an experimental comparison of a large number of different multimedia features to choose the one to use in the system implementation; in the second part the methodology to integrate geographic and multimedia data is showed; in the last part is presented a GIR system implementation using a “points of interest” search. An Android application has been developed for the client-side using Apache Solr as server side provider for the information retrieval functionalities. An experimental evaluation is carried out to demonstrate the effective improvement given by the combination of geographic and multimedia data. Our results have been obtained using a real dataset composed of artworks located in Naples’s museums. [ABSTRACT FROM AUTHOR]

Several tasks related to geographical information retrieval and to the geographical information sciences involve toponym matching, that is, the problem of matching place names that share a common referent. In this article, we present the results of a wide-ranging evaluation on the performance of different string similarity metrics over the toponym matching task. We also report on experiments involving the usage of supervised machine learning for combining multiple similarity metrics, which has the natural advantage of avoiding the manual tuning of similarity thresholds. Experiments with a very large dataset show that the performance differences for the individual similarity metrics are relatively small, and that carefully tuning the similarity threshold is important for achieving good results. The methods based on supervised machine learning, particularly when considering ensembles of decision trees, can achieve good results on this task, significantly outperforming the individual similarity metrics. [ABSTRACT FROM AUTHOR]

How do people perceive landscapes? What elements of the landscape do they identify as characteristic of a landscape? And how can we arrive at descriptions, and ultimately representations that better reflect people’s notions of landscapes? In this study, we collected landscape descriptions from five landscape types at 10 study sites in Switzerland. For each site, we collected data from three sources: free lists with participants, hiking blogs, and Flickr tags. Free lists were obtained through on-site interviews with visitors, hiking blogs were gathered by focused crawling of web content, and Flickr tags were selected based on spatial footprints obtained from the hiking blogs. We quantitatively compared landscape descriptions between data sources and landscape types using cosine similarity. We found that descriptions from the same data source were significantly more similar, irrespective of landscape type. Descriptions from the same landscape type were more similar, but only within the same data source. Through a qualitative analysis of different aspects of landscape in our content, we found that each data source offered a different distribution of landscape aspects. For example, while Flickr tags contained high proportions of toponyms, they contained little content relating to sense of place. In contrast, hiking blogs contained more information about sense of place. Our approach combining these varied textual sources thus offers a more holistic view on landscapes. This study constitutes a step toward extracting semantically rich descriptions of landscapes from a variety of sources and using this information to distinguish different landscapes, with potential applications for landscape monitoring and management. [ABSTRACT FROM AUTHOR]

Data-driven GIScience shows a growing interest in making spatial information from large text data. In this paper, we quantify and thus evaluate the relation between text frequency and properties of the outer-text, geographic setting by comparing text frequencies of mountain names to the respective geomorphometric characteristics. We focus on some 2000 unique mountain names that appear some 50,000 times in a large compilation of texts on Swiss alpine history. The results on the full data set suggest only a weak relation: only 5-10% of the variation in the text frequency being explained by the respective geomorphometric characteristics. However, an analysis of multiple scales allows us to identify a Simpson’s Paradox. What appears to be ‘noise’ in the analysis of all mountains in the whole of Switzerland shows significant local signals. Small spatial extents, found all over Switzerland, can show considerably strong correlations between text frequency and spatial prominence, with up to 90% of the total variation explained. We argue that our findings have practical implications for data-driven GIScience. Retrieving meaningful spatial information from text might only be possible if the spatial scale of analysis reflects the spatial scale described in the input text documents. [ABSTRACT FROM AUTHOR]

Most of the available digital information is associated with locations or places on the Earth. Although queries submitted to search engines often contain geographic information, the traditional information retrieval methods based on keywords are not effective，as they neglect the spatial relationship in the queries. Geographic information retrieval outputs results by spatial semantic match between queries and documents set，so it becomes a research hotspot in information retrieval and geographic information science. A framework of geographic information retrieval system is proposed, and the key techniques such as geographic knowledge base, geographic information extraction, geographic information retrieval model, hybrid index structure, and visualization of information retrieval are classified and surveyed according to this framework. Based on the comprehensive comparison and analysis of existed researches, some suggestions are presented for future research, and a lot of references are provided. [ABSTRACT FROM AUTHOR]

Neighbourhoods have been described as‘the building blocks of public services society’. Their subjective nature, however, and the resulting difficulties in collecting data, means that in many countries there are no officially defined neighbourhoods either in terms of names or boundaries. This has implications not only for policy but also business and social decisions as a whole. With the absence of neighbourhood boundaries many studies resort to using standard administrative units as proxies. Such administrative geographies, however, often have a poor fit with those perceived by residents. Our approach detects these important social boundaries byautomaticallymining the Web en masse for passively declared neighbourhood data within postal addresses. Focusing on the United Kingdom (UK), this research demonstrates the feasibility of automated extraction of urban neighbourhood names and their subsequent mapping as vague entities. Importantly, and unlike previous work, our process does not require any neighbourhood names to be established a priori. [ABSTRACT FROM PUBLISHER]

This paper investigates recent research on active learning for (geo) text and image classification, with an emphasis on methods that combine visual analytics and/or deep learning. Deep learning has attracted substantial attention across many domains of science and practice, because it can find intricate patterns in big data; but successful application of the methods requires a big set of labeled data. Active learning, which has the potential to address the data labeling challenge, has already had success in geospatial applications such as trajectory classification from movement data and (geo) text and image classification. This review is intended to be particularly relevant for extension of these methods to GISience, to support work in domains such as geographic information retrieval from text and image repositories, interpretation of spatial language, and related geo-semantics challenges. Specifically, to provide a structure for leveraging recent advances, we group the relevant work into five categories: active learning, visual analytics, active learning with visual analytics, active deep learning, plus GIScience and Remote Sensing (RS) using active learning and active deep learning. Each category is exemplified by recent influential work. Based on this framing and our systematic review of key research, we then discuss some of the main challenges of integrating active learning with visual analytics and deep learning, and point out research opportunities from technical and application perspectives--for application-based opportunities, with emphasis on those that address big data with geospatial components. [ABSTRACT FROM AUTHOR]

Toponym matching, i.e. pairing strings that represent the same real-world location, is a fundamental problemfor several practical applications. The current state-of-the-art relies on string similarity metrics, either specifically developed for matching place names or integrated within methods that combine multiple metrics. However, these methods all rely on common sub-strings in order to establish similarity, and they do not effectively capture the character replacements involved in toponym changes due to transliterations or to changes in language and culture over time. In this article, we present a novel matching approach, leveraging a deep neural network to classify pairs of toponyms as either matching or nonmatching. The proposed network architecture uses recurrent nodes to build representations from the sequences of bytes that correspond to the strings that are to be matched. These representations are then combined and passed to feed-forward nodes, finally leading to a classification decision. We present the results of a wide-ranging evaluation on the performance of the proposed method, using a large dataset collected from the GeoNames gazetteer. These results show that the proposed method can significantly outperform individual similarity metrics from previous studies, as well as previous methods based on supervised machine learning for combining multiple metrics. [ABSTRACT FROM AUTHOR]

Ground-truth datasets are essential for the training and evaluation of any automated algorithm. As such, gold-standard annotated corpora underlie most advances in natural language processing (NLP). However, only a few relatively small (geo-)annotated datasets are available for geoparsing, i.e., the automatic recognition and geolocation of place references in unstructured text. The creation of geoparsing corpora that include both the recognition of place names in text and matching of those names to toponyms in a geographic gazetteer (a process we call geo-annotation), is a laborious, time-consuming and expensive task. The field lacks efficient geo-annotation tools to support corpus building and lacks design guidelines for the development of such tools. Here, we present the iterative design of GeoAnnotator, a web-based, semi-automatic and collaborative visual analytics platform for geo-annotation. GeoAnnotator facilitates collaborative, multi-annotator creation of large corpora of geo-annotated text by generating computationally-generated pre-annotations that can be improved by human-annotator users. The resulting corpora can be used in improving and benchmarking geoparsing algorithms as well as various other spatial language-related methods. Further, the iterative design process and the resulting design decisions can be used in annotation platforms tailored for other application domains of NLP.

Urban model retrieval has wide applications in the geoscience field, and it is also a very challenging research topic due to the blur and background clutter in query images and the large spatial inconsistencies between query and database images. In this study, a feature extraction and similarity metric-learning framework for urban model retrieval is proposed. In the method, the selective search voting algorithm is presented to automatically localize and segment a query object from an input image with the help of the top-ranked retrieved database images. Then, the local features of object images are extracted via sparse coding, and the global features are learned using the spatial constrained convolutional neural network. We utilize a new similarity metric to match the database images with a query object image. Finally, similar 3D models are retrieved. Both qualitative and quantitative experimental results indicate that the proposed framework can localize and segment a query object from an input image precisely and that the retrieval results are better than those of other related approaches. [ABSTRACT FROM AUTHOR]

This article focuses on integrating computational and visual methods in a system that supports analysts to identify, extract, map, and relate linguistic accounts of movement. We address two objectives: (1) build the conceptual, theoretical, and empirical framework needed to represent and interpret human-generated directions; and (2) design and implement a geovisual analytics workspace for direction document analysis. We have built a set of geo-enabled, computational methods to identify documents containing movement statements, and a visual analytics environment that uses natural language processing methods iteratively with geographic database support to extract, interpret, and map geographic movement references in context. Additionally, analysts can provide feedback to improve computational results. To demonstrate the value of this integrative approach, we have realized a proof-of-concept implementation focusing on identifying and processing documents that contain human-generated route directions. Using our visual analytic interface, an analyst can explore the results, provide feedback to improve those results, pose queries against a database of route directions, and interactively represent the route on a map.

Similarity measures have a long tradition in fields such as information retrieval, artificial intelligence, and cognitive science. Within the last years, these measures have been extended and reused to measure semantic similarity; i.e., for comparing meanings rather than syntactic differences. Various measures for spatial applications have been developed, but a solid foundation for answering what they measure; how they are best applied in information retrieval; which role contextual information plays; and how similarity values or rankings should be interpreted is still missing. It is therefore difficult to decide which measure should be used for a particular application or to compare results from different similarity theories. Based on a review of existing similarity measures, we introduce a framework to specify the semantics of similarity. We discuss similarity-based information retrieval paradigms as well as their implementation in web-based user interfaces for geographic information retrieval to demonstrate the applicability of the framework. Finally, we formulate open challenges for similarity research.

Crowdsourced data (CSD) generated by citizens is becoming more popular as its potential utilization in many applications increases due to its currency and availability. However, the quality of CSD, including its relevance, is often questioned as the data is not generated by professionals nor follows standard data-collection procedures. The quality of CSD can be assessed according to a range of characteristics including its relevance. In this paper, information relevance has been explored through using geographic information retrieval (GIR) techniques to identify the most highly relevant information from a set of crowdsourced data. This research tested a relevance assessment approach for CSD by adapting relevance assessment techniques available in the GIR domain. Thematic and geographic relevance were assessed by analyzing the frequency of selected terms which appeared in CSD reports using natural language processing techniques. The study analyzed crowdsourced reports from the 2011 Australian flood’s Crowdmap to examine a proof of concept on relevance assessment using a subset of this dataset based on a defined set of queries. The results determined that the thematic and geographic specificities of the queries were 0.44 and 0.67, respectively, which indicated the queries used were more geographically specific than thematically specific. The Spearman’s rho value of 0.62 indicated that the final ranked relevance lists showed reasonable agreement with a manually classified list and confirmed the potential of the approach for CSD relevance assessment. In particular, this research has contributed to the field of CSD relevance assessment through an integrated thematic and geographic relevance ranking process by using a user-query specificity approach to improve the final ranking.

In Geographical Information Systems, geo-coding is used for the task of mapping from implicitly geo-referenced data to explicitly geo-referenced coordinates. At present, an enormous amount of implicitly geo-referenced information is hidden in unstructured text, e.g., Wikipedia, social data and news. Toponym recognition is the foundation of mining this useful geo-referenced information by identifying words as toponyms in text. In this paper, we propose an adapted toponym recognition approach based on deep belief network (DBN) by exploring two key issues: word representation and model interpretation. A Skip-Gram model is used in the word representation process to represent words with contextual information that are ignored by current word representation models. We then determine the core hyper-parameters of the DBN model by illustrating the relationship between the performance and the hyper-parameters, e.g., vector dimensionality, DBN structures and probability thresholds. The experiments evaluate the performance of the Skip-Gram model implemented by the Word2Vec open-source tool, determine stable hyper-parameters and compare our approach with a conditional random field (CRF) based approach. The experimental results show that the DBN model outperforms the CRF model with smaller corpus. When the corpus size is large enough, their statistical metrics become approaching. However, their recognition results express differences and complementarity on different kinds of toponyms. More importantly, combining their results can directly improve the performance of toponym recognition relative to their individual performances. It seems that the scale of the corpus has an obvious effect on the performance of toponym recognition. Generally, there is no adequate tagged corpus on specific toponym recognition tasks, especially in the era of Big Data. In conclusion, we believe that the DBN-based approach is a promising and powerful method to extract geo-referenced information from text in the future.

A large number of geographical toponyms exist in web pages and other documents, providing abundant geographical resources for GIS. It is very common for toponyms to co-occur in the same documents. To investigate these relations associated with geographic entities, a novel complex network model for co-occurrence toponyms is proposed. Then, 12 toponym co-occurrence networks are constructed from the toponym sets extracted from the People’s Daily Paper documents of 2010. It is found that two toponyms have a high co-occurrence probability if they are at the same administrative level or if they possess a part-whole relationship. By applying complex network analysis methods to toponym co-occurrence networks, we find the following characteristics. (1) The navigation vertices of the co-occurrence networks can be found by degree centrality analysis. (2) The networks express strong cluster characteristics, and it takes only several steps to reach one vertex from another one, implying that the networks are small-world graphs. (3) The degree distribution satisfies the power law with an exponent of 1.7, so the networks are free-scale. (4) The networks are disassortative and have similar assortative modes, with assortative exponents of approximately 0.18 and assortative indexes less than 0. (5) The frequency of toponym co-occurrence is weakly negatively correlated with geographic distance, but more strongly negatively correlated with administrative hierarchical distance. Considering the toponym frequencies and co-occurrence relationships, a novel method based on link analysis is presented to extract the core toponyms from web pages. This method is suitable and effective for geographical information retrieval. [ABSTRACT FROM AUTHOR]

Historic expeditions are events that are flavored by exploratory, scientific, military or geographic characteristics. Such events are often documented in literature, journey notes or personal diaries. A typical historic expedition involves multiple site visits and their descriptions contain spatiotemporal and attributive contexts. Expeditions involve movements in space that can be represented by triplet features (location, time and description). However, such features are implicit and innate parts of textual documents. Extracting the geospatial information from these documents requires understanding the contextualized entities in the text. To this end, we developed a semi-automated framework that has multiple Information Retrieval and Natural Language Processing components to extract the spatiotemporal information from a two-volume historic expedition gazetteer. Our framework has three basic components, namely, the Text Preprocessor, the Gazetteer Processing Machine and the JAPE (Java Annotation Pattern Engine) Transducer. We used the Brazilian Ornithological Gazetteer as an experimental dataset and extracted the spatial and temporal entities from entries that refer to three expeditioners' site visits (which took place between 1910 and 1926) and mapped the trajectory of each expedition using the extracted information. Finally, one of the mapped trajectories was manually compared with a historical reference map of that expedition to assess the reliability of our framework. [ABSTRACT FROM AUTHOR]

Recognizing references to places in texts is needed in many applications, such as search engines, location-based social media and document classification. In this paper we present a survey of methods and techniques for the recognition and identification of places referenced in texts. We discuss concepts and terminology, and propose a classification of the solutions given in the literature. We introduce a definition of the Geographic Scope Resolution (GSR) problem, dividing it in three steps: geoparsing, reference resolution, and grounding references. Solutions to the first two steps are organized according to the method used, and solutions to the third step are organized according to the type of output produced. We found that it is difficult to compare existing solutions directly to one another, because they often create their own benchmarking data, targeted to their own problem. [ABSTRACT FROM AUTHOR]

Describing current, past and future landscapes for inventory and policy making purposes requires classifications capturing variation in, for example, land use and land cover. Typical land cover classifications for such purposes result from a top-down process and rely on expert conceptualisations, and thus provide limited space for incorporating more widely held views of key landscape elements. In this paper we introduce the notion of spatial folksonomies, which we define as a tuple linking a vocabulary of landscape terms through authors and resources to locations. We demonstrate how spatial folksonomies can automatically be created for Switzerland using two text corpora: the Swiss Alpine Club's yearbook for the past 150 years and user generated content from a website describing a wide range of outdoor activities. The spatial folksonomies capture variation in space of the use of nouns describing 96 natural landscape terms (e.g. ridge, forest, mountain, etc.) and allow us to characterise regions and compute similarities. We compare our spatial folksonomies to two traditional land cover/land use classifications (CORINE and Arealstatistik) and demonstrate that despite their very different sources, the approaches capture landscape variation in broadly similar ways. However, our spatial folksonomies provide new insights into how landscapes are described, through for example variation in space, time and through the prism of different activities. We argue that our spatial folksonomies are a novel way of capturing variation closer to the bottom-up understandings of landscape for instance required to describe cultural ecosystem services. [ABSTRACT FROM AUTHOR]

In last years, many organizations have been given the task of collecting geographic information, which can be used in different contexts for different purposes. Consequently, locating this information becomes a priority for users who need to access it. In this paper a recovery system that manipulates spatial relations between geographic objects is presented. This proposal introduces of novel way the spatial relations to determinate semantic similarity between concepts to solve geospatial queries combining textual and spatial aspects. Another important feature of this system is to present the results in a friendly way, allowing navigate the mapping provided by the system. The indexed information can be easily accessed by users and displayed in a clear and organized to support decision-making way. This tool can be used for both characterization and geospatial location of the soil, as for the management of agricultural soils in Cuba. [ABSTRACT FROM AUTHOR]

In the recent big data era, massive spatial related data are continuously generated and scrambled from various sources. Acquiring accurate geographic information is also urgently demanded. How to accurately retrieve desired geographic information has become the prominent issue, needing to be resolved in high priority. The key technologies in geographic information retrieval are modeling document footprints and ranking documents based on their similarity evaluation. The traditional spatial similarity evaluation methods are mainly performed using a MBR (Minimum Bounding Rectangle) footprint model. However, due to its nature of simplification and roughness, the results of traditional methods tend to be isotropic and space-redundant. In this paper, a new model that constructs the footprints in the form of point-sets is presented. The point-set-based footprint coincides the nature of place names in web pages, so it is redundancy-free, consistent, accurate, and anisotropic to describe the spatial extents of documents, and can handle multi-scale geographic information. The corresponding spatial ranking method is also presented based on the point-set-based model. The new similarity evaluation algorithm of this method firstly measures multiple distances for the spatial proximity across different scales, and then combines the frequency of place names to improve the accuracy and precision. The experimental results show that the proposed method outperforms the traditional methods with higher accuracies under different searching scenarios. [ABSTRACT FROM AUTHOR]

With the popularity of mobile devices and smartphones, we have witnessed rapid growth in mobile applications and services, especially in location-based services (LBS). According to a mobile marketing survey, maps/location searches are among the most utilized services on smartphones. Points of interest (POIs), such as stores, shops, gas stations, parking lots, and bus stops, are particularly important for maps/location searches. Existing map services such as Google Maps and Wikimapia are constructed manually either professionally or with crowd sourcing. However, manual annotation is costly and limited in current POI search services. With the abundance of information on the Web, many store POIs can be extracted from the Web. In this paper, we focus on automatically constructing a POI database to enable store POI map searches. We propose techniques that are required to construct a POI database, including focused crawling, information extraction, and information retrieval techniques. We first crawl Yellow Page web sites to obtain vocabularies of store names. These vocabularies are then investigated with search engines to obtain sentences containing these store names from search snippets in order to train a store name recognition model. To extract POIs scattered across the Web, we propose a query-based crawler to find address-bearing pages that might be used to extract addresses and store names. We crawled 1.25 million distinct POI pairs scattered across the Web and implemented a POI search service via Apache Lucent’s search platform, called Solr. The experimental results demonstrate that the proposed geographical information retrieval model outperforms Wikimapia and a commercial app called ‘What’s the Number?’ [ABSTRACT FROM PUBLISHER]

Nowadays, the World Wide Web is growing at increasing rate and speed, and consequently the online available resources populating Internet represent a large source of knowledge for various business and research interests. For instance, over the past years, increasing attention has been focused on retrieving information related to geographical location of places and entities, which is largely contained in web pages and documents. However, such resources are represented in a wide variety of generally unstructured formats, and this actually does not help final users to find desired information items. The automatic annotation and comprehension of toponyms, location names and addresses (at different resolution and granularity levels) can deliver significant benefits for the whole web community by improving search engines filtering capabilities and intelligent data mining systems. The present paper addresses the problem of gathering geographical information from unstructured text in web pages and documents. In the specific, the proposed method aims at extracting geographical location (at street number resolution) of commercial companies and services, by annotating geo-related information from their web domains. The annotation process is based on Natural Language Processing (NLP) techniques for text comprehension, and relies on Pattern Matching and Hierarchical Cluster Analysis for recognizing and disambiguating geographical entities. Geotagging performances have been assessed by evaluating Precision, Recall and F-Measure of the proposed system output (represented in form of semantic RDF triples) against both a geo-annotated reference database and a semantic Smart City repository. [ABSTRACT FROM AUTHOR]

Approximate string matching (ASM) is a challenging problem, which aims to match different string expressions representing the same object. In this paper, detailed experimental studies were conducted on the subject of toponym matching, which is a new domain where ASM can be performed, and the creation of a single string-matching measure that can perform toponym matching process regardless of the language was attempted. For this purpose, an ASM measure called DAS, which comprises name similarity, word similarity and sentence similarity phases, was created. Considering the experimental results, the retrieval performance and system accuracy of DAS were much better than those of other well-known five measures that were compared on toponym test datasets. In addition, DAS had the best metric values of mean average precision in six languages, and precision/recall graphs confirm this result. [ABSTRACT FROM AUTHOR]

Nowadays, spatial and temporal data play an important role in social networks. These data are distributed and dispersed in several heterogeneous data sources. These peculiarities make that geographic information retrieval being a non-trivial task, considering that the spatial data are often unstructured and built by different collaborative communities from social networks. The problem arises when user queries are performed with different levels of semantic granularity. This fact is very typical in social communities, where users have different levels of expertise. In this paper, a novelty approach based on three matching-query layers driven by ontologies on the heterogeneous data sources is presented. A technique of query contextualization is proposed for addressing to available heterogeneous data sources including social networks. It consists of contextualizing a query in which whether a data source does not contain a relevant result, other sources either provide an answer or in the best case, each one adds a relevant answer to the set of results. This approach is a collaborative learning system based on experience level of users in different domains. The retrieval process is achieved from three domains: temporal, geographical and social, which are involved in the user-content context. The work is oriented towards defining a GIScience collaborative learning for geographic information retrieval, using social networks, web and geodatabases. [ABSTRACT FROM AUTHOR]

In this article, a novel observation-to-generalization place model is proposed. It is shown how this model can be used to formally define the problem of finding geographically similar places. The observation-to-generalization model differentiates between observations of phenomena in the environment at a specific location and time, and generalizations about places that are inferred from these observations. A suite of operations is defined to find similar places based on the invariance of generalized place properties, and it is demonstrated how these functions can be applied to the problem of finding similar places based on the topics that people write about in place descriptions. One use for similar-place search is for exploratory research that will enable investigators to perform case-control studies on place data. [ABSTRACT FROM AUTHOR]