Application of Text Summarization techniques to the Geographical Information Retrieval task

Automatic Text Summarization has been shown to be useful for Natural Language Processing tasks such as Question Answering or Text Classification and other related fields of computer science such as Information Retrieval. Since Geographical Information Retrieval can be considered as an extension of the Information Retrieval field, the generation of summaries could be integrated into these systems by acting as an intermediate stage, with the purpose of reducing the document length. In this manner, the access time for information searching will be improved, while at the same time relevant documents will be also retrieved. Therefore, in this paper we propose the generation of two types of summaries (generic and geographical) applying several compression rates in order to evaluate their effectiveness in the Geographical Information Retrieval task. The evaluation has been carried out using GeoCLEF as evaluation framework and following an Information Retrieval perspective without considering the geo-reranking phase commonly used in these systems. Although single-document summarization has not performed well in general, the slight improvements obtained for some types of the proposed summaries, particularly for those based on geographical information, made us believe that the integration of Text Summarization with Geographical Information Retrieval may be beneficial, and consequently, the experimental set-up developed in this research work serves as a basis for further investigations in this field.

Question answering and multi-search engines in geo-temporal information retrieval

In this paper we present a complete system for the treatment of both geographical and temporal dimensions in text and its application to information retrieval. This system has been evaluated in both the GeoTime task of the 8th and 9th NTCIR workshop in the years 2010 and 2011 respectively, making it possible to compare the system to contemporary approaches to the topic. In order to participate in this task we have added the temporal dimension to our GIR system. The system proposed here has a modular architecture in order to add or modify features. In the development of this system, we have followed a QA-based approach as well as multi-search engines to improve the system performance.

Una aproximación basada en corpus para la detección del foco geográfico en el texto

El foco geográfico de un documento identifica el lugar o lugares en los que se centra el contenido del texto. En este trabajo se presenta una aproximación basada en corpus para la detección del foco geográfico en el texto. Frente a otras aproximaciones que se centran en el uso de información puramente geográfica para la detección del foco, nuestra propuesta emplea toda la información textual existente en los documentos del corpus de trabajo, partiendo de la hipótesis de que la aparición de determinados personajes, eventos, fechas e incluso términos comunes, pueden resultar fundamentales para esta tarea. Para validar nuestra hipótesis, se ha realizado un estudio sobre un corpus de noticias geolocalizadas que tuvieron lugar entre los años 2008 y 2011. Esta distribución temporal nos ha permitido, además, analizar la evolución del rendimiento del clasificador y de los términos más representativos de diferentes localidades a lo largo del tiempo.

Analyzing the commercial activities of a street network by ranking their nodes: a case study in Murcia, Spain

Urban researchers and planners are often interested in understanding how economic activities are distributed in urban regions, what forces influence their special pattern and how urban structure and functions are mutually dependent. In this paper, we want to show how an algorithm for ranking the nodes in a network can be used to understand and visualize certain commercial activities of a city. The first part of the method consists of collecting real information about different types of commercial activities at each location in the urban network of the city of Murcia, Spain. Four clearly differentiated commercial activities are studied, such as restaurants and bars, shops, banks and supermarkets or department stores, but obviously we can study other. The information collected is then quantified by means of a data matrix, which is used as the basis for the implementation of a PageRank algorithm which produces a ranking of all the nodes in the network, according to their significance within it. Finally, we visualize the resulting classification using a colour scale that helps us to represent the business network.

Advances in camera trap data management tools: Towards collaborative development and integration with GIS

Camera traps have become a widely used technique for conducting biological inventories, generating a large number of database records of great interest. The main aim of this paper is to describe a new free and open source software (FOSS), developed to facilitate the management of camera-trapped data which originated from a protected Mediterranean area (SE Spain). In the last decade, some other useful alternatives have been proposed, but ours focuses especially on a collaborative undertaking and on the importance of spatial information underpinning common camera trap studies. This FOSS application, namely, “Camera Trap Manager” (CTM), has been designed to expedite the processing of pictures on the .NET platform. CTM has a very intuitive user interface, automatic extraction of some image metadata (date, time, moon phase, location, temperature, atmospheric pressure, among others), analytical (Geographical Information Systems, statistics, charts, among others), and reporting capabilities (ESRI Shapefiles, Microsoft Excel Spreadsheets, PDF reports, among others). Using this application, we have achieved a very simple management, fast analysis, and a significant reduction of costs. While we were able to classify an average of 55 pictures per hour manually, CTM has made it possible to process over 1000 photographs per hour, consequently retrieving a greater amount of data.

Delineating zones to increase geographical detail in individual response data files: An application to the Spanish 2011 Census of population

Due to confidentiality considerations, the microdata available from the 2011 Spanish Census have been codified at a provincial (NUTS 3) level except when the municipal (LAU 2) population exceeds 20,000 inhabitants (a requirement that is met by less than 5% of all municipalities). For the remainder of the municipalities within a given province, information is only provided for their classification in wide population intervals. These limitations, hampering territorially-focused socio-economic analyses, and more specifically, those related to the labour market, are observed in many other countries. This article proposes and demonstrates an automatic procedure aimed at delineating a set of areas that meet such population requirements and that may be used to re-codify the geographic reference in these cases, thereby increasing the territorial detail at which individual information is available. The method aggregates municipalities into clusters based on the optimisation of a relevant objective function subject to a number of statistical constraints, and is implemented using evolutionary computation techniques. Clusters are defined to fit outer boundaries at the level of labour market areas.