Felix Mata

Geographic information retrieval by topological, geographical, and conceptual matching

Geographic Information Science community is recognized that modern Geographic Information Retrieval systems should support the processing of imprecise data distributed over heterogeneous repositories. This means the search for relevant geographic results for a geographic query (QG) even if the data sources do not contain a result that matches exactly the users request and then approximated results would be useful. Therefore, GIR systems should be centred at the nature and essence of spatial data (their relations and properties) taken into consideration the users profile. Usually, semantic features are implicitly presented in different data sources. In this work, we use three heterogeneous data sources: vector data, geographic ontology, and geographic dictionaries. These repositories usually store topological relations, concepts, and descriptions of geographical objects under certain scenarios. In contrast to previous work, where these layers have been treated in an isolated way, their integration expects to be a better solution to capture the semantics of spatial objects. Thus, the use of spatial semantics and the integration of different information layers improve GIR, because adequate retrieval parameters according to the nature of spatial data, which emulate the users requirements, can be established. In particular, we use topological relations {inside, in}, semantic relations {hyperonimy, meronimy}, and descriptions {constraints, representation}. An information extraction mechanism is designed for each data source, while the integration process is performed using the algorithm of ontology exploration. The ranking process is based on similarity measures, using the previously developed confusion theory. Finally, we present a case study to show some results of integrated GIR (iGIR) and compare them with Googles ones in a tabular form.

An experimental virtual museum based on augmented reality and navigation

Indoor environments offer many possibilities for the development of navigation-aided systems and location-based services. This paper introduces an experimental setup that combines navigation facilities with augmented reality, and which is applied to two museums in the city of Mexico. The approach is based on a semantic model of a museum environment that reflects its organization and spatial structure. The experimental setup combines augmented reality, digital compass devices with smartphones. While several constraints reduce interaction capabilities among exhibitions and visitors, augmented reality offers the possibility of relaxing these constraints by combining real sceneries with digital representations. This enhances interactions between users and objects of interest pointed by the users, where additional multimedia information on the collections presented is available on demand.

GeoST: geographic, thematic and temporal information retrieval from heterogeneous web data sources

Geographical data commonly available on the Web is often related to thematic and temporal data, and represented in heterogeneous data sources. This makes geographical information retrieval tasks non straightforward processes as the information available is often unstructured. The research presented in this paper introduces an ontology-driven approach whose objective is to facilitate retrieval of geographical information on the Web. Spatio- temporal queries are categorized in order to solve questions related to several complementary modeling dimensions: when, what and where. The retrieval strategy is based on the exploration of a spatio-temporal ontology, and implemented by a search engine. The whole approach is exemplified by a prototype development, and the results obtained are evaluated by a metrics based on recall and precision measures. The experimental findings show that the strategy is effective for the types of spatio-temporal queries used by our geographical retrieval approach.

A mobile navigation and orientation system for blind users in a metrobus environment

The research presented in this paper introduces a mobile assistant to spatially locate and orient passengers of a Metrobus system in the city of Mexico. The system assists blind passengers or passengers with limited eyesight. While most of the navigation systems developed so far for blind people employ a complex conjunction of positioning systems, video cameras, location-based and image processing algorithms, we developed an affordable and low-cost combination of mobile phone, GPS and compass device to provide the same range of functionalities. The mobile application is developed on top of a 3rd generation cell phone extended by GPS and digital compass devices. Interaction among these devices is achieved by using Bluetooth communications. Audible user-oriented interfaces indicate to a given user her/his location within the Metrobus system, i.e., appropriate instructions of orientation that lead to the boarding gates in that station, thanks to an orientation algorithm developed for this aim. The experiments have been performed on a Nokia N73 cell phone in a Metrobus line of the city of Mexico and validated by a panel of blind users.

iRank: Integral Ranking of Geographical Information by Semantic, Geographic, and Topological Matching

Previous geographic information retrieval (GIR) works have used different criteria of a geographical nature to rank the documents retrieved from heterogeneous repositories. The most common approaches consider the characteristics and relationships process the documents in a separate way (only using their geometric or topologic aspects). In addition, they do not take into account the nature of geographic data (spatial semantics) in the weighting and ranking process which limits the assessment of document relevance. Nevertheless, the ranking can be improved by using approaches in tegrating the essence and nature of geographical space, i.e., (1) geographical attributes, (2) topological relationships, and (3) spatialsemantics that are focused on conceptually describing a geographic object. This paper outlines iRank, a method that integrates these three aspects to rank a document. iRank evaluates documents using three sources of information: GeoOntologies, dictionaries, and topology files. The approach consists of three stages which define the geographical relevance between a query and a document. In the first stage, the relevance is computed by using concepts (GeoOntologies), the second stage uses geographic attributes (dictionaries), and in the last stage, the relevance is processed by considering spatial relation-ships (vector files). Thus, the major iRank advantage is integral ranking. The results received by the authors show a better ranking with these criteria than ones that use them separately.

A cross-domain framework for designing healthcare mobile applications mining social networks to generate recommendations of training and nutrition planning

Abstract Nowadays, people are practicing physical exercise in order to maintain good health conditions. Such physical workouts are required by a plan, which should be designed and supervised by sport specialists and medical assistants. Thus, the exercise sessions shall start with consultation of a coach, doctor and dietician; however, many times this scenario is not presented. In typical activities such as running, cycling and fitness, people use health mobile apps with their smartphones, which offer support for these activities. Nevertheless, the functionality and operation of these applications are isolated, because many and long questionnaires are performed. Additionally, the physical and health state of a user is not considered. These issues would be taken into account for determining recommendations about the time for doing exercise and the kind of activity for each person. In this work, a social semantic mobile framework to generate recommendations where a mobile application allows sensing the physical performance, taking into consideration medical criteria with smartphones is proposed. The approach includes a semantic cross-information that comes from social network and official data as well as sport activities and medical knowledge. This knowledge is translated into application ontologies related directly to health, nutrition and training domains. The methodology also covers physical fitness tests and a monitoring tool for evaluating the nutrition plan and the correct execution of the training. As case study, the mobile application offers to evaluate the physical and health conditions of a runner, automatically generate a nutrition plan and training, monitor plans and recomputed them if users make changes in their routines. The data provided from the social network are used as feedback in the application, in order to make the training and nutrition plans more flexible by applying spatio-temporal analysis based on machine learning. Finally, the generated training and nutrition plans were validated by specialists, they have demonstrated 82% of effectiveness rate in exercise training routines and 86% in nutrition plans. In addition, the results were compared with isolated approaches and manual recommendations made by specialists, the obtained overall performance was 81%.

A Mobile Information System Based on Crowd-Sensed and Official Crime Data for Finding Safe Routes: A Case Study of Mexico City

Mobile information systems agendas are increasingly becoming an essential part of human life and they play an important role in several daily activities. These have been developed for different contexts such as public facilities in smart cities, health care, traffic congestions, e-commerce, financial security, user-generated content, and crowdsourcing. In GIScience, problems related to routing systems have been deeply explored by using several techniques, but they are not focused on security or crime rates. In this paper, an approach to provide estimations defined by crime rates for generating safe routes in mobile devices is proposed. It consists of integrating crowd-sensed and official crime data with a mobile application. Thus, data are semantically processed by an ontology and classified by the Bayes algorithm. A geospatial repository was used to store tweets related to crime events of Mexico City and official reports that were geocoded for obtaining safe routes. A forecast related to crime events that can occur in a certain place with the collected information was performed. The novelty is a hybrid approach based on semantic processing to retrieve relevant data from unstructured data sources and a classifier algorithm to collect relevant crime data from official government reports with a mobile application.

Geographical Knowledge Discovery applied to the Social Perception of Pollution in the City of Mexico

Nowadays, experts and citizens at large are keen to express their opinions using social networks on many issues, this generating a new form of participatory democracy. The research presented in this paper proposes a preliminary research work that combines semantics processing and machine learning to derive geographic and semantic knowledge implicitly derived from the perceptions and opinions as expressed by social networks, digital media and institutional data. The results are mapped to the geographical structure of the city in order to study differences and commonalities at the neighborhood level. The whole approach is applied and illustrated in the context of the city of Mexico and pollution perception as a case study. The figures that emerge show evidence of a significant impact of the structure of the city over the way citizens perceive pollution.

iRank: Ranking Geographical Information by Conceptual, Geographic and Topologic Similarity

Geographic Information Ranking consists of measuring if a document (answer) is relevant to a spatial query. It is done by comparing characteristics in common between document and query. The most popular approaches compare just one aspect of geographical data (geographic properties, topology, among others). It limits the assessment of document relevance. Nevertheless, it can be improved when key characteristics of geographical objects are considered in the ranking (1) geographical attributes, (2) topological relations, and (3) geographical concepts. In this paper, we outline iRank a method that integrates these three aspects to rank a document. Ourapproach evaluates documents from three sources of information: GeoOntologies , dictionaries, and topology files. Relevance is measured according to three stages. In the first stage, the relevance is computed by processing concepts; in second stage relevance is calculated using geographic attributes. In the last stage, the relevance is measured by computing topologic relations. Thus, the main contribution of iRank is show that integration of three ranking criteria is better than when they are used in separate way.

A recommender system to generate museum itineraries applying augmented reality and social-sensor mining techniques

Nowadays, museums offer technological and digital options to enrich the user experience in a visit. However, questions arise like which exhibition/museum could I visit? How to tour it and get the best experience? These questions are not easy to answer, because they do not represent tasks straightforward. Considering that the experiences of visiting a museum are now available in social networks, in which users describe, rate, and disseminate a work of art/exhibition of a museum, this information can be mined to generate tour recommendations in museums. Such recommendations could be improved by combining and applying data mining obtained from Internet of Things sensors installed in museums. In this paper, a hybrid approach to make recommendations for museum visits is proposed. It includes an Internet of Things architecture of beacons, incorporating some technologies based on semantic analysis, data mining, and machine learning. This approach integrates and combines data sources for generating and recommending indoor and outdoor itineraries for museums, which are visualized with augmented reality. The itinerary is built, taking into consideration opinions and assessments from social networks, the semantic classification of museums, and cultural activities, as well as data measured by beacon sensors in museum exhibitions. The result is a customized tour with augmented reality that contains a set of recommendations of how to visit a set of museums and obtain a better experience of the visit. A prototype of mobile application is available in the Google Play, called the “Historic Center,” with almost 500 downloads and an acceptable evaluation.