GIS Processing for Geocoding Described Collection Locations

Much useful data is currently not available for use in contemporary geographic information systems because location is provided as descriptive text and not in a recognized coordinate system format. This is particularly true for datasets with significant temporal depth such as museum collections. Development is just beginning on applications that automate the conversion of descriptive text based locations to geographic coordinate values. These applications are a type of geocoding or locator service and require functionality in two domains: natural language processing and geometric calculation. Natural language processing identifies the spatial semantics of the text describing a location and tags the individual text elements according to their spatially descriptive role. This is referred to as geoparsing. Once identified, these tagged text elements can be either converted directly to numeric values or used as pointers to geometric objects that represent geographic features identified in the description. These values and geometries can be employed in a series of functions to determine coordinates for the described location. This is referred to as geoprocessing. Selection of appropriate text elements from a location description and ancillary data as input is critical for successful geocoding. The traverse, one of many types of location description is selected for geocoding development. Specific text elements with spatial meaning are identified and incorporated into an XML format for use as geoprocessing input. Information associated with the location is added to the XML format to maintain database relations and geoprocessing error checking functionality. ESRI’s ArcGIS 8.3 is used as a development environment where geoprocessing functionality is tested for XML elements using ArcObjects and VBA forms

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)

An automated approach for geocoding tabular itineraries

Historical itineraries, often accessible as lists or tables describing places visited in sequence, are abundant resources and also important objects of study for humanities scholars. This article advances a novel method for automatically geocoding tabular itineraries, combining approximate string matching with a cost optimization algorithm based on dynamic programming. Experiments with a dataset of historical itineraries, with ground-truth geocoding annotations provided by domain experts and leveraging also the GeoNames gazetteer, attest to the effectiveness of the proposed method. The obtained results show that while approximate string matching can already achieve very low median errors, with many toponyms matching exactly against GeoNames entries, the combination with cost optimization can significantly improve results in terms of the average distance towards the correct disambiguations

Railroads and the Making of Modern America -- Tools for Spatio-Temporal Correlation, Analysis, and Visualization

This project aims to integrate large-scale data sources from the Digging into Data repositories with other types of relevant data on the railroad system, already assembled by the project directors. Our project seeks to develop useful tools for spatio-temporal visualization of these data and the relationships among them. Our interdisciplinary team includes computer science, history, and geography researchers. Because the railroad "system" and its spatio-temporal configuration appeared differently from locality-to-locality and region-to-region, we need to adjust how we "locate" and "see" the system. By applying data mining and pattern recognition techniques, software systems can be created that dynamically redefine the way spatial data are represented. Utilizing processes common to analysis in Computer Science, we propose to develop a software framework that allows these embedded concepts to be visualized and further studied

Doctor of Philosophy

dissertationWildfire is a common hazard in the western U.S. that can cause significant loss of life and property. When a fire approaches a community and becomes a threat to the residents, emergency managers need to take into account both fire behavior and the expected response of the threatened population to warnings before they issue protective action recommendations to the residents at risk. In wildfire evacuation practices, incident commanders use prominent geographic features (e.g., rivers, roads, and ridgelines) as trigger points, such that when a fire crosses a feature, the selected protective action recommendation will be issued to the residents at risk. This dissertation examines the dynamics of evacuation timing by coupling wildfire spread modeling, trigger modeling, reverse geocoding, and traffic simulation to model wildfire evacuation as a coupled human-environmental system. This dissertation is composed of three manuscripts. In the first manuscript, wildfire simulation and household-level trigger modeling are coupled to stage evacuation warnings. This work presents a bottom-up approach to constructing evacuation warning zones and is characterized by fine-grain, data-driven spatial modeling. The results in this work will help improve our understanding and representation of the spatiotemporal dynamics in wildfire evacuation timing and warnings. The second manuscript integrates trigger modeling and reverse geocoding to extract and select prominent geographic features along the boundary of a trigger buffer. A case study using a global gazetteer GeoNames demonstrates the potential value of the proposed method in facilitating communications in real-world evacuation practice. This work also sheds light on using reverse geocoding in other environmental modeling applications. The third manuscript explores the spatiotemporal dynamics behind evacuation timing by coupling fire and traffic simulation models. The proposed method sets wildfire evacuation triggers based on the estimated evacuation times using agent-based traffic simulation and could be potentially used in evacuation planning. In summary, this dissertation enriches existing trigger modeling approaches by coupling fire simulation, reverse geocoding, and traffic simulation. A framework for modeling wildfire evacuation as a coupled human-environmental system using triggers is proposed. Moreover, this dissertation also attempts to advocate and promote open science in wildfire evacuation modeling by using open data and software tools in different phases of modeling and simulation

Mapping Heritage: Geospatial Online Databases of Historic Roads. The Case of the N-340 Roadway Corridor on the Spanish Mediterranean

The study has developed an online geospatial database for assessing the complexity of roadway heritage, overcoming the limitations of traditional heritage catalogues and databases: the itemization of heritage assets and the rigidity of the database structure. Reflecting the current openness in the field of heritage studies, the research proposes an interdisciplinary approach that reframes heritage databases, both conceptually and technologically. Territorial scale is key for heritage interpretation, the complex characteristics of each type of heritage, and social appropriation. The system is based on an open-source content-management system and framework called ProcessWire, allowing flexibility in the definition of data fields and serving as an internal working tool for research collaboration. Accessibility, flexibility, and ease of use do not preclude rigor: the database works in conjunction with a GIS (Geographic Information System) support system and is complemented by a bibliographical archive. A hierarchical multiscalar heritage characterization has been implemented in order to include the different territorial scales and to facilitate the creation of itineraries. Having attained the main goals of conceptual heritage coherence, accessibility, and rigor, the database should strive for broader capacity to integrate GIS information and stimulate public participation, a step toward controlled crowdsourcing and collaborative heritage characterization.Consejería de Obras Públicas y Transportes, Junta de Andalucí

Advanced Location-Based Technologies and Services

Since the publication of the first edition in 2004, advances in mobile devices, positioning sensors, WiFi fingerprinting, and wireless communications, among others, have paved the way for developing new and advanced location-based services (LBSs). This second edition provides up-to-date information on LBSs, including WiFi fingerprinting, mobile computing, geospatial clouds, geospatial data mining, location privacy, and location-based social networking. It also includes new chapters on application areas such as LBSs for public health, indoor navigation, and advertising. In addition, the chapter on remote sensing has been revised to address advancements