Les séries temporelles d'images satellitaires sont une source d'information importante pour le suivi des changements spatio-temporels des surfaces terrestres. En outre, le nombre d’images est en augmentation constante. Pour les exploiter pleinement, des outils dédiés au traitement automatique du contenu informationnel sont développés. Néanmoins ces techniques ne satisfont pas complètement les géographes qui exploitent pourtant, de plus en plus couramment, les données extraites des images dans leurs études afin de prédire le futur. Nous proposons dans cette thèse, une méthodologie générique à base d’un modèle de Markov caché pour l’analyse et la prédiction des changements sur une séquence d’images satellitaires. Cette méthodologie présente deux modules : un module de traitement intégrant les descripteurs et les algorithmes classiquement utilisés en interprétation d'images, et un module d’apprentissage basé sur les modèles de Markov cachés. La performance de notre approche est évaluée par des essais d’interprétations des évènements spatio-temporels effectués sur plusieurs sites d’études. Les résultats obtenus permettront d’analyser et de prédire les changements issus des différentes séries temporelles d’images SPOT et LANDSAT pour l’observation des évènements spatio-temporels telle que l'expansion urbaine et la déforestation. / The time series of satellite images are an important source of information for monitoring spatiotemporal changes of land surfaces. Furthermore, the number of satellite images is increasing constantly, for taking full advantage, tools dedicated to the automatic processing of information content is developed. However these techniques do not completely satisfy the geographers who exploit more currently, the data extracted from the images in their studies to predict the future. In this research we propose a generic methodology based on a hidden Markov model for analyzing and predicting changes in a sequence of satellite images. The methodology that is proposed presents two modules : a processing module which incorporating descriptors and algorithms conventionally used in image interpretation and a learning module based on hidden Markov models. The performance of the approach is evaluated by trials of interpretation of spatiotemporal events conducted in several study sites. Results obtained allow us to analyze and to predict changes from various time series of SPOT and LANDSAT images for observation of spatiotemporal events such as urban development and deforestation.

The assumption that prehistoric Native American land use practices had little impact on the North American landscape persists in the literature. However, recent research suggests the effects of prehistoric burning, deforestation and agriculture may potentially have been greater than previously considered. To resolve this discrepancy, quantitative estimates of changes in human population size and forest structure and composition over the course of the Holocene are needed. This thesis addresses this need by providing radiocarbon-based paleodemographic reconstructions and pollen-inferred estimates of vegetation change, as well as analyses of associations between the two at both continental and regional scales, from the late Pleistocene to the Anthropocene. One way to estimate paleodemographic change is to use the number of radiocarbon (14C) dates from a given area to study patterns of human occupation through time. A review of the literature and compilation of existing databases relevant to this method showed there is now sufficient data to study the paleodemographic history of many regions around the world. An analysis of 14C datasets from North America and Australia compared well with model-based reconstructions of past demographic growth, and provided higher frequency fluctuations in population densities that will be important for future research. Using a kernel density estimation approach, the first estimates of prehistoric population density for North America were obtained and synthesized into a series of continental-scale maps showing the distribution and frequency of 14C dates in the Canadian Archaeological Radiocarbon Database (CARD). The maps illustrated the space-time evolution of population and migration patterns, which were corroborated by independent sources of evidence. A methodology based on the statistical evaluation of cross-correlations between population and plant abundance was then developed to analyze the associations between these population estimates and plant communities derived from pollen databases. Periods of high spatial cross-correlation (positive and negative) between population and plant abundance were irregular and did not improve over time, suggesting that ancient human impacts are not discernable at a continental scale, either due to low populations or varying human land use practices. To further examine the relationship between pollen data and human land use at a regional scale, estimates of plant density and landscape openness are needed. The REVEALS (Regional Estimates of VEgetation Abundance from Large Sites) model corrects for the non-linear relationship between pollen production and plant abundance and can therefore be used to map histories of land use and land cover change. The model was applied to pollen records from lake sediments in the deciduous forest of southeastern Quebec. A preliminary analysis comparing these results to population density revealed low population during times of high Populus abundance and high population following the appearance of the mixed temperate forest suggesting a discernable human-environment association at regional scales. Overall, the results of thesis support the growing body of literature that suggests prehistoric Native Americans impacted their environments and that these impacts can be detected and quantified by integrating archaeological and paleoecological information. However, the timing, location, and intensity of human land use has changed in both space and time, suggesting regional- to local-scale analyses of human-environment interactions are most appropriate for continental North America. The methodology presented here can be used to study additional North American regions for the purpose of developing a continental history of human-environment interaction.

Focos geográficos bem definidos de pênfigo prevalecem no mundo todo, inclusive no Brasil. Nas últimas décadas vem sendo estudada a possibilidade de fatores ambientais participarem do desencadeamento da doença. A região nordeste do Estado de São Paulo, onde localizam-se três Bacias Hidrográficas, apresenta prevalência de duas formas clínicas do pênfigo, pênfigo vulgar (PV) e pênfigo foliáceo endêmico (PFE) sendo uma importante área para o estudo da doença. Nesse estudo, foi utilizado um Sistema de Informação Geográfica (SIG) para descrever a distribuição espacial e o comportamento temporal de PV e PFE nessa região do Estado de São Paulo nas últimas cinco décadas e caracterizar o uso e ocupação do solo no município com maior número de ocorrências de pênfigo. Os pacientes foram identificados baseados nos prontuários médicos entre 1965 e 2014. Os mapas temáticos foram desenvolvidos com o software ArcGIS 10.2. Para representar a distribuição espacial do pênfigo, os mapas foram organizados em décadas de 1965 a 2014. Para o município com maior número de ocorrências de PV e PFE, o uso e ocupação do solo, de acordo com a hidrografia, a vegetação nativa, à área agrícola, o solo exposto e a área urbana, foi analisado em um raio de 2 km no entorno da residência dos pacientes no momento do surgimento dos sintomas. Como análise adicional, mapas ilustrando a distribuição dos casos de pênfigo de acordo com as classes hipsométricas, declividade do solo e densidade populacional por distrito do município (Norte, Sul, Leste, Oeste e Central) foram também desenvolvidos. Quatrocentos e vinte e seis casos foram analisados. Os casos de PFE predominaram, com 285 (67%) dos casos. De acordo com a distribuição espacial e evolução temporal, PV não foi reportado de 1965 a 1974, entretanto, os casos de PV tiveram um aumento contínuo nas próximas décadas e ultrapassou o número de casos de PFE na última década. Analisando de forma acumulada os casos, tanto o PV quanto o PFE tiveram aumento ao longo do período estudado, revelando uma expansão espacial. A Bacia Hidrográfica do Rio Pardo teve o maior número de casos com um total de 153 (41% PV e 59% PFE). No período de 1965 a 2014 o número de cidades com registros de casos de PV e PFE aumentou de 0 para 49 e de 13 para 60, respectivamente, com Ribeirão Preto e Batatais sendo os principais focos geográficos de PV e PFE, respectivamente. Ribeirão Preto foi o município com maior ocorrência de pênfigo (35 casos de PV e 37 casos de PFE). A área agrícola (42%) e o solo exposto (33,2%) foram os usos do solo que predominam no município. Além disso, todos os pacientes com PV ou PFE moram perto de rios e área agrícola. Em Ribeirão Preto, os casos de pênfigo estão concentrados nos distritos norte e oeste, os casos de PFE estão distribuídos em baixas altitudes quando comparadas com o PV e tanto o PV quanto o PFE predominam em áreas com baixa porcentagem de declividade do solo. No contexto da saúde pública, o SIG se tornou uma importante ferramenta que ajuda os pesquisadores entenderem a ocorrência e tendência de certos eventos, conduzindo nas melhores estratégias de controle de doenças. As análises de distribuição espacial e evolução temporal mostraram que os casos de PV e PFE aumentaram na região nordeste do Estado de São Paulo nas últimas cinco décadas. Esse monitoramento também ajudou a identificar os principais focos geográficos de pênfigo nessa região. A predominância de agricultura e solo exposto em Ribeirão Preto e a proximidade dos casos com rios e agricultura reforça a hipótese de que os fatores ambientais desempenham um importante papel na etiopatogênese do pênfigo / Defined foci of pemphigus prevalence worldwide, including Brazil, raise the possibility that environmental factors trigger the onset of this disease. The northeastern region of the state of São Paulo is located within three Watersheds and is an appropriate site to investigate pemphigus because this disease is prevalent in both clinical forms--endemic pemphigus foliaceus (PFE) and pemphigus vulgaris (PV)--therein. In this study, we have used Geographic Information Systems (GIS) to describe the spatial distribution and temporal behavior of PV and PFE in this region of the state of São Paulo over the last five decades; we have also characterized land use in the city with the highest number of cases. Patients were identified based on patients\' medical records between 1965 and 2014. Thematic maps were developed with the ArcGIS 10.2 software. To represent the spatial distribution of pemphigus, maps were organized in decades from 1965 to 2014. For the city with the highest occurrence of PFE and PV cases, land use regarding hydrography, native vegetation, agriculture, exposed soil, and urbanization was analyzed within a 2-km buffer surrounding from the patients\' residencies considering the address where the pemphigus clinical signs and symptoms started. For additional analysis, thematic maps illustrating the distribution of pemphigus cases according to hypsometric classes, soil declivity, and population density by sector (North, South, East, West, and Central) were designed. Four hundred and twenty-six cases were analyzed. PFE cases predominated: they corresponded to 285 or 67% of the cases. Regarding spatial distribution and temporal evolution, PV was not reported from 1965 to 1974; notwithstanding, PV rose in the following four decades and overcame the number of PFE cases in the last decade. Regarding cumulative cases, both PV and PFE increased throughout the studied period, which revealed spatial expansion. The Pardo River Basin had the highest number of cases with a total of 153 (41% PV and 59% PFE). In the studied period, the number of cities with recorded cases of PV and PFE increased from 0 to 49 and from 13 to 60, respectively, with Ribeirão Preto and Batatais being the main geographical foci of PV and PFE, respectively. Ribeirão Preto was the city with the highest occurrence of pemphigus--35 PV cases and 37 PFE cases. Agricultural area (42%) and exposed soil (33.2%) were the land uses that predominated in the city. In addition, all patients with PV and PFE lived close to rivers and agricultural areas. In Ribeirão Preto, pemphigus cases were concentrated in the northern and western sectors; PFE cases were distributed at lower altitudes as compared to PV; and both PV and PFE predominated in areas with lower percentage of declivity. In the context of public health, GIS has become a powerful tool that helps researchers to understand the occurrence and trend of some events, leading to improved interventional strategies and disease control. The spatial distribution and temporal evolution analyses showed PV and PFE increased in the northeastern region of the state of São Paulo over the last five decades. This monitoring also helped to identify the main geographical foci of pemphigus. The predominance of agriculture and exposed soil in Ribeirão Preto and the proximity of the cases to rivers and agriculture reinforced the hypothesis that environmental factors play a role in pemphigus etiopathogenesis

本研究提出一個兩階段的時空模型來分析空氣污染與健康的關係。我們選取在台灣的49個有設置空氣品質監測站的鄉鎮市區做為研究地區。資料包含這些小地區中1997-2001年的各地區每日因呼吸道疾病而就醫的門診人數與空氣污染物濃度與氣象監測資料。在第一階段中，對每一個月所有地區的每日因呼吸道疾病而就醫的門診人數與空氣污染配適時空模型，並利用氣象條件等因素做調整。在第二階段裡，利用線性混合效果模型將第一階段所獲得的60 個月空氣污染物係數估計值來獲得代表這五年全國整體污染物係數的估計。本文利用模擬研究來探討當季節因素與不可解釋的因素，例如像流行性感冒等存在時會對文獻上其他時空模型中參數的估計所造成的影響，同時與我們所提出的方法作一比較。 / We proposed a spatiotemporal model to investigate the association between the acute health effects and daily numbers of clinic visits for respiratory illness. The data include clinic records due to respiratory illness and environmental variables from air quality monitoring stations in Taiwan during 1997-2001. A small-area design and two-phase modeling were used for the analysis. In the first phase, we constructed a Poisson regression with autogressive residual process and spatial correlation to obtain the pollution coefficient of each single month. In the second phase, we combined the information from phase one model to improve estimates of the pollution coefficients of each month and to obtain an overall pollution coefficient across the temporal course. Simulation study was used to illustrate the bias of estimation when there are seasonal, spatial and the unexplained effects in the data.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-08-04T13:59:37Z No. of bitstreams: 1 MAXWELL GUIMARÃES DE OLIVEIRA - DISSERTAÇÃO PPGCC 2012..pdf: 28277196 bytes, checksum: 398cd7b385ee61c414d0086810fbeeed (MD5) / Made available in DSpace on 2018-08-04T13:59:37Z (GMT). No. of bitstreams: 1 MAXWELL GUIMARÃES DE OLIVEIRA - DISSERTAÇÃO PPGCC 2012..pdf: 28277196 bytes, checksum: 398cd7b385ee61c414d0086810fbeeed (MD5) Previous issue date: 2012-08-20 / Capes / Atualmente, há um volume considerável de dados espaço-temporais disponíveis em vários meios, sobretudo na Internet. A visualização de dados espaço-temporais é uma tarefa complexa, que requer uma série de recursos visuais apropriados para que, em conjunto, possam permitir aos usuários uma correta interpretação das informações analisadas. Além do emprego de técnicas de visualização, a utilização de técnicas de descoberta de conhecimento em bancos de dados tem se mostrado relevante no auxílio à análise exploratória de relacionamentos em dados espaço-temporais. O levantamento do estado da arte em visualização de dados espaço-temporais leva à conclusão de que a área ainda é deficiente em soluções para visualização e análise desses tipos. Muitas abordagens abrangem somente questões espaciais, desprezando as características temporais desses dados. Inserido nesse contexto, o principal objetivo deste trabalho é melhorar a experiência do usuário em visualização e análise espaço-temporal, indo além do universo da visualização dos dados espaço-temporais brutos e considerando, também, a importância em visualização de dados espaço-temporais derivados de um processo de descoberta de conhecimento, mais especificamente algoritmos de clustering. Esse objetivo é atingido com a definição de uma abordagem inovadora em visualização de dados espaço-temporais, e de sua implementação, denominada GeoSTAT (Geographic SpatioTemporal Analysis Tool), que engloba pontos importantes observados nas principais abordagens existentes e acrescenta, principalmente, técnicas de visualização voltadas à dimensão temporal e à utilização de algoritmos de clustering, valorizando características até então pouco exploradas em dados espaço-temporais. A validação deste trabalho ocorre por meio de dois estudos de caso, onde cada um aborda dados espaço-temporais de um domínio específico, para demonstrar a experiência do usuário final diante das técnicas de visualização reunidas na abordagem proposta. / Nowadays, there is a considerable amount of spatiotemporal data available in various media, especially on the Internet. The visualization of spatiotemporal data is a complex task that requires a series of visual suitable resources which can enable users to have a correct interpretation of the data. Apart from the use of visualization techniques, the use of techniques of knowledge discovery in databases has proven relevantfor the exploratory analysis of relationships in spatiotemporal data. The state of the art in visualization of spatiotemporal data leads to the conclusion that the area is still deficient in solutions for viewing and analysis of those data. Many approaches cover only spatial issues, ignoring the temporal characteristics of such data. Inserted in this context, the main objective of this work is to improve the user experience in spatiotemporal visualization and analysis, going beyond the universe of visualization of spatiotemporal raw data and also considering the importance of visualization of spatiotemporal data derived from a knowledge discovery process, more specifically clustering algorithms. This goal is achieved by defining an innovative approach for the analysis and visualization of spatiotemporal data, and its implementation, called GeoSTAT (Spatiotemporal Geographic Analysis Tool), which includes importam points in the main existing approaches and adds especially visualization techniques geared to the temporal dimension and the use of clustering algorithms, enhancing unexplored features in spatiotemporal data. The validation of this work occurs through two case studies, where each one deals with spatiotemporal data of a specific domain to demonstrate the end-user experience on the visualization techniques combined in the proposed approach.

No description available.

Tesis por compendio / [ES] Los servicios ecosistémicos de montaña han adquirido importancia entre los científicos, los administradores y los encargados de formular políticas de todo el mundo; sin embargo, las actividades humanas están amenazando su conservación, en particular los cambios en el uso del suelo debido al aumento de la urbanización, la expansión agrícola y la deforestación. La Puna Altoandina es un ecosistema montañoso representativo que enfrenta estos serios y crecientes desafíos. La Puna Altoandina, cuyos principales socioecosistemas consisten en pastizales naturales, matorrales y zonas agrícolas, puede proporcionar múltiples servicios ecosistémicos influenciados por el tipo de cobertura terrestre y sus dinámicas. En este contexto, se han explorado las dinámicas entre los usos representativos de la superficie terrestre y su potencial para proporcionar servicios ecosistémicos en la Puna húmeda Altoandina a lo largo del tiempo. Asimismo, se ha completado un análisis espacio-temporal que describe cómo diferentes patrones de 6 dinámicas de cambio del uso del suelo impactan en la provisión de 7 servicios ecosistémicos durante un período de 13 años (de 2000 a 2013), y en el territorio de 25 provincias. Además, con el fin de mejorar la gestión de los servicios ecosistémicos, abordamos los efectos de aplicar dos análisis "clúster" (estáticos y dinámicos) para evaluar los conjuntos de servicios ecosistémicos en cuatro escalas de observación diferentes (dos ámbitos administrativos y dos tamaños de pixel geográfico: 0.25 y 9 km2). En general, este estudio proporciona un enfoque para facilitar la incorporación de los servicios ecosistémicos a múltiples escalas que permite una interpretación fácil del desarrollo de la región y que puede contribuir a mejorar las acciones para la gestión del uso del suelo y las decisiones de política ambiental. / [CA] Els serveis ecosistèmics muntanya han adquirit importància entre els científics, els administradors i els encarregats de formular polítiques de tot el món; no obstant això, les activitats humanes estan amenaçant la seua conservació, en particular els canvis en l'ús del sòl a causa de l'augment de la urbanització, l'expansió agrícola i la desforestació. La Puna Altoandina és un ecosistema muntanyenc representatiu que enfronta aquests seriosos i creixents desafiaments. La Puna Altoandina que els seus principals soci-ecosistemes consisteixen en pasturatges naturals, matolls i zones agrícoles, pot proporcionar múltiples serveis ecosistèmics influenciats per les diferents categories de cobertura terrestre y els seus dinàmiques. En aquest context, s'han explorat les dinàmiques entre els usos representatius de la superfície terrestre i el seu potencial per a proporcionar serveis ecosistèmics en la Puna humida Altoandina al llarg del temps. Així mateix, s'ha completat una anàlisi espai-temporal que descriu com diferents patrons de 6 dinàmiques de canvi de l'ús del sòl impacten en la provisió de 7 serveis ecosistèmics durant un període de 13 anys (de 2000 a 2013), i en el territori de 25 províncies. A més, amb la finalitat de millorar la gestió dels serveis ecosistèmics, abordem els efectes d'aplicar dues anàlisis "clúster" (estàtics i dinàmics) per a avaluar els conjunts de serveis ecosistèmics en quatre escales d'observació diferents (dos àmbits administratius i dues grandàries de píxel geogràfic: 0.25 y 9 km2). En general, aquest estudi proporciona un enfocament per a facilitar la incorporació dels serveis ecosistèmics a múltiples escales que permet una interpretació fàcil del desenvolupament de la regió i que pot contribuir a millorar les accions per la gestió de l'ús del sòl i les decisions de política ambiental. / [EN] Mountain ecosystem services have gained relevance among scientists, managers, and policy-makers worldwide; but, human activities are threatening its conservation, particularly land changes due to increased urbanization, agricultural expansion and deforestation. The high-Andean Puna is a representative mountain ecosystem that is facing these serious and growing challenges. The high-Andean Puna, whose main socialecosystems consist of natural grassland, shrubland and agricultural areas, can provide multiple regulating ecosystem services influenced by the land cover/use type and their dynamics. In this context, we explored the dynamics between the representative land-cover classes and its potential to provide ecosystem services in the high-Andean moist Puna over time. We completed a spatiotemporal analysis that describes how different patterns of 6 landchange dynamics impact on the supply of 7 ecosystem services over a period of 13 years (from 2000 to 2013), and across 25 provinces. Moreover, in order to improve the management of ecosystem services, we addressed the effects of applying two cluster analyses (static and dynamic) for assessing bundles of ecosystem services across four different scales of observation (two administrative boundaries and two sizes of grids: 0.25 and 9 km2). Overall, this study provides an approach to facilitate the incorporation of ES at multiple scales allowing an easy interpretation of the region development that can contribute to land management actions and policy decisions. / Madrigal Martínez, S. (2021). Land-change dynamics and ecosystem services using expert-based assessment and GIS: methodological implications for improving decision-making [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/172369 / Compendio

<p>Technological advances have a profound impact on people and the world in which they live. People use a wide range of smart devices, such as the Internet of Things (IoT), smartphones, and wearable devices, on a regular basis, all of which store and use location data. With this explosion of technology, these devices have been playing an essential role in digital forensics and crime investigations. Digital forensic professionals have become more able to acquire and assess various types of data and locations; therefore, location data has become essential for responders, practitioners, and digital investigators dealing with digital forensic cases that rely heavily on digital devices that collect data about their users. It is very beneficial and critical when performing any digital/cyber forensic investigation to consider answering the six Ws questions (i.e., who, what, when, where, why, and how) by using location data recovered from digital devices, such as where the suspect was at the time of the crime or the deviant act. Therefore, they could convict a suspect or help prove their innocence. However, many digital forensic standards, guidelines, tools, and even the National Institute of Standards and Technology (NIST) Cyber Security Personnel Framework (NICE) lack full coverage of what location data can be, how to use such data effectively, and how to perform spatial analysis. Although current digital forensic frameworks recognize the importance of location data, only a limited number of data sources (e.g., GPS) are considered sources of location in these digital forensic frameworks. Moreover, most digital forensic frameworks and tools have yet to introduce geo-contextualization techniques and spatial analysis into the digital forensic process, which may aid digital forensic investigations and provide more information for decision-making. As a result, significant gaps in the digital forensics community are still influenced by a lack of understanding of how to properly curate geodata. Therefore, this research was conducted to develop a transdisciplinary framework to deal with the limitations of previous work and explore opportunities to deal with geodata recovered from digital evidence by improving the way of maintaining geodata and getting the best value from them using an iPhone case study. The findings of this study demonstrated the potential value of geodata in digital disciplinary investigations when using the created transdisciplinary framework. Moreover, the findings discuss the implications for digital spatial analytical techniques and multi-intelligence domains, including location intelligence and open-source intelligence, that aid investigators and generate an exceptional understanding of device users' spatial, temporal, and spatial-temporal patterns.</p>

Indiana University-Purdue University Indianapolis (IUPUI) / Over the past two decades, northern parts of the United States have experienced extreme heat conditions. Some of the notable heat wave impacts have occurred in Chicago in 1995 with over 600 reported deaths and in Philadelphia in 1993 with over 180 reported deaths. The distribution of extreme heat events in Indianapolis has varied since the year 2000. The Urban Heat Island effect has caused the temperatures to rise unusually high during the summer months. Although the number of reported deaths in Indianapolis is smaller when compared to Chicago and Philadelphia, the heat wave in the year 2010 affected primarily the vulnerable population comprised of the elderly and the lower socio-economic groups. Studying the spatial distribution of high temperatures in the vulnerable areas helps determine not only the extent of the heat affected areas, but also to devise strategies and methods to plan, mitigate, and tackle extreme heat. In addition, examining spatial patterns of vulnerability can aid in development of a heat warning system to alert the populations at risk during extreme heat events. This study focuses on the qualitative and quantitative methods used to measure extreme heat events. Land surface temperatures obtained from the Landsat TM images provide useful means by which the spatial distribution of temperatures can be studied in relation to the temporal changes and socioeconomic vulnerability. The percentile method used, helps to determine the vulnerable areas and their extents. The maximum temperatures measured using LST conversion of the original digital number values of the Landsat TM images is reliable in terms of identifying the heat-affected regions.