The objective of this thesis is to analyze how existing data can address Chagas disease transmission risk in South America given data availability. A literature review was conducted to determine prominent variables that models use to assist with Chagas disease mitigation efforts, followed by a Web search to collect publicly available spatial data pertaining to these variables. The data were then used to create maps of data availability and in an agent-based model to identify which variables are most associated with disease transmission risk. Data availability varied widely across South America, and model results indicate that datasets related to household size and spatial housing arrangement are most important to Chagas disease infection in urban areas. Governments can use this information to better direct their resources to collect data and control the spread of triatomine vectors and Chagas disease more effectively, and potentially identify more cost-effective strategies for vector elimination.

How patterns arise from an apparently uniform group of cells is one of the classical problems in developmental biology. The mechanism is complicated by the fact that patterning occurs on a growing medium. Therefore, changes in an organism’s size and shape affect the patterning processes. In turn, patterning itself may affect growth. This interaction between growth and patterning leads to the generation of complex shapes and structures from simpler ones. Studying such interactions requires the possibility to monitor both processes in vivo. To this end, we developed a new technique to monitor and quantify vein patterning in a growing leaf over time using the leaves of Arabidopsis thaliana as a model system. We used a transgenic line with fluorescent markers associated with the venation. Individual leaves are followed in many samples in vivo through time-lapse imaging. Custom-made software allowed us to extract the leaf surface and vein pattern from images of each leaf at each time point. Then average spatial maps from multiple samples that were generated revealed spatio-temporal gradients. Our quantitative description of wild type vein patterns during leaf development revealed that there is no constant size at which a part of tissue enclosed by vasculature will become irrigated by a new vein. Instead, it seemed that vein formation depends on the growth rate of the tissue. This is the first time that vein patterning in growing leaves was quantified. The techniques developed will later be used to explore the interaction between growth and patterning through a variety of approaches, including mutant analysis, pharmacological treatments and variation of environmental conditions.

The topic of the current Master's thesis is Business Intelligence's data presentation using maps. Through integrating BI and geographic information systems a new discipline is emerging - Location Intelligence. The main goal of this thesis is to highlight and analyse reporting possibilities of the BI tools in the framework of maps. The theoretical part of this paper is dedicated to the foundation and principles of geographic information systems and their intersection with BI, where such field as Location Intelligence is being created. In the practical part of the thesis the BI tools IBM Cognos and Oracle BI are compared. The comparison is based on the following criteria: field of implementation, visualization, map external sources and performance. The evaluating criteria are defined in the beginning of the practical part as well as the evaluation method. The methods of analysis and information collecting were used to extract and revise the knowledge from specific electronic and printed sources in Czech or English. Sources for the practical part origin from my technical knowledge of the field of BI, as well as practical experience with implementation of map sources as a feature of Business Intelligence. Statistical methods are used for evaluation of the criteria results. The practical and theoretical value of the thesis lies in creating the lucid comparison of implementation of the map sources into the selected BI tools and options for reporting or visualization of BI data over map. Apart from comparison the framework for implementation of maps into the selected BI tools is established in the above mentioned work.

of the Ph.D. Thesis Netrdová, P.: Current trends in quantitative analysis of geographical data: potentialities and limitations of spatial analysis The thesis is a contribution to the discussion about the potentialities of the quantitative approach in geography. It follows the current trends in quantitative analysis of geographical data, specifically spatial analysis, particularly from the perspective of changes in the concept and character of applied methods and their possible contribution in geographical research. Due to the research focus of the author, the entire work is focused primarily on the issue of using quantitative methods in terms of social geography. Attention is focused particularly on statistically spatial analyses, which are the most widely used techniques in social geography, with a wide range of possible applications. One of the goals of this work is to bring the current development in quantitative geography closer to the Czech academic community, and thus contribute to the increased awareness of the potentialities of the application of quantitative methods and spatial analyses in geographical research. Methodological problems in the analysis of spatial data, theoretical changes in the concept of quantitative analysis and also newly emerging quantitative methods have not so far...

Recently it is obvious that climate change has became a critical topic for human society. As climate change becomes more severe, natural disasters caused by climate change have increasingly impacted humans. Most recently, Hurricane Ida killed 43 people across four states. Hurricane Ida's damage could top $95 billion, and many meteorologists predict that climate change is making storms wetter and wider. Thus, there is an urgent need to predict how much damage the flood will cause and prepare for possible destruction. Most current flood damage estimation system did not apply social media data. The theme of this thesis was to evaluate the feasibility of using machine learning models to predict hurricane damage and the input data are social media and satellite imagery. This work involves developing Data Mining approach and a couple of different Machine Learning models that further extract the feature from the data. Satellite imagery is used to identify changes in building structures as well as landscapes, and Twitter data is used to identify damaged locations and the severity of the damage. The features of Twitter posts and satellite imagery were extracted through pre-trained GloVe, ResNet, and VGG models separately. The embedding features were then fed to MLP models for damage level estimation. The models were trained and evaluated on the data. Finally, a case study was performed on the test dataset for hints on improving the models. / Master of Science / Natural disasters affect Millions of people's lives each year and it is becoming even more severe because of global warming. To make rescue more efficient when the roads and bridges are cut, social media and satellite imagery are effective data sources to help estimating flood damage. With the growth of social media, it is obvious that the post and information from people on the Internet are powerful. Also, with image processing technology improves, the information extracted from satellite images is crucial. In this work we have developed a data mining approach along with different combinations of pre-trained models using neural networks, satellite imagery and archived data from Twitter to estimate flood damage. The data mining approach leverages keywords to identify the event in the history posts in the Twitter, more specifically, we attain the geo-location, time, language information from Twitter, also using pre-event and post-event images which satellite took to generate vectors and thus effectively acquire very useful embedding features. With vectored information from Twitter and satellite imagery, we use pre-trained models and generate damage level prediction. The final results suggest that the proposed approach has potential to create more accurate prediction by using multiple data as input. Furthermore, the estimate result by using only satellite images even outperformed the result using Twitter information, which is an unexpected result comparing to previous studies.

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With the growing spread of spatial data, exploratory analysis has gained a considerable amount of attention. Particularly in the fields of Information Retrieval and Data Mining, the integration of data points helps uncover interesting patterns not always visible to the naked eye. Social networks often link entities that share places and activities; marketing tools target users based on behavior and preferences; and medical technology combines symptoms to categorize diseases. Many of the current approaches in this field of research depend on semantic analysis, which is good for inferencing and decision making. From a functional point of view, objects can be investigated from a spatial and temporal perspectives. The former attempts to verify how proximity makes the objects related; the latter adds a measure of coherence by enforcing time ordering. This type of spatio-temporal reasoning examines several aspects of semantic analysis and their characteristics: shared relationships among objects, matches versus mismatches of values, distances among parents and children, and bruteforce comparison of attributes. Most of these approaches suffer from the pitfalls of disparate data, often missing true relationships, failing to deal with inexact vocabularies, ignoring missing values, and poorly handling multiple attributes. In addition, the vast majority does not consider the spatio-temporal aspects of the data. This research studies semantic techniques of data analysis in spatial contexts. The proposed solutions represent different methods on how to relate spatial entities or sequences of entities. They are able to identify relationships that are not explicitly written down. Major contributions of this research include (1) a framework that computes a numerical entity similarity, denoted a semantic footprint, composed of spatial, dimensional, and ontological facets; (2) a semantic approach that translates categorical data into a numerical score, which permits ranking and ordering; (3) an extensive study of GML as a representative spatial structure of how semantic analysis methods are influenced by its approaches to storage, querying, and parsing; (4) a method to find spatial regions of high entity density based on a clustering coefficient; (5) a ranking strategy based on connectivity strength which differentiates important relationships from less relevant ones; (6) a distance measure between entity sequences that quantifies the most related streams of information; (7) three distance-based measures (one probabilistic, one based on spatial influence, and one that is spatiological) that quantifies the interactions among entities and events; (8) a spatio-temporal method to compute the coherence of a data sequence. / Ph. D.

Address data within geographic information systems (GIS) is used as reference data to link personal and administrative information, thus making it possible to locate and deliver goods and services to eligible persons. Preferably, every country must develop and maintain a single national address database (NAD) to eliminate data redundancy and provide a common point of reference across the board. In South Africa, the challenge is that there are separate address databases, which are developed and maintained by various public and private organizations – with little or no cooperation on data sharing. Currently, the establishment of a Committee for Spatial Information (CSI) which is tasked with the implementation of the South African Spatial Data Infrastructure (SASDI) and the publication of the South African Address Standard (SANS 1883) offer organizations an opportunity to collaborate towards the creation of a single address dataset. This research posits that the implementation of a successful data sharing initiative depends on the understanding of motivators and barriers of organizations participating in it. The research applied the case study method – with a semi-structured questionnaire – to assess the issues that motivate or obstruct GIS data sharing among three address organizations in South Africa. The results identified significant motivators that underlie the data sharing activities, e.g. reduced cost of data collection, improved data quality; and equally identified significant barriers that make organizations reluctant to enter into a data sharing initiative, e.g. data copyright and ownership, high staff-turnover, and lack of financial and technical resources. Although the case studies focused on address data in South Africa, the research findings can equally apply to other spatial datasets and are relevant for the successful implementation of the South African Spatial Data Infrastructure (SASDI). / Dissertation (MIT)--University of Pretoria, 2012. / Computer Science / Unrestricted