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LSI-STAT - a Visualization and Analytics PlatformAuthor: Neha Pande Date: 2018-07-27 Report no: IIIT/TH/2018/56 Advisor:K S Rajan AbstractGeovisualization or geographic visualization makes patterns become apparent which otherwise would not have been deciphered. It prompts users into thinking and developing new hypothesis which leads them to make better decisions based on the visualization. It can be 2D or 3D visualization generated using real or simulated data. Moreover, both static as well as dynamic processes can be visualized using geovisualization softwares and tools. The visualizations are graphical representations of properties and relationships between objects in space and time. Today, a large variety of geovisualization tools are available. Each of these have a different set of features. Study of current geovisualization tools was done to identify set of unique features. Based on this study, features of these tools were divided into data modelling, data analysis and data visualization. Data visualization helps us explore data, build and validate hypothesis. To gather further insights and identify patterns in this data, analytical functions are required. In some cases, you will have to model the data. Modelling is required to convert input data and develop a more sensible visualization. Different combinations of these features exist in various tools. Along with this, it is also important to understand other aspects such as whether the tool is map-based or graph-based, whether is open source or proprietary, what kind of data input format they have, whether it is interactive or not among others. This work involves the design and development of LSI-STAT, a web-based spatio-temporal interactive analytical platform. It is a visualization platform which captures both spatial and temporal trends in map along with neighborhood. It is a SOLAP platform which extends OLAP capabilities to Spatial. It provides user with some basic querying capabilities based on OLAP principle. The platform models on user-given data. It computes data aggregation over field names based on hierarchy. This hierarchy is user-defined. It enables users to do an in-depth visual analysis of data to discover hidden insights and patterns in data. With this platform, it is expected that users can visualize charts in combination with maps spatially distributed over the area of interest thus providing for a more enhanced integrated spatio-temporal experience. It allows user to configure various parameters and define hierarchy and time-intervals as required. A large number of visualizations can be generated by the user with the help of this platform using location, time and attribute combinations. The developed platform allows user to select multiple data attributes and generate chart for geovisualization. With the toggle basemap option, user can change the basemap used in geovisualization. As geo-spatial extent of the data can change the perspective of looking at the data at multiple geo-levels, the information is tagged to these zoom levels. Also, we anticipate that as the usage of this tool builds, there can be some pre-defined functions and some new toolsets may need to be incorporated. Hence, the platform has been entirely developed using a suite of open-source technologies. To demonstrate the utility and functionalities of the developed tool and to provide for a good understanding of the value that it provides, two case studies have been presented in this thesis. To cover the range, 2 extremely different levels of case studies have been taken - local and global level. Additionally, both of them have different number of parameters. One of them is a GeoBI case study which was done on a large online retail data of three products sold in different countries during January 2009. The combined view of the temporal trends over the regions helps provide insights into ’what sells more where’, ’is the growth patterns similar or not’, etc. It is apparent that this approach helps overcome the fragmented view that the separate views of space and time using the earlier paradigm provided. Another case study that was done using this tool was on Rajiv Aarogyasri health insurance data of Khammam district in Telangana for the years 2013 to 2015. Over the three year analysis which was done using this tool, it was seen that the map showed a lack of access to a large population. Results can help a policy maker to find places that are inaccessible, have limited access to healthcare facilities, lack certain healthcare services or have fewer number of healthcare facilities. Further, they can find the geographical extents of the population that are actually utilizing different healthcare facilities. By analyzing the geographical coverage and spatial distribution of existing healthcare facilities, they can take decisions for scaling up the existing healthcare facility network. It helps them to easily locate places to setup new healthcare facilities and identify services that need to be added to the healthcare facility. Also, capturing the temporal trends here, can be informative in understanding how these services or facilities fare or provide the right level of service. The platform can be extended later to include more features such as time slider, word cloud, supporting multiple data input types, supporting overlay with external datasets such as road network etc. Interaction time can be improved by using tile-based rendering. Spatial predicate, neighborhood queries and displaying multiple graphs in a single chart can be supported in future. Full thesis: pdf Centre for Spatial Informatics |
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