Visual Exploration of Geographic Time Series using Interactive Maps

Proc. Int. Conf. on Computational Intelligence and Information Technology, CIIT Visual Exploration of Geographic Time Series using Interactive Maps Yogini Marathe 1,1, Pravin Game 1, 1 Pune Institute of Computer Technology, Pune, Maharashtra, India {yogini.marathe, pravingame}@gmail.com Abstract. The Geographic time series constitutes time based data collected from various geographic regions. Today, almost all the businesses have the data associated with geographic locations. Businesses need effective techniques to visualize this time series data for better analysis. Visualizing geographic data helps individuals to think in spatial terms. Also the interactive and dynamic displays having multiple representations of the data enhance the data exploration. Development of novel interaction algorithms, designing an effective display to show appropriate data with desired level of detail are still the research goals in geographic visualization area. Taking these research challenges into consideration, we aim to build a model for effective visualization and analysis of time series data using interactive maps which will help business users to do better analysis. Keywords: Geovisualization, Visual data exploration, Maps. 1 Introduction In [1], authors mention following as one of the long term research goal: Development of novel interaction algorithms incorporating machine recognition of the actual user intent and appropriate adaptation of main display parameters such as the level of detail, data selection, etc. by which the data is presented. We aim to develop a system that will help users to see the data at appropriate level of detail and filter the data based on various attributes. Geographic time series data can be explored with the help of three different dimensions i.e. what, when and where as mentioned in [2]. The analysts would like to get answers to the questions such as: 1. What exists at a given location at given point of time? 2. Where are the objects located at a given point in time or for a specified time interval? 3. When the given objects were present at given locations? The visual exploratory system should be able to satisfactorily answer all the questions as mentioned above. The system proposed by us would use interactive maps along with other data controls for effective exploration of geographical time series data. Elsevier, 2012 237

The paper is organized as follows, section 2 mentions the relative work, proposed system features and model in terms of mathematics is explained in section 3, Section 4 explains the hypothesis with the help of an illustration and in section 5 we conclude the work and list the future directions. 2 Related Work Various applications have been developed to explore the geographic data using maps such as the one for exploring multi scale pattern in avalanche data [3] or the one in [4] to explore a demographic and health survey data. The applications are specifically developed taking into consideration the domain aspects. The paper [5] contains information about application of visual exploration techniques for analyzing the simulations data in the field of computational fluid dynamics. Authors used multiple, linked views for effective visualization of different aspects of the data. Authors explain a suite of tools designed to facilitate the exploration of spatiotemporal data sets in [6]. The system enables users to search for interesting things in both space and time. Linked views and interactive filtering provide users with contextual information about their data and allow the user to develop and explore their hypotheses. The enhancements mentioned are moving from a visual analytics system to a predictive analytics system, creating views to allow for event planning, prediction and interdiction. Similar things are being explored by us. Paper [7] gives overall idea about how various visualization techniques can be used to explore the geographic data. Daniel Keim mentions The advantage of visual data exploration is that the user is directly involved in the data mining process. [8], the paper provides basic definitions and helps in understanding the Visual Exploration paradigm. In [9], authors have presented a method to visualize large-scale, dynamic relational data with the help of the geographic map metaphor. Similar thing can be used in our project. Paper [10], is about effectiveness of heat-maps for visualization of road incident data. Geovisualization is useful for detecting complex patterns in large network datasets as mentioned in [11] where authors explain a spatial-social network visualization tool for network analysis. From the literature survey above, it comes out that various techniques have been proposed for effective geographic or temporal data visualization, however effective techniques for visualizing data from both the perspectives still need enhancements. We aim to develop a system that will cater for effective visualization of both geographical as well temporal aspects and applicable to any system having spatiotemporal data. 3 Proposed System After reviewing various techniques used earlier, we propose the system with following important features that will help in better exploration: 1. Hierarchical views. 2. Linked views. 238

3. Enhanced user interaction in terms of a. Drill down actions on the map. b. Data filters to select appropriate measure attributes for analysis. c. Timescale slider to see data in a specific time range. The system being designed is generic and not limited to any domain. System handles multivariate time series data. Multivariate time series data and its importance in time series analysis are mentioned in [12]. Various components in the system are described in the subsections below. 3.1 Components The proposed system uses following components to aid visual exploration of the geographical timeseries data. 1. Map - Maps are very useful aid for visualization of geographical information present in input data. Various types of maps such as choropleth map, marker maps are used. 2. Chart - Chart is used to display statistical and/or trend information to be analyzed in the view. 2D as well as 3D charts are used to display appropriate dimensions. 3. Table - This shows time series data applicable to a specific view. 4. Hierarchy Tree - This is used for displaying the hierarchies applicable to the dataset. One or more hierarchies can be specified. User needs to configure the hierarchy when data is imported into the system. Appropriate dimension attribute from the input data set needs to be assigned at each level in the hierarchy. 5. Data filters - One or more data filters are implemented as drop down lists where user can select the attributes of the data for selective display of information in the view. 3.2 Model This section explains the system in terms of mathematical model. System is defined as S = {D, V, H} System components are as explained below: 1. D = {D 1, D 2,, D n }, is input geographic timeseries data that needs to be visually explored where Di = {Aa, At, Am, Ad}, individual record in the dataset a. Aa = {Sa, Ct, St, C, Z}, Address component in the data record Sa = Street address, Ct = City, St = State, C = Country, Z = Zip code b. At = Time component in the data record c. Am = Measure attributes used for analysis d. Ad = Dimension attributes used for grouping and hierarchy formation 2. V = {M, ζ, τ, Dv, F}, represent view for displaying data where a. M = {L, β, Mir, Amap, Mt}, is a geographic map where L = {L1, L2,, Lm}, set of locations where Li = {Lat, Long} such that Lat = Latitude, Long = Longitude β represent boundaries 239

Mir = value for map region e.g. world, country, state etc. Amap = Data attribute used for color coding of the map. Mt = Type of map such as choropleth, marker etc. b. ζ = {X, Y, ψ, ζt}, is a chart view where X = X axis variable and Y = {Y1, Y2,, Yx}, Y axis variables ψ = Statistical data associated with the chart such that ψ Dv ζt = Type of chart such as line, bar, pie etc. c. τ = Text display d. Dv = Data set associated with the view e. F = Filter control for data selection 3. H = {H1, H2,, Hk}, Hierarchies present in the data set based on the classification with respect to data attributes where Hi = {Hl, Al} where Hl = Level in the hierarchy Al = Attribute for hierarchy level that corresponds to one of the dimension attributes in the data Please note that the Data Dv is common for the view, so data is retrieved once and used for computation related to all view components. This also helps in implementing linked views. Following are the various functions related to data exploration: 1. Fg (Aa) Li, a function to convert address into geocode. Geocode is nothing but address in latitude, longitude form. 2. Fv (D) V, a function to display initial view with default hierarchy. 3. Fd (M) ζ, drill down function on map to see detailed data for the location. 4. Fclick (L) τ, click on location displays textual information. 5. Fselect (F) V, choose parameters with the help of Filter Control of view to load a new view. 6. Fexit (S), function to handle closing of application. 3.3 Workflow Following section explains general workflow for the data exploration. 1: Input: Data set D 2: Call Fv to display view with initial map attribute and default hierarchy. 3: Capture User Action 4: switch (User Action) 5: case Expand Hierarchy: 6: Show all the children of selected hierarchy level in Tree View 7: case Click on a level of Hierarchy: 8: Call Fv to load view to show data corresponding to the selected level. 9: case Collapse Hierarchy: 10: Reload view to remove data for closed levels and show data for current level. 11: case Drill down on Map: 12: Reload the map with new map region. 13: Call Fd to reflect the selected region in other components of the view. 240

14: case Click Map Location: 15: Call Fclick to show textual information corresponding to clicked location. 16: case Select a data record in one of the view components: 17: Highlight associated data record in other view components. 18: case Change View Parameters using Data Filter: 19: Call Fv to load view with new data as per the changed parameters. 20: case Press Exit: 21: Close all views and Exit from the system. 22: end switch 23: Go to Statement 3, Capture User Action 4 Illustration We illustrate working of system with the help of Sales data for a retail store. The sample data is as shown in the table 1. Actual and Predicted sales are number of items being sold. The data shown is snippet of the entire dataset. Assume following: 1. Data is present for other countries as well as for years 2003 to 2011. 2. Input data set D = Sample data shown in table 1. 3. Initial map region, Mir = World 4. Map type, Mt = Choropleth, Chart Type = Line chart 5. Map attribute for colour coding, Amap = Average Actual Sales per region. The product hierarchy and initial Map that will be shown for Product division is as shown in the figure 1. Assume following user action: User clicks on India, Selects city as Mumbai, Selects product type as Refrigerator and selects the Year range as 2006 2011. The resulting display is shown in figure 2. This is one of the ways to explore the data. User can vary the parameters at run time and visually explore the data in different ways to compare the results. Fig 1. Product Hierarchy and Initial Map 241

Table 1. Sample Sales data. Actual Predicted Coun State City Product Product Product Year Sales Sales try Division Type 200 400 India Maharashtra Pune Furniture Wooden Sofa 2003 100 450 India Maharashtra Mumbai Furniture Metal Sofa 2003 450 700 India Karnataka Bangalore Electronic Home Refriger ator 2004 500 250 India Maharashtra Mumbai Electronic Home Refriger ator 2004 150 270 India West Bengal Kolkata Electronic Office Printer 2004 350 500 India Tamil Nadu Chennai Apparel Men s Casuals 2004 200 350 India Gujarat Surat Apparel Women s Suit 2004 250 400 India Gujarat Ahmedabad Apparel Kids T-Shirts 2004 Fig 2. Sales Data 5 Conclusion and Future Work Many approaches have been specified for the geographical exploration of the data; however our approach differs from these in following ways: 1. The system is aimed to take care of any spatiotemporal data and not restricted to a particular domain. 2. Data on map along with time window slider helps in effective visualization of data from all three aspects (What, When and Where). 3. The statistical chart views would help the businesses for historical data analysis and future predictions. 4. The simple user interface with hierarchical view, and other data filters lead to a user friendly system. 242

The run time filtering and data population poses challenges as data being handled is huge. We are exploring various techniques for effective data handling such as Inmemory-storage and/or storing pre-computed datasets. The system is in prototyping stage and we are working on further implementations and optimizations for the same. Acknowledgments. This project work is being carried out at SAS R & D India Pvt. Ltd., Pune under the guidance of Mr. Dinesh Apte, Senior Software Manager at SAS R & D India. We would like to thank Dinesh Apte for his constant guidance and SAS R & D India for providing this opportunity. References 1. David Osimo and Francesco Mureddu, Research Challenge on Visualization, http://www.w3.org/2012/06/pmod/visualization.pdf 2. Timothee Becker, Visualizing Time Series Data Using Web Map Service Time Dimension and SVG Interactive Animation, http: //geoserver.itc.nl/timemapper/docs/becker- MScGFM.pdf 3. C. McCollister, Exploring multi-scale spatial patterns in historical avalanche data, Jackson Hole Mountain Resort, Wyoming, Elsevier, Cold Regions Science and Technology 37, pp.299-313, 2003 4. Etien L Koua and Menno-Jan Kraak, Geovisualization to support the exploration of large health and demographic survey data, International Journal of Health Geographics, pp. 3-12, 2004 5. Helmut Doleisch and Helwig Hauser, Interactive Visual Exploration and Analysis of Multivariate Simulation Data, IEEE Computing in Science and Engineering, 1521-9615/12, 2012. 6. Ross Maciejewski, A Visual Analytics Approach to Understanding Spatiotemporal Hotspots, IEEE transactions on visualization and computer graphics, vol. 16, no. 2, March/April 2010 7. Carolina Tobon, Visual and Interactive Exploration of Point Data, UCL Centre for advanced spatial analysis, working papers series, Paper 31, ISSN 1467-1298, Mar 2001. 8. Daniel A. Keim, Information Visualization and Visual Data Mining, IEEE transactions on visualization and computer graphics, vol. 7, no. 1, January-March 2002. 9. Daisuke Mashima, Stephen G. Kobourov, and Yifan Hu, Visualizing Dynamic Data with Maps, IEEE transactions on visualization and computer graphics, vol. 18, no. 9, September 2012 10.Dillingham, I., Mills, B. and Dykes, J., Exploring Road Incident Data with Heat Maps,Geographic Information Science Research UK 19th Annual Conference (GISRUK 2011), 27-29 Apr 2011, University of Portsmouth, Portsmouth, UK. 11.Wei Luo, Alan M. MacEachren, Peifeng Yin, and Frank Hardisty, Spatial-social network visualization for exploratory data analysis, Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks (LBSN 11). ACM, New York, NY, USA, 65-68, 2011. 12.Michael Leonard and Renee Samy, Forecasting Geographic Data, SAS Institute Inc, http://support.sas.com/rnd/app/ets/papers/geographicforecast.pdf 243