Visualizing Geospatial Graphs
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1 Visualizing Geospatial Graphs An introduction to visualizing geospatial graph data with KeyLines Geospatial USA: +1 (775) UK: +44 (0) Cambridge Intelligence Ltd, Regent Street, Cambridge, CB2 1AB, UK.
2 Introduction to geospatial graph data Geospatial graph data is data in which both the connections between the data points (nodes) and their locations are important. In other words, it is highly connected (i.e. graph) data whose where aspect is as important as the what. Some of the domains in which geospatial graph analysis is important include: Cyber security: network, IP, server and communications logs are all examples of connected data with a geographic dimension. Intelligence / law enforcement: joining the dots between events, people and locations can help analysts uncover intelligence faster. Anti-fraud: understanding where transactions take place can help financial institutions identify fraud. The most effective means of analyzing this geospatial graph data is through a process of visualization. It provides analysts with a simple way to explore and understand both the where and what aspects of their data. It is an interactive process that results in: 1. Higher quality data analysis 2. Faster access to critical data insight 3. More intuitive and comprehensive reporting By presenting complex connected geospatial data in a clear visual format, it becomes easier to explore, understand and communicate. The most intuitive way to present geospatial graphs is to overlay node-link charts on top of geographic maps: Labeling and standard graph visualization layouts are an inefficient way of exploring and understanding geospatial graph data By visualizing graph data directly on a map, we can instantly understand a node s geographic and connective properties Introducing KeyLines Geospatial KeyLines Geospatial is an integration for the KeyLines toolkit for geospatial graphs. It allows quick and easy integration of high quality geospatial graph visualization into applications with minimal development effort. The applications built with KeyLines Geospatial offer a compelling and intuitive way to understand the where aspect of your graph data, without losing sight of the all-important connections: 2
3 An example KeyLines application that uses the toolkit s Geospatial functionality to visualize 5.8m US domestic flights How does KeyLines Geospatial work? As long as your graph data has a geographic attribute it can be visualized with KeyLines Geospatial. This allows you to: View your network data on a map Transition seamlessly from a network view to a map view Zoom in and out, and pan around the map, with map tiles loaded as required Integrate maps with other KeyLines functionality, like filters and the time bar. The integration uses LeafletJS a popular JavaScript library for displaying interactive maps in the browser. There are several advantages to this setup: Power and speed Despite its tiny size (about 33kb) LeafletJS is incredibly fast and powerful, keeping your application response times short on all devices. Interactivity The library makes use of animation and CSS3 features to provide intuitive and smooth interactions, including pan and zoom. Flexibility KeyLines Geospatial uses LeafletJS to manage the mapping, but you can use whichever map tiles best suit your application. There are many different styles of map tiles available to choose. Note that some may require a license for commercial use 3
4 Case Study: Visualizing the Boston Hubway In this example, we are going to combine KeyLines Geospatial with the KeyLines Time Bar, to analyze three dimensions of our data: connections, geography and time. About the data The data from this example relates to the Boston Hubway scheme a bike sharing system in the Boston metro area. We took a cut of data detailing 60,916 bicycle journeys made during the month of April The data is available from We chose this data as it is a large, publicly available graph dataset with a temporal and geographic dimension. The techniques we are using, however, can be applied to any use case. Initial load In our visualization, each node is a HubWay station and each link is a journey. On first load into a network view, so many connected nodes just create a hairball: KeyLines standard layout attempts to reduce the hairball effect by minimizing link overlap, but this dataset is just too dense. Technique 1: Combine geography with temporal analysis to uncover events At this level, despite the hairball, we can get still get some useful time pattern information using the time bar. There are some interesting peaks and troughs through the week as one would expect from a system heavily used by commuters. 4
5 Technique 2: Switching to map mode To learn more about the data s geospatial properties, we need to switch into map mode: This shows us the general geography of the scheme, but the volume of links still makes it difficult to uncover any specific patterns. Technique 3: Coloring and link weighting One useful technique is to use color to highlight specific node properties. In this case, we can color the nodes by activity and weight the links to reflect traffic volumes: Here, we can clearly see one station that is busier than the rest during the month of April, in the geographic center of the graph. The station is MIT at Mass Ave / Amherst St. Useful information for ensuring enough cycles are available in high-traffic locations: 5
6 Using the time bar, we can also compare daily traffic patterns. It is no surprise, for example, that Boston s main railway terminus, South Station, is significantly; busier on Monday compared to Saturday: Another useful bit of insight we can glean from this data is which stations are net losers/gainers of bikes. Maintaining level stocks of cycles where and when they are needed is a balancing act. Let s change the coloring to show net loss/gain per station: 6
7 Beacon St / Mass Ave. managed to lose a total of 225 bikes in April. They all seem to be heading towards Harvard Square, which ends April with a surplus of 192 bikes. Technique 4: Filtering When working large graph datasets, filtering can help cut out noise and focus on the important parts of the data. In addition to filtering by time and date (using the time bar), users can filter the visualization based on any logic you can define. In this example, we have included a slider to filter the journeys by duration: Here we can only see rentals taking more than 55 minutes. As would be expected, these journeys stretch across the city, with the most common journey being the 4.4 mile trip between Harvard Square and Arlington Station. 7
8 Try it yourself! This is a fun and easy to understand snapshot of the power of KeyLines Geospatial. The functionality shown can be used for any graph dataset - whether you want to understand cyber threats, detect fraud or explore any other kind of data connections. Being able to see flow of traffic gives real power to both identify risks, and optimize a network for performance and efficiency. As part of a KeyLines evaluation you get full access to all our demos including the JavaScript source code for the featured HubWay demo, so you can quickly and easily apply any of these techniques to your own data. To start an evaluation and to try the toolkit for yourself, visit Want to learn more? We have extra resources and information available to download from our website. If you have any questions about pricing, or would like a free trial, just get in touch. We would be delighted to help! USA: +1 (775) UK: +44 (0) Cambridge Intelligence Ltd, Regent 8 Street, Cambridge, CB2 1AB, UK.
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