1 00:00:04,500 --> 00:00:06,930 Let me comment on the merits of heatmaps 2 00:00:06,930 --> 00:00:10,710 as a way of representing data in the context of representing 3 00:00:10,710 --> 00:00:12,660 crime activity. 4 00:00:12,660 --> 00:00:14,350 Criminal activity-related data often 5 00:00:14,350 --> 00:00:17,200 has both components of time and location. 6 00:00:17,200 --> 00:00:20,880 Sometimes, all that is required is a line chart, 7 00:00:20,880 --> 00:00:23,180 but heatmaps can visualize data that 8 00:00:23,180 --> 00:00:25,190 will be too big for a table. 9 00:00:25,190 --> 00:00:28,070 Plotting data on maps is much more effective 10 00:00:28,070 --> 00:00:33,900 than a table for location based data, and is eye catching. 11 00:00:33,900 --> 00:00:36,560 What is the edge of predictive policing? 12 00:00:36,560 --> 00:00:39,970 Many police forces are exploiting their databases 13 00:00:39,970 --> 00:00:44,020 to focus finite resources on problem areas. 14 00:00:44,020 --> 00:00:47,720 Not only do analytics help improve policework, 15 00:00:47,720 --> 00:00:50,940 the outputs are also good communication tools 16 00:00:50,940 --> 00:00:53,590 to decision makers in government, and the wider 17 00:00:53,590 --> 00:00:54,690 public. 18 00:00:54,690 --> 00:00:58,210 The application of analytics to data like this 19 00:00:58,210 --> 00:01:01,180 is new and growing, with companies 20 00:01:01,180 --> 00:01:06,790 like PredPol and Palantir leading the effort.