1. Mapping the Fear of Crime, a Web Based GIS Solution to Capturing “Fuzzy” Geography – or Andy Evans – Leeds University http://www.ccg.leeds.ac.uk/ Centre for Computational Geography, School of Geography, University of Leeds Leeds, UK, LS2 9JT Tim Waters – Bradford Council http://www.bradford.gov.uk Jacobs Well, Bradford, BD1 5RW what “the dodgy area near me Gran’s” means
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20. Input GUI User sprays on map Change spray size Adds new areas Wipe the map Writes in comments When done clicks on “send” button
25. Compression WHY? From 859K to 14K !! E.G. a combined image and data object of 859Kb was compressed to 67Kb just using the GZip algorithm, and further compressed to 14Kb with the addition of the shrinking process. Other algorithms could improve on this. User dots Density map Shrunk to 1/5 size GIF style compression GZip Sent
26. Averaging - User tests suggested a 9x9 pixel averaging kernel best represented the areas users had drawn using the dots. - Tests suggested this could be shrunk to 5 times the size and re-inflated without users noticing a significant change in the image.
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32. Comments Guard House estate is probably the worst of Keighley's large estates for high crime. Anti social behaviour around the station and shops These area elected the BNP – (hate crime). Town centre is worst. The last question 'what would make you feel safer' encourages dependency on services and precludes personal involvement. My perceptions are that Keighley is generally safe, but that crime/anti-social behaviour takes place around the railway station/Chrome however I generally feel safe during the day/when there are people around There will always be a perception of crime in all areas including on your own door stop. Known areas of crime are in areas typical of early council housing estates. Utley drug dealing in woods near Cliffe Castle About areas, about persons experiences, meta, specific problems
33. Results Real density Combined inputs Recorded - Perceived
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Notes de l'éditeur
Fuzzy areas & boundaries are not the same as traditional points, polygons, distinct areas – the boundaries, attributes or variables are vague or undefined. They can occur for a number of different reasons Continuous – where does the mountain start (and hill begin) Aggregation – when data is aggregated, for example many different soil types are lumped together under one type Averaging – a river on a map is pretty much its average course, since the river will alter and move its course over time Ambiguity – Where the data is ambiguous, I.e. defining “high” crime areas as opposed to low
Socially biased: Elderly, women and minorities have the most anxiety regarding crime. BUT these groups actually are the least likely to experience violent crime. (British Crime Survey)
Crime reporting in the news media (especially involving sex or violence) creates a distorted picture of reality which is reflected in the beliefs of news consumers. (Williams and Dickinson, 1993)
Sprayings are then aggregated and weighted
Click on map of combined areas. Comments of the people who weighted that area as most important float to the top.
Applets and perl cgi: server just stored data, each applet transferred in and out the whole combined file Applets and j2ee server : server does processing, one applet for input, just sends image
The differences on the map above were generated in a very arbitrary manner: this highest perceived crime area levels were stretched to the highest real crime levels. Blue areas have higher crime than expected, and red areas lower. Red areas are perceived to have higher level of crime than actually, and blue areas lower The main red area is just above the town centre , possible people mistook the centre of town? The round blue spot is an area of terraced houses And the blue area to the south – concentrated urban area. Bracken bank south - Guard house to the west
Possible actions for policy : look at these areas and why
Fuzzy – building membership rules. For example if crime is “high” and perception is “high” then investment is high Ideally this analysis would be backed up by questionnaires to define the crime levels people felt were high, and sprays of areas people felt they knew about To test whether their knowledge was representative of the broader population, or to compare or normalise levels of crime concern with the familiararity of areas. Data of users and where people live or where they know might also allow us to disaggregate their results. Looking at the comments uses made does give us an insight into how they chose their areas.
Ethical – area tainting. Legal – slander & responsibilities. Online publishing etc. Maintenance of moderation vs open govt, public participation Good point is its use as a public consultation exercise – just by gathering in comments – easy to handle is good.
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