This map showning the rates of crises against women for 2015 is based on the data and analysis by Tinniam V Ganesh from this post. The darker areas have higher crime rates against women and the lighter are lower crime rates. The GIS data is from Diva GIS.
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <meta charset="utf-8"> | |
| <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1"> | |
| <title>Crimes against Women in India</title> | |
| <script type="text/javascript" src="http://d3js.org/d3.v3.min.js"></script> | |
| <script type="text/javascript" src="http://d3js.org/topojson.v1.min.js"></script> | |
| </head> | |
| <body> | |
| <div id="map"></div> | |
| </body> | |
| <script type="text/javascript"> | |
| var h = 500, | |
| w = 960; | |
| // set-up unit projection and path | |
| var projection = d3.geo.mercator() | |
| .scale(1) | |
| .translate([0, 0]); | |
| var path = d3.geo.path() | |
| .projection(projection); | |
| // set-up svg canvas | |
| var svg = d3.select("body").append("svg") | |
| .attr("height", h) | |
| .attr("width", w); | |
| // set-up scale for colour coding crime | |
| var cScale = d3.scale.linear() | |
| .domain([0, 1]); | |
| // read in topojson of India | |
| d3.json("data/india.json", function(error, india) { | |
| // crime statistics from https://gigadom.wordpress.com/2015/01/16/a-crime-map-of-india-in-r-crime-against-women/ | |
| d3.csv("data/Total_crimes_against_women.csv", function(error, crimes) { | |
| var cRange = d3.extent(crimes, function(d, i) { | |
| return +d["2015"] | |
| }); | |
| cScale.domain(cRange); | |
| var states = []; | |
| crimes.forEach(function(d) { | |
| var el = d.State | |
| states.push(el) | |
| }); | |
| var bTopo = topojson.feature(india, india.objects.india), | |
| topo = bTopo.features; | |
| topo.forEach(function(d, i) { | |
| var n = states.indexOf(d.properties.NAME_1); | |
| if (n !== -1) { | |
| d.properties.crime = crimes[n]["2015"]; | |
| } else { | |
| d.properties.crime = null; | |
| } | |
| }); | |
| // calculate bounds, scale and transform | |
| // see http://stackoverflow.com/questions/14492284/center-a-map-in-d3-given-a-geojson-object | |
| var b = path.bounds(bTopo), | |
| s = .95 / Math.max((b[1][0] - b[0][0]) / w, (b[1][1] - b[0][1]) / h), | |
| t = [(w - s * (b[1][0] + b[0][0])) / 2, (h - s * (b[1][1] + b[0][1])) / 2]; | |
| projection.scale(s) | |
| .translate(t); | |
| svg.selectAll("path") | |
| .data(topo).enter() | |
| .append("path") | |
| .style("fill", function(d, i) { | |
| if(d.properties.crime === null) { | |
| return "grey"; | |
| } else { | |
| return interp(cScale(+d.properties.crime)); | |
| } | |
| }) | |
| .style("stroke", "black") | |
| .attr("d", path); | |
| }); | |
| }); | |
| function interp(x) { | |
| var ans = d3.interpolateLab("#ffffe5", "#004529")(x); | |
| return ans | |
| } | |
| </script> | |
| </html> |
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| Rank | State | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | |
|---|---|---|---|---|---|---|---|---|
| 3 | Andhra Pradesh | 41971.47 | 43406.07 | 44840.67 | 46275.26 | 47709.86 | 49144.46 | |
| 27 | Arunachal Pradesh | 189.08 | 190.22 | 191.36 | 192.5 | 193.64 | 194.78 | |
| 8 | Assam | 17617.21 | 18176.91 | 18736.61 | 19296.31 | 19856.01 | 20415.71 | |
| 12 | Bihar | 13143.79 | 13744.78 | 14345.77 | 14946.77 | 15547.76 | 16148.75 | |
| 15 | Chhattisgarh | 6799.26 | 6942.73 | 7086.21 | 7229.68 | 7373.16 | 7516.64 | |
| 20 | Delhi | 3728.21 | 3657.41 | 3586.61 | 3515.81 | 3445.01 | 3374.21 | |
| 24 | Goa | 225.71 | 230.41 | 235.11 | 239.81 | 244.51 | 249.21 | |
| 6 | Gujarat | 25225.33 | 26259.87 | 27294.41 | 28328.95 | 29363.49 | 30398.03 | |
| 13 | Haryana | 7488.62 | 7583.23 | 7677.84 | 7772.45 | 7867.05 | 7961.66 | |
| 22 | Himachal Pradesh | 1420.79 | 1436.9 | 1453 | 1469.11 | 1485.22 | 1501.33 | |
| 17 | Jammu and Kashmir | 5004.2 | 5199.95 | 5395.69 | 5591.44 | 5787.19 | 5982.94 | |
| 16 | Jharkhand | 5719.14 | 5915.81 | 6112.49 | 6309.16 | 6505.84 | 6702.51 | |
| 9 | Karnataka | 16662.48 | 17341.5 | 18020.51 | 18699.52 | 19378.53 | 20057.54 | |
| 11 | Kerala | 14300.59 | 14806.8 | 15313 | 15819.21 | 16325.42 | 16831.62 | |
| 5 | Madhya Pradesh | 29155.17 | 29639.13 | 30123.09 | 30607.05 | 31091.01 | 31574.97 | |
| 2 | Maharashtra | 43434.42 | 44636.9 | 45839.38 | 47041.85 | 48244.33 | 49446.8 | |
| 26 | Manipur | 179.23 | 184.34 | 189.45 | 194.56 | 199.67 | 204.79 | |
| 23 | Meghalaya | 276.48 | 295.92 | 315.35 | 334.79 | 354.22 | 373.65 | |
| 25 | Mizoram | 189.39 | 195.2 | 201 | 206.81 | 212.61 | 218.41 | |
| 29 | Nagaland | 74.55 | 77.78 | 81.02 | 84.26 | 87.5 | 90.73 | |
| 7 | Orissa | 16496.3 | 17360.02 | 18223.73 | 19087.44 | 19951.16 | 20814.87 | |
| 18 | Punjab | 4489 | 4524.73 | 4560.46 | 4596.19 | 4631.92 | 4667.65 | |
| 10 | Rajasthan | 16719.21 | 17046.07 | 17372.92 | 17699.77 | 18026.62 | 18353.48 | |
| 28 | Sikkim | 77.73 | 81.69 | 85.64 | 89.6 | 93.56 | 97.52 | |
| 14 | Tamil Nadu | 9175.27 | 8846.01 | 8516.74 | 8187.48 | 7858.21 | 7528.94 | |
| 19 | Tripura | 2692.64 | 2876.81 | 3060.99 | 3245.16 | 3429.34 | 3613.51 | |
| 1 | Uttar Pradesh | 75662.09 | 79874.18 | 84086.27 | 88298.36 | 92510.45 | 96722.55 | |
| 21 | Uttaranchal | 1721 | 1697.62 | 1674.23 | 1650.85 | 1627.46 | 1604.08 | |
| 4 | West Bengal | 31828.74 | 33474.57 | 35120.41 | 36766.24 | 38412.07 | 40057.9 |
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