phil-pedruco/Total_crimes_against_women.csv

## readme.md

      
    Raw
  

              readme.md
            
          
    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.

  
## index.html
<!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>

## india.json

      
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    Raw
  

              india.json
            
          
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## Total_crimes_against_women.csv

          
            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
	<!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>
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