var x = d3.time.scale() .range([0, width]);

var y = d3.scale.linear() .range([height, 0]);

var area = d3.svg.area() .x(function(d) { return x(d.date); }) .y0(height) .y1(function(d) { return y(d.rate); });

var line = d3.svg.line() .x(function(d) { return x(d.date); }) .y(function(d) { return y(d.rate); });

d3.csv("https://www.cityam.com/special/d3/unemployment_regions.csv", type, function(error, data) {

// Nest data by region. var regions = d3.nest() .key(function(d) { return d.region; }) .entries(data);

// Compute the maximum rate per region, needed for the y-domain. regions.forEach(function(s) { s.maxrate = d3.max(s.values, function(d) { return d.rate; }); });

// Compute the minimum and maximum date across regions. // We assume values are sorted by date. x.domain([ d3.min(regions, function(s) { return s.values[0].date; }), d3.max(regions, function(s) { return s.values[s.values.length - 1].date; }) ]);

// Add an SVG element for each region, with the desired dimensions and margin. var svg = d3.select("#cam-map-immigration").selectAll("svg") .data(regions) .enter().append("svg") .attr("width", width + margin.left + margin.right) .attr("height", height + margin.top + margin.bottom) .append("g") .attr("transform", "translate(" + margin.left + "," + margin.top + ")");

// Add the area path elements. Note: the y-domain is set per element. svg.append("path") .attr("class", "area") .attr("d", function(d) { y.domain([0, 12]); return area(d.values)});

// .attr("fill", function(d) { d.values.color > 0 ? "#00ff00" : "#ff0000" });//

// Add the line path elements. Note: the y-domain is set per element. svg.append("path") .attr("class", "line") .attr("d", function(d) { y.domain([0, 12]); return line(d.values); });

// Add a small label for the region name. svg.append("text") .attr("class", "bolder") .attr("x", width - 6) .attr("y", height - 6) .style("text-anchor", "end") .text(function(d) { return d.key + " " + d.values[d.values.length - 1].rate.toFixed(1) + "%"; }) ; });

function type(d) { d.rate = +d.rate; d.date = parseDate(d.date); return d; }

Here are the areas with the greatest number of (net) migrants, from all sources, per 100 population. 

As we can see, it’s not just the usual suspects. Top of the list for desirability is actually Ceredigion in Wales.

For gross numbers of people moving in from overseas, the top 10 is much more predictable. But some of the destinations imply wealth. A house in Westminster, for example, had an average asking price of £1.3m in December 2011 (the end of the year the census was carried out), while Kensington and Chelsea, top on the list, was even higher at almost £2m.

What the data implies is that fears about immigration may be misplaced, especially as a large volume of research has implied that immigration is beneficial to the economy.