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OK Voting Trends
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README.md

Voting trends in Oklahoma legislature

This is a two part project:

  1. Retrieve and organize legislator's votes on every bill in a particular session.
  2. Analyze and publish the findings of voting trends amongst the legislators.

This work was originally spawned by @MadiLAlexander. I've gladly contributed.

The overall goal of this work is to illuminate previously unknown trends in the state legislature. There is a wealth of analysis and insight to be gained through this project and further augmentation will be fruitful in more robust and interesting ways.

Part 1: Retrieval

Using the Sunlight Foundation's OpenStates api, we were able to get a full dataset of every legislator, every bill of the current session, and every deciding vote on those bills by the legislators. This data was parsed and organized into csv files allowing manipulation in spreadsheets and availability for visualization and presentation on the web.

All code and datasets are publicly available on GitHub.

Part 2: Analysis

I'm creating a series of visualizations of the data to highlight various dimensions and reveal some trends. To start, I'm creating a matrix of values that represent two legislators voting similarity. This is based on cross reference of each legislator's votes to another legislator's votes. The possible similarity scores range from 0 to 1. 0 representing no voting similarity between two legislators and 1 representing precise complete similarity between the two legislators.

  • 0 = no votes were the same between two legislators
  • 1 = all votes were the same between two legislators

I created a color scale to visually show the difference in the voting similarities. The darker the shade of blue the higher the similarity in votes between the two legislators. Conversely the closer to 0 in similarity score the lighter the shade of blue between the two legislators.

This visualization models the lower chamber voting trends, while another models the upper chamber voting trends. Another page will present the final versions of all visualizations.

This visualization does not analyze the votes based on any qualities of the bills themselves. Ideas I had to add more dimensions to the data are:

  • demographic data -- race, gender, age
  • years held in office
  • party affiliation

I want to find voting trends that aren't obvious to a casual consumer of politics in Oklahoma. Is there a virtual quorum in state government that goes beyond party line? How conservative/liberal are representatives voting trends? How polarized is the state legislature?

Raw
index.html
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Raw
lower.csv
leg_id OKL000148 OKL000173 OKL000267 OKL000180 OKL000254 OKL000172 OKL000193 OKL000197 OKL000186 OKL000167 OKL000200 OKL000257 OKL000179 OKL000183 OKL000065 OKL000185 OKL000190 OKL000261 OKL000187 OKL000203 OKL000071 OKL000178 OKL000165 OKL000164 OKL000191 OKL000264 OKL000194 OKL000175 OKL000195 OKL000202 OKL000253 OKL000266 OKL000189 OKL000169 OKL000096 OKL000166 OKL000058 OKL000116 OKL000066 OKL000093 OKL000051 OKL000064 OKL000103 OKL000086 OKL000074 OKL000092 OKL000115 OKL000056 OKL000123 OKL000090 OKL000109 OKL000146 OKL000108 OKL000132 OKL000143 OKL000125 OKL000118 OKL000111 OKL000121 OKL000098 OKL000060 OKL000104 OKL000105 OKL000087 OKL000113 OKL000054 OKL000124 OKL000126 OKL000057 OKL000100 OKL000106 OKL000133 OKL000077 OKL000134 OKL000149 OKL000082 OKL000174 OKL000130 OKL000061 OKL000084 OKL000097 OKL000110 OKL000127 OKL000147 OKL000055 OKL000062 OKL000129 OKL000140 OKL000075 OKL000063 OKL000072 OKL000089 OKL000107 OKL000069 OKL000059 OKL000144 OKL000081 OKL000101
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OKL000144 0.892857143 0.731070496 0.724935733 0.820580475 0.751898734 0.754285714 0.754475703 0.727762803 0.892307692 0.664864865 0.916216216 0.666666667 0.778625954 0.750642674 0.773109244 0.761658031 0.779487179 0.929487179 0.778097983 0.854219949 0.711111111 0.714652956 0.723237598 0.715542522 0.720101781 0.932642487 0.719346049 0.862244898 0.736702128 0.715012723 0.757731959 0.742782152 0.722077922 0.80952381 0.72987013 0.737609329 0.75862069 0.860273973 0.728426396 0.765743073 0.721621622 0.791044776 0.878326996 0.793281654 0.732782369 0.851428571 0.862244898 0.88630491 0.75862069 0.888579387 0.762803235 0.788732394 0.80878553 0.86996904 0.748110831 0.842541436 0.868501529 0.766497462 0.66572238 0.756962025 0.869897959 0.756476684 0.694235589 0.694300518 0.763925729 0.907042254 0.736040609 0.730650155 0.770618557 0.837545126 0.758426966 0.920588235 0.75 0.880368098 0.753926702 0.914634146 0.744382022 0.766756032 0.769633508 0.713157895 0.952522255 0.774480712 0.735449735 0.724324324 0.695652174 0.742937853 0.894428152 0.798866856 0.719480519 0.783382789 0.89375 0.818181818 0.714659686 0.750750751 0.723237598 1 0.904392765 0.701265823
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OKL000101 0.624260355 0.894433781 0.893186004 0.815589354 0.892531876 0.891891892 0.878676471 0.905950096 0.739371534 0.895874263 0.726744186 0.9082397 0.875457875 0.87150838 0.851190476 0.882899628 0.866055046 0.74595843 0.865853659 0.76416819 0.896078431 0.910946197 0.875232775 0.875776398 0.888073394 0.780075188 0.87890625 0.786764706 0.891840607 0.887850467 0.900917431 0.893939394 0.903345725 0.858672377 0.87164751 0.875510204 0.869565217 0.823984526 0.907103825 0.889692586 0.883268482 0.898832685 0.731903485 0.87962963 0.866533865 0.74375 0.737614679 0.795539033 0.876826722 0.761904762 0.870155039 0.875510204 0.869888476 0.656455142 0.896551724 0.73964497 0.738095238 0.87706422 0.884297521 0.903811252 0.799270073 0.875925926 0.879279279 0.894444444 0.866791745 0.737843552 0.903846154 0.870614035 0.878453039 0.663072776 0.858 0.728744939 0.860294118 0.805369128 0.871212121 0.771058315 0.892057026 0.882917466 0.86728972 0.891181989 0.75 0.862579281 0.871153846 0.893145161 0.904672897 0.847695391 0.773019272 0.857740586 0.899628253 0.85021097 0.767281106 0.851111111 0.90960452 0.884353741 0.9 0.701265823 0.768518519 1
Raw
script.js
var margin = 100,
width = 1000 - margin,
height = 1000 - margin,
colorRange = ["#f1eef6", "#045a8d"],
x = d3.scale.ordinal().rangeBands([0, width]).domain(d3.range(99));
var color = d3.scale.linear().range(colorRange).domain([.59, 1]);
var svg = d3.select('body')
.append('svg')
.attr("width", width + (margin * 2))
.attr("height", height + (margin * 2))
.append("g")
.attr("transform", "translate(" + (margin) + "," + (margin) + ")");
d3.csv('lower.csv', function(error, data) {
if (error) return console.log(error);
var row = svg.selectAll(".row")
.data(data)
.enter()
.append("g")
.attr("class", "row")
.attr("transform", function(d, i) { return "translate(0," + x(i) + ")"; })
.each(row);
row.append("line")
.attr("x2", width);
row.append("text")
.attr("x", -6)
.attr("y", x.rangeBand() / 2)
.attr("dy", ".32em")
.attr("text-anchor", "end")
.text(function(d) { return d.leg_id; });
var column = svg.selectAll(".column")
.data(data)
.enter().append("g")
.attr("class", "column")
.attr("transform", function(d, i) { return "translate(" + x(i) + ")rotate(-90)"; });
column.append("line")
.attr("x1", -width);
column.append("text")
.attr("x", 6)
.attr("y", x.rangeBand() / 2)
.attr("dy", ".32em")
.attr("text-anchor", "start")
.text(function(d) { return d.leg_id; });
function row(row) {
var array = Object.keys(row).map(function(k) { return row[k]; });
array.shift();
var cell = d3.select(this).selectAll(".cell")
.data(array)
.enter()
.append("rect")
.attr("class", "cell")
.attr("x", function(d, i) { return x(i); })
.attr("width", x.rangeBand())
.attr("height", x.rangeBand())
.style("fill", function(d) { return color(d); });
}
})
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