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Political Polarization in the US House of Representatives

If you intend to fork this, please note that it contains my Google Analytics tracking code.

Summary

This is a visualization of political polarization in the US House of Representatives, as calculated by DW-NOMINATE. DW-NOMINATE allows one to calculate the political leaning of a member simply by comparing their voting record to others', ignoring their party affiliation and even the content of the bills they vote on.

Design

My initial idea was to draw the career progression of every House member as a distinct path, color coded according to their party affiliation in any given congress. The user would also be able to select members to view detailed statistics about them. But when I implemented that, I discovered it was far too noisy. Trends were difficult to make out, few individual members were discernible, and the elements used to represent them were so small that they were almost impossible to select. Feedback #1 confirmed that this was a problem.

I added aggregated statistics, to make the trends visible, but there was still too much noise. I briefly removed all members whose careers fell completely within the 5-95 percentile range of their parties. That helped a bit, but not enough, and it looked strange. Then I removed all members who had never served as an independent, which helped a lot. I was able to increase the size of the nodes, making them easier to see and easier to select.

Meanwhile, I also decided it would be useful to be able to see statistics about each individual congress. And once those statistics were available, I also wanted them to be available as distinct graphs, since they couldn't easily be seen on the main ones. So I added some secondary graphs that could be displayed hovering over the main graph.

In response to feedback #3, I flipped the vertical orientation of the graph, agreeing that people were more likely to be interested in recent congresses than historical ones. I also added some annotations noting significant periods and events.

To provide more detail about independents, I broke them down into their individual parties, and provided statistics and highlighting for each of them.

I made the green slightly darker to make it easier to see, and also made highlights more obvious.

Following comments on the first version I submitted, I improved the exposition above the graph, added axis labels and a title, and rearranged the legend.

Feedback

  1. The green of the independents is a bit hard to see against the grey background. It’s also hard to intentionally get the right cursor position to trigger the illumination of individual representatives.

  2. [What do you learn from this graph?] That there were a lot of independents back in ye olden days. That some independents moved a long way. That the Democrats lost a lot of members.

  3. [Same person as 2] That light green is kinda hard to see. My guess is that people are more interested in the present so I'd flip it. You could link to sources on the results - so people could look up why there was a huge jump. Or you could annotate noticeable jumps / longstanding independents.

  4. The independent representation is definitely the visual weak spot of this graph. I'm not getting enough easy information.

Resources

The DW-NOMINATE data set itself is from http://voteview.com/dwnomin.htm.

I drew much inspiration from an XKCD poster: http://xkcd.com/1127/large/.

Many wikipedia pages were useful for research, and are linked from the visualization itself.

The .min.js files are all libraries available on http://cdnjs.com.

Previous versions

The feedback that I acquired was based on the following git commits:

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// Wrapper aronud d3.scale.linear, allowing internal margins to be added. Only
// allows simple linear scales (not polylinear).
// This doesn't really make sense as a specific type of scale, but there's no
// way to add .margin() to existing scales without completely reimplementing
// them.
d3.scale.margin = function () {
var range, domain, margin = [0, 0];
var linear;
function rescale () {
linear = d3.scale.linear();
if (domain)
linear = linear.domain(domain);
if (range)
linear = linear.range([range[0] + margin[0],
range[1] - margin[1]]);
scale.ticks = linear.ticks;
scale.tickFormat = linear.tickFormat;
return scale;
}
function scale (x) {
return linear(x);
}
scale.domain = function (x) {
if (!arguments.length)
return domain;
domain = x;
return rescale();
}
scale.range = function (x) {
if (!arguments.length)
return range;
range = x;
return rescale();
}
scale.margin = function (x) {
if (!arguments.length)
return margin;
margin = x.map(Number);
return rescale();
}
scale.copy = function () {
var copy = d3.scale.margin().margin(margin);
if (domain)
copy = copy.domain(domain);
if (range)
copy = copy.range(range);
return copy;
}
return rescale();
}
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0!==c&&(c=[].concat(c)),d=[],a.forEach(function(a){null===a[b]?d.push(a):c.indexOf([].concat(a[b]).join("/"))>-1&&d.push(a)},this)),d},b.getUniqueValues=function(a,b){var c=[];return null!==b&&void 0!==b&&(b=[].concat(b),a.forEach(function(a){var d="";b.forEach(function(b,c){c>0&&(d+="/"),d+=a[b]},this),-1===c.indexOf(d)&&c.push(d)},this)),c},b.newSvg=function(b,c,d){var e=null;if((null===b||void 0===b)&&(b="body"),e=a.select(b),e.empty())throw"The '"+b+"' selector did not match any elements. Please prefix with '#' to select by id or '.' to select by class";return e.append("svg").attr("width",c).attr("height",d)},b});
// right-compose: rcps(a, b, c)(o) == c(b(a(o))). This applies the functions in
// the order written. Additionally, any argument to rcps may be a number or a
// string, in which case it's used as an index. Example:
// rcps('date', _.isDate)(o) == _.isDate(o['date'])
function rcps () {
var args = arguments;
var len = args.length;
return function (o) {
for (var i = 0; i < len; i++) {
var arg = args[i];
if (typeof arg === 'function')
o = arg.call(this, o);
else
o = o[arg];
}
return o;
}
}
// Turn [ { key: k0, values: v0 }, ... ] into just [ v0, ... ]. But also add a
// .key property, with .key[k0] = v0.
function restructure_nest (nested) {
nested.key = {};
for (var i = 0; i < nested.length; i++) {
var d = nested[i];
nested.key[ d.key ] = d.values;
nested[i] = d.values;
}
}
var parties = {
100: "Democrat",
112: "Conservative",
114: "Readjuster",
117: "Readjuster Democrat",
200: "Republican",
203: "Unconditional Unionist",
206: "Unionist",
208: "Liberal Republican",
213: "Progressive Republican",
326: "National Greenbacker",
328: "Non-affiliated",
329: "Non-affiliated (D aligned)",
331: "Non-affiliated (R aligned)",
340: "Populist",
347: "Prohibitionist",
354: "Silver Republican",
355: "Union",
356: "Union Labor",
370: "Progressive",
380: "Socialist",
402: "Liberal",
522: "American Labor",
537: "Farmer-Labor",
1060: "Silver",
1116: "Conservative Republican",
};
function getParty(id) {
return parties[id];
}
// .classed() accepts a hash of functions. When we give it this hash, it assigns
// one of the classes dem/rep/ind, as well as the class party-n, corresponding
// to a data point's .party val.
var set_party_classes = {
dem: function (d) { return (d[0]||d).party == 100; },
rep: function (d) { return (d[0]||d).party == 200; },
ind: function (d) { var p = (d[0]||d).party; return p != 100 && p != 200; }
};
Object.keys(parties).map(function (p) {
set_party_classes['party-'+p] = function(d){ return (d[0]||d).party == p; };
});
// The hover title that shows up when you mouse over a point.
function getTitle(d) {
return sprintf('%s - %s - %.2f', d.name, getParty(d.party), d.dim1);
}
// Make some variables global to help with debugging.
var data; // original data
var data_icpsr; // data about members, keyed by ICPSR (i.e. unique id)
var data_year; // aggregate data grouped by year
var data_year_long; // three entries per year, one each for rep/dem/ind
var data_party; // aggregate data grouped by political party
var notes; // separate data set, notes to display in relation to specific years
// Take the original data from the TSV, and construct the various different
// views onto it that we'll need in other places.
function restructure_data(data_) {
data = data_;
data_year = d3.nest()
.key(rcps('year'))
.rollup(function (values) {
function dim1s(name) {
return values.filter(function (v) {
return getParty(v.party) == name;
}).map(rcps('dim1')).sort(function (a, b) { return a-b; });
}
var D_dim1s = dim1s('Democrat');
var R_dim1s = dim1s('Republican');
var I_count = values.length - D_dim1s.length - R_dim1s.length;
var I_breakdown = d3.nest()
.key(rcps('party'))
.rollup(function (vs) { return vs.length; })
.entries(values)
.filter(function (v) { return v.key != 100 && v.key != 200; })
.sort(function (a, b) { return b.values - a.values; });
function aggs(vals) {
return { mean: d3.mean(vals),
count: vals.length,
p05: d3.quantile(vals, 0.05),
p25: d3.quantile(vals, 0.25),
p75: d3.quantile(vals, 0.75),
p95: d3.quantile(vals, 0.95) };
}
var R_aggs = aggs(R_dim1s);
var D_aggs = aggs(D_dim1s);
return { year: values[0].year,
R: aggs(R_dim1s),
D: aggs(D_dim1s),
I: { count: I_count, breakdown: I_breakdown },
all: { mean: d3.mean(values, rcps('dim1')),
count: values.length },
polarization: R_aggs.mean - D_aggs.mean,
icpsr_classes: _.pluck(values, 'icpsr_class') };
})
.entries(data);
restructure_nest(data_year);
data_year_long = _.flatten(data_year.map(function (d) {
return [
_.extend({}, d.R, { year: d.year, party: 'Republican' }),
_.extend({}, d.D, { year: d.year, party: 'Democrat' }),
_.extend({}, d.I, { year: d.year, party: 'Independent' }),
];
}), true);
data_icpsr = d3.nest()
.key(rcps('icpsr'))
.rollup(function (values) {
var ret = { icpsr: values[0].icpsr,
ever_independent: false,
has_progress: values.length > 1,
progressions: [],
icpsr_class: values[0].icpsr_class };
for (var i = 0; i < values.length; i++) {
var party = values[i].party;
if (party != 100 && party != 200)
ret.ever_independent = true;
// ret.progressions is an array [[y0, y1], [y1, y2], ...]. The
// ys represent adjacent years in the member's career. So each
// pair represents their career progression from one congress to
// the next that they were elected in.
if (i)
ret.progressions.push([ values[i-1], values[i] ]);
}
ret.ever_independent_and_progress =
ret.ever_independent && ret.has_progress;
return ret;
})
.entries(data);
restructure_nest(data_icpsr);
data.map(function (d) {
d.ever_independent = data_icpsr.key[d.icpsr].ever_independent;
});
data_party = d3.nest()
.key(rcps('party'))
.rollup(function (values) {
return { name: getParty(values[0].party),
extent: d3.extent(values, rcps('year')),
seats: values.length };
})
.entries(data);
restructure_nest(data_party);
}
// List of years to put the white congress break markers on the y axis.
function congress_breaks(years_extent) {
return _.range(years_extent[0], years_extent[1]+1, 2);
}
// Add axes to the SVG.
function add_axes (parent, scale_x, scale_y) {
var x_dom = scale_x.domain();
var axis_x = d3.svg.axis()
.scale(scale_x)
.tickValues(_.range(x_dom[0], x_dom[1] + 0.01, 0.2))
.orient('top');
var axis_y = d3.svg.axis()
.scale(scale_y)
.orient('left')
.tickValues(_.range(1870, 2011, 10))
.tickFormat(d3.format(''));
var axis_y2 = d3.svg.axis()
.scale(scale_y)
.orient('left')
.innerTickSize(-scale_x.range()[1])
.tickValues(congress_breaks(scale_y.domain()))
.tickFormat(function () { return ''; });
parent.append('g').attr('class', 'axis-inner').call(axis_y2);
parent.append('g').attr('class', 'axis').call(axis_y);
parent.append('g').attr('class', 'axis').call(axis_x);
var x_range = scale_x.range();
parent.append('text')
.classed('label', true)
.attr('transform',
sprintf('translate(%f, %f)', (x_range[1]-x_range[0])/2, -30))
.text('DW-NOMINATE score');
var y_range = scale_y.range();
parent.append('text')
.classed('label', true)
.attr('transform', sprintf('translate(%f, %f) rotate(-90)',
-50, (y_range[0]-y_range[1])/2))
.text('Year');
}
// When the infobox is unfixed, it changes with the user's mouse movements. When
// it's fixed, it only changes when the user clicks.
var infobox_fixed = false;
function fix_infobox () {
infobox_fixed = true;
d3.select('#infobox').classed('fixed', true);
}
function unfix_infobox () {
infobox_fixed = false;
d3.select('#infobox').classed('fixed', false);
}
// Place information about a data point in the infobox. If the infobox is fixed,
// this does nothing unless force is true.
function fill_infobox (d, force) {
if (infobox_fixed && !force)
return;
d3.select('#infobox').style('display', 'block');
d3.select('#infobox-year')
.text(sprintf('%d - %d', +d.year - 1, +d.year + 1));
d3.select('#num-democrats').text(d.D.count);
d3.select('#num-republicans').text(d.R.count);
d3.select('#num-independents').text(d.I.count);
d3.select('#polarization').text(d.polarization.toFixed(2));
var ib = d3.select('#independents-breakdown').html('');
d.I.breakdown.map(function (p) {
var party = data_party.key[p.key];
ib.append('dt').append('span')
.classed('party', true)
.attr('title', sprintf('%d - %d: %d seats',
party.extent[0]-1, party.extent[1]+1,
party.seats))
.text(party.name)
.on('mouseover', function () { highlight_party(p.key); })
.on('mouseout', function () { highlight_party(false); });
ib.append('dd').text(p.values);
});
d3.select('#infobox-notes').html(d.notes || '');
}
function render (data_) {
// DW-NOMINATE scores can't be compared between pre- and post- civil war.
data_ = data_.filter(function (d) { return d.year >= 1866; });
restructure_data(data_); // defines global vars data, data_*
// Construct the SVG element, scales, etc.
var margin = { left: 75, right: 30, top: 100, bottom: 30 };
var width = 1100 - margin.left - margin.right;
var height = 1200 - margin.top - margin.bottom;
var svg = d3.select('#main-graph-ctnr')
.attr('width', width + margin.left + margin.right)
.attr('height', height + margin.top + margin.bottom)
.append('g') // In chrome, can't transform() SVG directly
.attr('transform', sprintf('translate(%d, %d)',
margin.left, margin.top));
var scale_x = d3.scale.linear()
.range([0, width])
.domain([-1.2, 1.2]);
var year_extent = d3.extent(data, rcps('year'));
var scale_y = d3.scale.linear()
.range([height, 0])
.domain([year_extent[0]-1, year_extent[1]+1]);
var axes = svg.append('g').attr('class', 'axes');
add_axes(axes, scale_x, scale_y);
// Create layers to add data to.
var main_graph = svg.append('g').attr('id', 'main-graph');
main_graph.append('g').attr('id', 'background');
main_graph.append('g').attr('id', 'year-highlights');
main_graph.append('g').attr('id', 'aggregates');
main_graph.append('g').attr('id', 'progressions');
main_graph.append('g').attr('id', 'points');
main_graph.append('g').attr('id', 'notes-markers');
svg.append('text')
.attr('id', 'title')
.attr('transform', sprintf('translate(%f, %f)',
width/2, -margin.top + 23))
.text('Political Polarization in the U.S. House of Representatives');
var legend = svg.append('g')
.attr('id', 'legend')
.attr('transform', sprintf('translate(%f, %f)',
width - 120, -margin.top + 3));
add_legend(legend);
// line and area are functions used to draw shapes, when applied to data in
// the correct format. They return values suitable for the d attribute of a
// path element.
var line = d3.svg.line()
.x(rcps('dim1', scale_x))
.y(rcps('year_jitter', scale_y))
.defined(function (d) { return d.dim1 !== undefined; });
var area = d3.svg.area()
.y(rcps(0, scale_y))
.x0(rcps(1, scale_x))
.x1(rcps(2, scale_x))
.defined(function (d) { return !isNaN(+d[0] + d[1] + d[2]); });
// Draw the democrat and republican percentile ribbons.
main_graph.select('#background').append('path')
.attr('fill', 'blue')
.attr('fill-opacity', 0.3)
.attr('d', area(data_year.map(function (d) {
return [ d.year, d.D.p25, d.D.p75 ];
})));
main_graph.select('#background').append('path')
.attr('fill', 'blue')
.attr('fill-opacity', 0.3)
.attr('d', area(data_year.map(function (d) {
return [ d.year, d.D.p05, d.D.p95 ];
})));
main_graph.select('#background').append('path')
.attr('fill', 'red')
.attr('fill-opacity', 0.3)
.attr('d', area(data_year.map(function (d) {
return [ d.year, d.R.p25, d.R.p75 ];
})));
main_graph.select('#background').append('path')
.attr('fill', 'red')
.attr('fill-opacity', 0.3)
.attr('d', area(data_year.map(function (d) {
return [ d.year, d.R.p05, d.R.p95 ];
})));
// For representatives who served at least two terms, of which at least one
// was as an independent, draw lines plotting their career progress. Every
// line segment is a separate element, because they might need to be
// different colors.
main_graph.select('#progressions').selectAll('g.progression')
.data(data_icpsr.filter(rcps('ever_independent_and_progress')))
.enter()
.append('g')
.attr('class', rcps('icpsr_class'))
.classed('progression', true)
.on('mouseover', function (d) { highlight_icpsr(d.icpsr); })
.on('mouseout', function (d) { highlight_icpsr(false); })
.each(function (d) {
d3.select(this)
.selectAll('path')
.data(d.progressions)
.enter()
.append('path')
.classed(set_party_classes)
.attr('d', line);
});
// For representatives who ever served as an independent, draw circles for
// each year they were elected.
main_graph.select('#points').selectAll('circle')
.data(data.filter(rcps('ever_independent')))
.enter()
.append('circle')
.attr('class', rcps('icpsr_class'))
.classed(set_party_classes)
.attr({ cx: rcps('dim1', scale_x),
cy: rcps('year_jitter', scale_y),
r: 2 })
.on('mouseover', function (d) { highlight_icpsr(d.icpsr); })
.on('mouseout', function (d) { highlight_icpsr(false); })
.append('title').text(getTitle);
// Draw rects to highlight years when hovered over. CSS makes them invisible
// by default, and transparent white when hovered.
main_graph.select('#year-highlights').selectAll('rect')
.data(data_year)
.enter()
.append('rect')
.attr({ x: 0, y: function (d) { return scale_y(d.year + 1); },
width: width, height: scale_y(0) - scale_y(2),
'fill-opacity': 0.5, fill: 'none' })
.on('mouseover', function (d) {
highlight_year(d.year);
fill_infobox(d);
})
.on('mouseout', function () {
highlight_year(false);
})
.on('click', function (d) {
fill_infobox(d, true);
fix_infobox();
});
// Draw the party means and House mean DW-NOMINATE score.
function draw_aggregate(party) {
var g = main_graph.select('#aggregates').append('g');
g.append('path')
.attr('d', line(data_year.map(function (d) {
return {'dim1': d[party].mean, 'year_jitter': d.year};
})))
.attr('fill', 'none')
.attr('stroke', 'black')
.attr('stroke-width', 2);
}
draw_aggregate('all');
draw_aggregate('D');
draw_aggregate('R');
// Add event handlers for showing and hiding the secondary graph.
d3.select('#show-polarization')
.on('click', secondary_click_handler(render_polarization, data_year));
d3.select('#show-count-ind')
.on('click', secondary_click_handler(render_counts,
data_year_long, 'IDR'));
d3.select('#show-count-rep')
.on('click', secondary_click_handler(render_counts,
data_year_long, 'RDI'));
d3.select('#show-count-dem')
.on('click', secondary_click_handler(render_counts,
data_year_long, 'DRI'));
d3.select('html').on('click', function () {
var ctnr = d3.select('#secondary-graph-ctnr').node();
if (!ctnr.contains(d3.event.target))
hide_secondary();
});
d3.select('#infobox-unfix').on('click', function () {
unfix_infobox();
d3.event.preventDefault();
});
// Get notes data and render it.
d3.json('notes.json', function(error, notes) {
render_notes(notes, data_year, scale_y);
});
}
// Whenever we have a note, draw a black mark on the graph and edit the year
// data to include the note text.
function render_notes(notes_, data, scale_y) {
notes = notes_;
notes.map(function (note) {
data.key[ note.year ].notes = note.text;
});
d3.select('#notes-markers').selectAll('circle')
.data(notes)
.enter()
.append('circle')
.attr({ cx: 5, cy: rcps('year', scale_y), r: 3});
}
// Return an event handler to show the secondary graph, and render it using the
// provided function.
function secondary_click_handler(func, data, arg) {
return function () {
show_secondary();
d3.select('#secondary-graph-ctnr').html('');
func(data, arg);
d3.event.stopPropagation();
d3.event.preventDefault();
}
}
// Render an area graph of counts of members by affiliation. order should have
// the characters R, D and I in some permutation, to specify the stacking order
// bottom-to-top.
function render_counts (data, order) {
var svg = d3.select('#secondary-graph-ctnr')
.classed({counts: true, polarization: false});
var margin = { left: 50, right: 30, top: 30, bottom: 60 };
var width = 800 - margin.left - margin.right;
var height = 600 - margin.top - margin.bottom;
var chart = new dimple.chart(svg, data);
chart.setBounds(margin.left, margin.top, width, height);
var x = chart.addCategoryAxis('x', 'year');
var y = chart.addMeasureAxis('y', 'count');
var s = chart.addSeries('party', dimple.plot.area);
x.title = 'Year';
y.title = 'Number of members';
s.interpolation = 'step';
chart.assignClass('Democrat', 'dem');
chart.assignClass('Republican', 'rep');
chart.assignClass('Independent', 'ind');
if (order) {
var o = [null, null, null];
o[ order.indexOf('I') ] = 'Independent';
o[ order.indexOf('D') ] = 'Democrat';
o[ order.indexOf('R') ] = 'Republican';
s.addOrderRule(o);
}
chart.draw();
// Hacky: when there are no independents, the independent marker completely
// covers the one below it, so remove all those markers. Don't bother if the
// independents are at the bottom anyway.
if (order && order[0] == 'I')
return;
d3.selectAll('circle.dimple-marker').each(function (d) {
if (d.yValue == 0)
d3.select(this).remove();
});
}
// Render a line graph of polarization year-on-year.
function render_polarization (data) {
var svg = d3.select('#secondary-graph-ctnr')
.classed({counts: false, polarization: true});
var margin = { left: 50, right: 30, top: 30, bottom: 60 };
var width = 800 - margin.left - margin.right;
var height = 600 - margin.top - margin.bottom;
var chart = new dimple.chart(svg, data);
chart.setBounds(margin.left, margin.top, width, height);
var x = chart.addCategoryAxis('x', 'year');
var y = chart.addMeasureAxis('y', 'polarization');
chart.addSeries(null, dimple.plot.line);
x.title = 'Year';
y.title = 'Polarization';
chart.draw();
}
// Show/hide the secondary graph.
function show_secondary () {
d3.select('#secondary-graph-ctnr').style('display', 'block');
}
function hide_secondary () {
d3.select('#secondary-graph-ctnr').style('display', 'none');
}
// Highlight a specific member's career progression, according to their ICPSR.
function highlight_icpsr (icpsr) {
d3.selectAll('.highlight').classed('highlight', false);
if (!icpsr)
return;
d3.selectAll('.icpsr-' + icpsr).classed('highlight', true);
}
// Highlight the career progression of every member who served in a given year.
function highlight_year(year) {
d3.selectAll('.highlight').classed('highlight', false);
if (!year)
return;
var classes = data_year.key[year].icpsr_classes;
classes.map(function (c) {
d3.selectAll('.' + c).classed('highlight', true);
});
}
// Highlight the members who served in a specific party (not just
// "independent"), only in the years they were actually affiliated with that
// party. The other highlight_ functions don't need to touch radius, because
// they're implemented with CSS (by adding a stroke the same color as the fill).
// This highlight can't be done in CSS.
function highlight_party (party) {
d3.selectAll('.highlight-p')
.classed('highlight-p', false)
.attr('r', 2);
if (!party)
return;
d3.selectAll('.party-' + party)
.classed('highlight-p', true)
.attr('r', 3);
}
// Appropriately format rows from the raw TSV data.
function transform (row) {
row.dim1 = +row.dim1;
row.year = +row.year;
row.icpsr_class = 'icpsr-' + row.icpsr;
// Add some vertical jitter to reduce overplotting.
row.year_jitter = row.year + Math.random() - 0.5;
return row;
}
d3.tsv('house.tsv', transform, render);
function add_legend(parent) {
// All rects in the legend are the same, except for their x position and
// their class. Use this to generate their attributes.
function rect_attrs(x, cls) {
return { x: x, y: 0, width: 15, height: 15, class: cls };
}
// Add 5-95 percentile legend.
var percentiles_0595 = parent.append('g');
percentiles_0595.append('rect').attr(rect_attrs(0, 'dem'));
percentiles_0595.append('rect').attr(rect_attrs(20, 'rep'));
percentiles_0595.append('text')
.text('5-95 percentile')
.attr({ x: 40, y: 9 });
// Add 25-75 percentile legend. Just draw two of the 5-95 ones overlaid,
// because that's what it is on the graph itself.
var percentiles_2575 = parent.append('g')
.attr('transform', 'translate(0, 20)');
percentiles_2575.append('rect').attr(rect_attrs(0, 'dem'));
percentiles_2575.append('rect').attr(rect_attrs(0, 'dem'));
percentiles_2575.append('rect').attr(rect_attrs(20, 'rep'));
percentiles_2575.append('rect').attr(rect_attrs(20, 'rep'));
percentiles_2575.append('text')
.text('25-75 percentile')
.attr({ x: 40, y: 9 });
// Add party/house means legend.
var means = parent.append('g')
.attr('transform', 'translate(0, 40)');
means.append('line')
.attr({ x1: 5, x2: 15, y1: 0, y2: 15,
stroke: 'black', 'stroke-width': 2 });
means.append('text')
.text('Party/House mean')
.attr({ x: 25, y: 9 });
}
This file has been truncated, but you can view the full file.
1 9062 198 CONNECT 5000 STURGES 0.648 0.330 0.0 0.0 0.0 -26.87624 80 12 0.715
1 9706 198 CONNECT 5000 WADSWORTH 0.759 0.136 0.0 0.0 0.0 -18.20466 86 4 0.809
1 8457 198 CONNECT 5000 SHERMAN 0.715 0.203 0.0 0.0 0.0 -39.45672 107 18 0.692
1 4766 198 CONNECT 5000 HUNTINGTON 0.747 0.125 0.0 0.0 0.0 -28.30918 84 12 0.714
1 9489 198 CONNECT 5000 TRUMBULL 0.778 0.069 0.0 0.0 0.0 -31.09822 106 11 0.746
1 9676 11 1 DELAWAR 5000 VINING 0.450 -0.333 0.0 0.0 0.0 -30.87977 94 15 0.720
1 4854 44 1 GEORGIA 4000 JACKSON -0.461 0.585 0.0 0.0 0.0 -21.07589 98 11 0.806
1 379 44 2 GEORGIA 4000 BALDWIN 0.052 -0.129 0.0 0.0 0.0 -32.69657 103 15 0.728
1 6071 44 3 GEORGIA 4000 MATHEWS -0.379 0.048 0.0 0.0 0.0 -11.54729 99 3 0.890
1 8983 52 1 MARYLAN 4000 STONE 0.107 0.229 0.0 0.0 0.0 -37.85437 97 22 0.677
1 8363 52 2 MARYLAN 4000 SENEY 0.311 0.309 0.0 0.0 0.0 -55.79742 106 29 0.591
1 2010 52 3 MARYLAN 4000 CONTEE -0.022 -0.264 0.0 0.0 0.0 -21.26855 92 11 0.794
1 8693 52 4 MARYLAN 4000 SMITH W. 0.480 0.255 0.0 0.0 0.0 -54.82164 103 29 0.587
1 3430 52 5 MARYLAN 5000 GALE 0.303 -0.953 0.0 0.0 0.0 -26.07953 94 14 0.758
1 1538 52 6 MARYLAN 5000 CARROLL 0.153 -0.796 0.0 0.0 0.0 -24.47979 96 9 0.775
1 154 3 1 MASSACH 5000 AMES 0.884 0.109 0.0 0.0 0.0 -27.35509 103 11 0.767
1 3674 3 2 MASSACH 5000 GOODHUE 0.890 -0.066 0.0 0.0 0.0 -31.74106 95 20 0.716
1 3541 3 3 MASSACH 4000 GERRY 0.686 0.727 0.0 0.0 0.0 -37.07116 100 20 0.690
1 8344 3 4 MASSACH 5000 SEDGWICK 0.870 0.255 0.0 0.0 0.0 -17.86754 74 4 0.785
1 7224 3 5 MASSACH 5000 PARTRIDGE 0.833 0.538 0.0 0.0 0.0 -31.69459 96 16 0.719
1 9268 3 6 MASSACH 5000 THACHER 0.822 0.461 0.0 0.0 0.0 -38.33833 103 19 0.689
1 5611 3 7 MASSACH 5000 LEONARD 0.878 0.275 0.0 0.0 0.0 -26.57026 83 15 0.726
1 3857 3 8 MASSACH 4000 GROUT 0.431 0.902 0.0 0.0 0.0 -36.93435 100 19 0.691
1 3610 498 NEW HAM 5000 GILMAN 0.486 -0.305 0.0 0.0 0.0 -59.08680 105 29 0.570
1 3299 498 NEW HAM 5000 FOSTER 0.847 0.485 0.0 0.0 0.0 -28.83252 101 16 0.752
1 5713 498 NEW HAM 4000 LIVERMORE 0.706 0.583 0.0 0.0 0.0 -51.97416 109 32 0.621
1 878 1298 NEW JER 5000 BOUDINOT 0.654 -0.002 0.0 0.0 0.0 -62.76849 97 34 0.524
1 1387 1298 NEW JER 5000 CADWALADER 0.667 -0.745 0.0 0.0 0.0 -27.62413 106 15 0.771
1 8278 1298 NEW JER 5000 SCHUREMAN 0.663 0.239 0.0 0.0 0.0 -37.09978 86 21 0.650
1 8544 1298 NEW JER 5000 SINNICKSON 0.651 -0.409 0.0 0.0 0.0 -51.24582 103 29 0.608
1 3237 13 1 NEW YOR 4000 FLOYD 0.514 0.794 0.0 0.0 0.0 -33.19650 105 18 0.729
1 5498 13 2 NEW YOR 5000 LAURANCE 0.690 0.249 0.0 0.0 0.0 -39.94890 106 20 0.686
1 649 13 3 NEW YOR 5000 BENSON 0.822 0.374 0.0 0.0 0.0 -30.22378 104 16 0.748
1 4193 13 4 NEW YOR 4000 HATHORN 0.231 0.973 0.0 0.0 0.0 -37.36005 97 20 0.680
1 8511 13 5 NEW YOR 5000 SILVESTER 0.635 0.371 0.0 0.0 0.0 -38.01908 108 17 0.703
1 9629 13 6 NEW YOR 4000 VANRENSSELA 0.420 0.907 0.0 0.0 0.0 -29.58990 106 14 0.756
1 259 47 1 NORTH C 4000 ASHE -0.546 -0.762 0.0 0.0 0.0 -16.29563 68 7 0.787
1 10193 47 2 NORTH C 4000 WILLIAMSON 0.126 -0.137 0.0 0.0 0.0 -24.91597 67 10 0.689
1 800 47 3 NORTH C 4000 BLOODWORTH -0.210 0.978 0.0 0.0 0.0 -12.82772 66 4 0.823
1 8869 47 4 NORTH C 5000 STEELE 0.276 -0.314 0.0 0.0 0.0 -27.71360 64 19 0.649
1 8374 47 5 NORTH C 5000 SEVIER -0.071 0.182 0.0 0.0 0.0 -32.36610 44 10 0.479
1 4390 1498 PENNSYL 4000 HIESTER 0.240 -0.140 0.0 0.0 0.0 -40.70320 94 28 0.649
1 6784 1498 PENNSYL 5000 MUHLENGBRG 0.185 -0.337 0.0 0.0 0.0 -46.80498 105 24 0.640
1 4156 1498 PENNSYL 5000 HARTLEY 0.469 -0.141 0.0 0.0 0.0 -38.23565 100 14 0.682
1 8312 1498 PENNSYL 5000 SCOTT 0.380 -0.518 0.0 0.0 0.0 -39.11363 106 20 0.691
1 10405 1498 PENNSYL 5000 WYNKOOP 0.656 -0.755 0.0 0.0 0.0 -16.64883 100 4 0.847
1 1859 1498 PENNSYL 5000 CLYMER 0.614 -0.789 0.0 0.0 0.0 -14.58570 96 5 0.859
1 3196 1498 PENNSYL 5000 FITZSIMONS 0.540 -0.203 0.0 0.0 0.0 -27.68835 107 4 0.772
1 8702 48 1 SOUTH C 5000 SMITH W.L. 0.641 0.281 0.0 0.0 0.0 -72.67693 104 38 0.497
1 1260 48 2 SOUTH C 4000 BURKE 0.215 0.586 0.0 0.0 0.0 -49.96458 101 22 0.610
1 4695 48 3 SOUTH C 5000 HUGER 0.646 0.763 0.0 0.0 0.0 -12.35777 32 6 0.680
1 9088 48 4 SOUTH C 4000 SUMTER -0.714 0.482 0.0 0.0 0.0 -22.36394 92 8 0.784
1 9501 48 5 SOUTH C 4000 TUCKER 0.289 0.535 0.0 0.0 0.0 -51.53628 104 29 0.609
1 10006 40 1 VIRGINI 5000 WHITE 0.203 0.065 0.0 0.0 0.0 -31.78233 91 14 0.705
1 1118 40 2 VIRGINI 4000 BROWN -0.097 -0.506 0.0 0.0 0.0 -17.93131 98 8 0.833
1 6625 40 3 VIRGINI 4000 MOORE -0.090 -0.374 0.0 0.0 0.0 -21.18295 102 7 0.812
1 5564 40 4 VIRGINI 5000 LEE 0.303 -0.038 0.0 0.0 0.0 -41.92163 101 23 0.660
1 5903 40 5 VIRGINI 4000 MADISON 0.033 -0.423 0.0 0.0 0.0 -27.34512 102 11 0.765
1 1936 40 6 VIRGINI 4000 COLES -0.260 0.235 0.0 0.0 0.0 -14.07770 83 6 0.844
1 7156 40 7 VIRGINI 4000 PAGE 0.074 0.067 0.0 0.0 0.0 -22.52697 82 11 0.760
1 7199 40 8 VIRGINI 4000 PARKER 0.088 0.130 0.0 0.0 0.0 -31.31138 98 12 0.727
1 786 40 9 VIRGINI 4000 BLAND -0.862 0.507 0.0 0.0 0.0 -8.89097 28 5 0.728
1 3825 4010 VIRGINI 5000 GRIFFIN 0.251 -0.077 0.0 0.0 0.0 -35.28434 95 19 0.690
2 4433 198 CONNECT 5000 HILLHOUSE 0.626 -0.043 0.0 0.0 0.0 -37.23514 89 18 0.658
2 5543 198 CONNECT 5000 LEARNED 0.841 0.328 0.0 0.0 0.0 -14.88952 90 3 0.848
2 9062 198 CONNECT 5000 STURGES 0.648 0.330 0.0 0.0 0.0 -68.22063 98 34 0.499
2 9706 198 CONNECT 5000 WADSWORTH 0.759 0.136 0.0 0.0 0.0 -15.95766 77 6 0.813
2 9676 11 1 DELAWAR 5000 VINING 0.450 -0.333 0.0 0.0 0.0 -22.98034 39 12 0.555
2 9885 44 1 GEORGIA 4000 WAYNE 0.248 0.969 0.0 0.0 0.0 -7.93063 29 3 0.761
2 379 44 2 GEORGIA 4000 BALDWIN 0.060 -0.140 0.0 0.0 0.0 -28.92876 100 13 0.749
2 10202 44 3 GEORGIA 4000 WILLIS -0.040 0.843 0.0 0.0 0.0 -26.04754 87 13 0.741
2 3798 51 1 KENTUCK 4000 GREENUP -0.079 -0.292 0.0 0.0 0.0 -17.74782 35 8 0.602
2 7084 51 2 KENTUCK 4000 ORR 0.082 -0.350 0.0 0.0 0.0 -6.04703 32 1 0.828
2 5224 52 1 MARYLAN 5000 KEY 0.334 0.244 0.0 0.0 0.0 -35.55610 81 22 0.645
2 8363 52 2 MARYLAN 4000 SENEY 0.311 0.309 0.0 0.0 0.0 -29.19346 58 14 0.605
2 6418 52 3 MARYLAN 4000 MERCER -0.409 0.201 0.0 0.0 0.0 -10.40708 59 5 0.838
2 8896 52 4 MARYLAN 4000 STERETT 0.509 0.853 0.0 0.0 0.0 -19.31298 79 8 0.783
2 6830 52 5 MARYLAN 5000 MURRAY 0.466 0.149 0.0 0.0 0.0 -52.15109 89 28 0.557
2 154 3 1 MASSACH 5000 AMES 0.884 0.109 0.0 0.0 0.0 -15.98617 92 8 0.840
2 3674 3 2 MASSACH 5000 GOODHUE 0.890 -0.066 0.0 0.0 0.0 -20.36527 96 10 0.809
2 3541 3 3 MASSACH 4000 GERRY 0.686 0.727 0.0 0.0 0.0 -38.54721 79 20 0.614
2 8344 3 4 MASSACH 5000 SEDGWICK 0.870 0.255 0.0 0.0 0.0 -12.50369 75 4 0.846
2 886 3 5 MASSACH 5000 BOURNE 0.954 0.164 0.0 0.0 0.0 -17.32468 98 6 0.838
2 5611 3 6 MASSACH 5000 LEONARD 0.878 0.275 0.0 0.0 0.0 -9.11240 39 5 0.792
2 9808 3 7 MASSACH 5000 WARD 0.925 -0.379 0.0 0.0 0.0 -23.40014 87 12 0.764
2 9268 3 8 MASSACH 5000 THACHER 0.843 0.463 0.0 0.0 0.0 -43.26923 96 24 0.637
2 5713 498 NEW HAM 5000 LIVERMORE 0.706 0.583 0.0 0.0 0.0 -57.91862 100 27 0.560
2 8644 498 NEW HAM 5000 SMITH J. 0.487 -0.313 0.0 0.0 0.0 -47.45479 85 30 0.572
2 3610 498 NEW HAM 5000 GILMAN 0.486 -0.305 0.0 0.0 0.0 -44.04788 96 20 0.632
2 5303 1298 NEW JER 5000 KITCHELL 0.250 -0.895 0.0 0.0 0.0 -37.68584 97 20 0.678
2 2440 1298 NEW JER 5000 DAYTON 0.558 -0.554 0.0 0.0 0.0 -22.83612 61 11 0.688
2 878 1298 NEW JER 5000 BOUDINOT 0.654 -0.002 0.0 0.0 0.0 -34.42166 89 17 0.679
2 1750 1298 NEW JER 5000 CLARK 0.305 -0.746 0.0 0.0 0.0 -35.54831 89 20 0.671
2 9463 13 1 NEW YOR 4000 TREDWELL -0.101 -0.995 0.0 0.0 0.0 -27.38025 90 15 0.738
2 5498 13 2 NEW YOR 5000 LAURANCE 0.690 0.249 0.0 0.0 0.0 -30.81858 82 16 0.687
2 649 13 3 NEW YOR 5000 BENSON 0.822 0.374 0.0 0.0 0.0 -28.08649 96 14 0.746
2 8272 13 4 NEW YOR 4000 SCHOONMAKER 0.031 -1.000 0.0 0.0 0.0 -30.76725 77 14 0.671
2 8511 13 5 NEW YOR 5000 SILVESTER 0.635 0.371 0.0 0.0 0.0 -40.13914 88 16 0.634
2 3695 13 6 NEW YOR 5000 GORDON 0.574 0.094 0.0 0.0 0.0 -34.19562 80 16 0.652
2 8869 47 1 NORTH C 5000 STEELE 0.276 -0.314 0.0 0.0 0.0 -51.43906 87 31 0.554
2 5895 47 2 NORTH C 4000 MACON -0.035 -0.732 0.0 0.0 0.0 -26.49220 95 9 0.757
2 259 47 3 NORTH C 4000 ASHE -0.546 -0.762 0.0 0.0 0.0 -16.84909 95 8 0.837
2 10193 47 4 NORTH C 4000 WILLIAMSON 0.126 -0.137 0.0 0.0 0.0 -31.93766 84 13 0.684
2 3859 47 5 NORTH C 5000 GROVE 0.109 -0.150 0.0 0.0 0.0 -35.70038 91 20 0.675
2 3196 14 1 PENNSYL 5000 FITZSIMONS 0.540 -0.203 0.0 0.0 0.0 -47.63362 98 23 0.615
2 6781 14 2 PENNSYL 4000 MUHLENBERG 0.329 0.270 0.0 0.0 0.0 -31.45301 89 13 0.702
2 4871 14 3 PENNSYL 5000 JACOBS 0.333 -0.498 0.0 0.0 0.0 -33.89287 67 15 0.603
2 4390 14 4 PENNSYL 4000 HIESTER 0.195 -0.141 0.0 0.0 0.0 -32.37142 74 19 0.646
2 5310 14 5 PENNSYL 5000 KITTERA 0.546 0.085 0.0 0.0 0.0 -25.45678 69 9 0.691
2 3805 14 6 PENNSYL 4000 GREGG 0.232 -0.320 0.0 0.0 0.0 -38.97535 80 19 0.614
2 4156 14 7 PENNSYL 5000 HARTLEY 0.488 -0.178 0.0 0.0 0.0 -37.47621 84 15 0.640
2 3147 14 8 PENNSYL 4000 FINDLEY -0.060 0.498 0.0 0.0 0.0 -25.01661 83 9 0.740
2 884 5 1 RHODE I 5000 BOURN 0.749 -0.001 0.0 0.0 0.0 -27.59469 99 13 0.757
2 8702 48 1 SOUTH C 5000 SMITH W.L. 0.588 0.315 0.0 0.0 0.0 -32.60893 95 17 0.709
2 456 48 2 SOUTH C 5000 BARNWELL 0.741 0.671 0.0 0.0 0.0 -30.07315 99 14 0.738
2 4695 48 3 SOUTH C 5000 HUGER 0.646 0.763 0.0 0.0 0.0 -21.84903 78 7 0.756
2 9088 48 4 SOUTH C 4000 SUMTER -0.714 0.482 0.0 0.0 0.0 -35.33348 89 17 0.672
2 9501 48 5 SOUTH C 4000 TUCKER 0.289 0.535 0.0 0.0 0.0 -44.41305 92 25 0.617
2 8639 6 1 VERMONT 4000 SMITH I. 0.150 -0.989 0.0 0.0 0.0 -41.09962 83 20 0.609
2 6932 6 2 VERMONT 4000 NILES 0.267 -0.964 0.0 0.0 0.0 -32.36426 97 14 0.716
2 10006 40 1 VIRGINI 5000 WHITE 0.203 0.065 0.0 0.0 0.0 -35.22724 93 16 0.685
2 1118 40 2 VIRGINI 4000 BROWN -0.097 -0.506 0.0 0.0 0.0 -14.39271 52 6 0.758
2 6625 40 3 VIRGINI 4000 MOORE -0.081 -0.371 0.0 0.0 0.0 -19.83400 89 8 0.800
2 5564 40 4 VIRGINI 5000 LEE 0.303 -0.038 0.0 0.0 0.0 -37.27102 84 13 0.642
2 5903 40 5 VIRGINI 4000 MADISON 0.033 -0.423 0.0 0.0 0.0 -26.82887 92 12 0.747
2 9658 40 6 VIRGINI 4000 VENABLE -0.118 0.055 0.0 0.0 0.0 -15.46206 87 6 0.837
2 7156 40 7 VIRGINI 4000 PAGE 0.074 0.067 0.0 0.0 0.0 -29.26633 87 14 0.714
2 7199 40 8 VIRGINI 4000 PARKER 0.172 0.089 0.0 0.0 0.0 -32.47131 90 13 0.697
2 3582 40 9 VIRGINI 4000 GILES -0.283 -0.127 0.0 0.0 0.0 -9.13394 89 2 0.902
2 3825 4010 VIRGINI 4000 GRIFFIN 0.251 -0.077 0.0 0.0 0.0 -22.13485 67 5 0.719
3 9489 198 CONNECT 5000 TRUMBELL 0.778 0.069 0.0 0.0 0.0 -9.06484 67 4 0.873
3 5543 198 CONNECT 5000 LEARNED 0.841 0.328 0.0 0.0 0.0 -7.20300 64 2 0.894
3 1914 198 CONNECT 5000 COIT 0.561 0.828 0.0 0.0 0.0 -33.64972 69 14 0.614
3 4433 198 CONNECT 5000 HILLHOUSE 0.626 -0.043 0.0 0.0 0.0 -22.22856 65 10 0.710
3 9452 198 CONNECT 5000 TRACY 0.884 0.091 0.0 0.0 0.0 -13.24123 68 5 0.823
3 9127 198 CONNECT 5000 SWIFT 0.523 0.757 0.0 0.0 0.0 -23.53763 68 9 0.707
3 9706 198 CONNECT 5000 WADSWORTH J 0.759 0.136 0.0 0.0 0.0 -7.56749 51 3 0.862
3 5494 11 1 DELAWAR 5000 LATIMER 0.400 0.917 0.0 0.0 0.0 -15.87558 48 5 0.718
3 379 4498 GEORGIA 4000 BALDWIN 0.068 -0.152 0.0 0.0 0.0 -28.23587 61 19 0.629
3 1518 4498 GEORGIA 4000 CARNES 0.116 -0.462 0.0 0.0 0.0 -19.39209 56 13 0.707
3 3798 51 1 KENTUCK 4000 GREENUP -0.079 -0.292 0.0 0.0 0.0 -9.89176 41 5 0.786
3 7084 51 2 KENTUCK 4000 ORR 0.082 -0.350 0.0 0.0 0.0 -19.23770 57 7 0.714
3 2512 52 1 MARYLAN 5000 DENT 0.380 -0.892 0.0 0.0 0.0 -33.64263 65 23 0.596
3 8811 52 4 MARYLAN 4000 SPRIGG 0.192 -0.360 0.0 0.0 0.0 -19.07029 36 10 0.589
3 8678 52 5 MARYLAN 4000 SMITH S. 0.421 -0.488 0.0 0.0 0.0 -8.51636 25 4 0.711
3 1716 52 6 MARYLAN 4000 CHRISTIE 0.082 -0.401 0.0 0.0 0.0 -22.86352 58 13 0.674
3 4441 52 7 MARYLAN 5000 HINDMAN 0.856 -0.225 0.0 0.0 0.0 -15.30430 60 7 0.775
3 6830 52 8 MARYLAN 5000 MURRAY 0.466 0.149 0.0 0.0 0.0 -16.34710 66 6 0.781
3 3674 3 1 MASSACH 5000 GOODHUE 0.890 -0.066 0.0 0.0 0.0 -11.43561 57 6 0.818
3 4546 3 1 MASSACH 4000 HOLTEN 0.557 0.054 0.0 0.0 0.0 -5.45095 28 2 0.823
3 154 3 1 MASSACH 5000 AMES 0.884 0.109 0.0 0.0 0.0 -7.97036 68 2 0.889
3 2550 3 1 MASSACH 5000 DEXTER 0.579 0.123 0.0 0.0 0.0 -13.77574 46 4 0.741
3 5852 3 2 MASSACH 4000 LYMAN -0.011 -0.960 0.0 0.0 0.0 -31.79503 66 12 0.618
3 9808 3 2 MASSACH 5000 WARD 0.925 -0.379 0.0 0.0 0.0 -11.90060 47 5 0.776
3 3303 3 2 MASSACH 5000 FOSTER 0.813 0.495 0.0 0.0 0.0 -7.00655 66 2 0.899
3 8344 3 2 MASSACH 5000 SEDGWICK 0.870 0.255 0.0 0.0 0.0 -4.34100 61 0 0.931
3 886 3 3 MASSACH 5000 BOURNE 0.954 0.164 0.0 0.0 0.0 -2.55003 32 1 0.923
3 1906 3 3 MASSACH 5000 COFFIN 0.821 0.344 0.0 0.0 0.0 -6.83362 69 1 0.906
3 2451 3 4 MASSACH 4000 DEARBORN 0.238 -0.205 0.0 0.0 0.0 -24.91071 60 10 0.660
3 9707 3 4 MASSACH 5000 WADSWORTH P 0.801 0.210 0.0 0.0 0.0 -7.81407 64 2 0.885
3 9268 3 4 MASSACH 5000 THACHER 0.865 0.465 0.0 0.0 0.0 -10.31900 67 3 0.857
3 1865 399 MASSACH 5000 COBB 0.783 0.363 0.0 0.0 0.0 -5.90693 67 2 0.916
3 3610 498 NEW HAM 5000 GILMAN 0.486 -0.305 0.0 0.0 0.0 -35.09373 63 17 0.573
3 10272 498 NEW HAM 5000 WINGATE 0.175 0.985 0.0 0.0 0.0 -17.59181 31 12 0.567
3 8644 498 NEW HAM 5000 SMITH J. 0.487 -0.313 0.0 0.0 0.0 -27.55676 59 15 0.627
3 8448 498 NEW HAM 4000 SHERBURNE 0.261 0.126 0.0 0.0 0.0 -26.29331 49 14 0.585
3 554 1298 NEW JER 5000 BEATTY 0.554 -0.833 0.0 0.0 0.0 -24.57328 63 14 0.677
3 878 1298 NEW JER 5000 BOUDINOT 0.654 -0.002 0.0 0.0 0.0 -25.17400 53 12 0.622
3 1387 1298 NEW JER 5000 CADWALADER 0.667 -0.745 0.0 0.0 0.0 -12.85104 51 5 0.777
3 2440 1298 NEW JER 5000 DAYTON 0.558 -0.554 0.0 0.0 0.0 -15.29887 46 7 0.717
3 1750 1298 NEW JER 5000 CLARK 0.305 -0.746 0.0 0.0 0.0 -20.06982 43 9 0.627
3 9881 13 1 NEW YOR 5000 WATTS 0.826 0.564 0.0 0.0 0.0 -9.37923 62 5 0.860
3 9463 13 2 NEW YOR 4000 TREDWELL -0.101 -0.995 0.0 0.0 0.0 -8.68695 55 3 0.854
3 9602 13 3 NEW YOR 4000 VANCORTLAND 0.035 -0.215 0.0 0.0 0.0 -24.34119 59 17 0.662
3 9615 13 4 NEW YOR 5000 VANGAASBECK 0.568 0.823 0.0 0.0 0.0 -16.18053 63 10 0.773
3 351 13 5 NEW YOR 4000 BAILEY -0.068 0.287 0.0 0.0 0.0 -18.62659 68 8 0.760
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7 3324 51 2 KENTUCK 200 FOWLER 0.007 0.412 0.0 0.0 0.0 -25.66361 92 15 0.757
7 1455 52 1 MARYLAN 1 CAMPBELL 0.563 -0.399 0.0 0.0 0.0 -20.25808 95 11 0.808
7 911 52 2 MARYLAN 200 BOWIE 0.110 -0.521 0.0 0.0 0.0 -12.06545 27 8 0.640
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7 4390 52 4 MARYLAN 200 HIESTER D. 0.060 -0.146 0.0 0.0 0.0 -31.35315 90 13 0.706
7 8678 52 5 MARYLAN 200 SMITH S. 0.146 -0.011 0.0 0.0 0.0 -38.56994 111 20 0.706
7 222 52 6 MARYLAN 200 ARCHER 0.018 -0.126 0.0 0.0 0.0 -42.94628 127 18 0.713
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7 6089 3 3 MASSACH 1 MATTOON 0.550 0.835 0.0 0.0 0.0 -13.27641 79 7 0.845
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7 10169 3 5 MASSACH 1 WILLIAMS L. 0.764 0.646 0.0 0.0 0.0 -5.21078 114 0 0.955
7 8662 3 6 MASSACH 200 SMITH J. 0.036 0.999 0.0 0.0 0.0 -53.03656 112 27 0.623
7 733 3 7 MASSACH 200 BISHOP -0.057 0.748 0.0 0.0 0.0 -31.53005 108 10 0.747
7 3001 3 8 MASSACH 200 EUSTIS 0.191 0.160 0.0 0.0 0.0 -45.71635 113 22 0.667
7 9650 3 9 MASSACH 200 VARNUM -0.071 1.004 0.0 0.0 0.0 -43.78432 106 21 0.662
7 7764 310 MASSACH 1 READ 0.638 0.725 0.0 0.0 0.0 -7.63686 130 1 0.943
7 2287 311 MASSACH 1 CUTLER 0.803 0.383 0.0 0.0 0.0 -11.05665 115 3 0.908
7 9271 312 MASSACH 1 THATCHER 0.657 0.452 0.0 0.0 0.0 -6.71289 33 3 0.816
7 9707 313 MASSACH 1 WADSWORTH 0.670 0.040 0.0 0.0 0.0 -20.57264 107 11 0.825
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7 9567 498 NEW HAM 1 UPHAM 0.840 0.543 0.0 0.0 0.0 -13.54724 108 5 0.882
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7 8748 1298 NEW JER 200 SOUTHARD -0.077 0.621 0.0 0.0 0.0 -43.07251 113 21 0.683
7 1974 1298 NEW JER 200 CONDIT -0.132 0.970 0.0 0.0 0.0 -33.87340 132 16 0.774
7 2942 1298 NEW JER 200 ELMER 0.061 0.678 0.0 0.0 0.0 -42.81438 119 24 0.698
7 4293 1298 NEW JER 200 HELMS 0.057 0.508 0.0 0.0 0.0 -39.61323 119 21 0.717
7 8647 13 1 NEW YOR 200 SMITH J. 0.105 -0.082 0.0 0.0 0.0 -42.42767 122 20 0.706
7 6577 13 2 NEW YOR 200 MITCHILL 0.079 0.351 0.0 0.0 0.0 -42.84544 123 21 0.706
7 9602 13 3 NEW YOR 200 VANCORTLAND 0.160 -0.156 0.0 0.0 0.0 -34.64797 109 15 0.728
7 2941 13 4 NEW YOR 200 ELMENDORF -0.091 -0.354 0.0 0.0 0.0 -22.55101 129 9 0.840
7 351 13 5 NEW YOR 200 BAILEY -0.068 0.287 0.0 0.0 0.0 -24.21967 116 11 0.812
7 9625 13 6 NEW YOR 200 VANNESS 0.160 -0.961 0.0 0.0 0.0 -15.20192 78 6 0.823
7 9290 13 7 NEW YOR 200 THOMAS -0.045 0.817 0.0 0.0 0.0 -45.38614 111 20 0.664
7 9630 13 8 NEW YOR 1 VANRENSSELA 0.772 -0.190 0.0 0.0 0.0 -7.36392 83 4 0.915
7 9735 13 9 NEW YOR 1 WALKER 0.847 -0.531 0.0 0.0 0.0 -9.22707 41 5 0.798
7 6715 1310 NEW YOR 1 MORRIS T. 0.665 -0.747 0.0 0.0 0.0 -19.41131 105 8 0.831
7 4519 47 1 NORTH C 200 HOLLAND -0.228 0.424 0.0 0.0 0.0 -13.07196 115 5 0.893
7 4303 47 2 NORTH C 1 HENDERSON 0.587 -0.223 0.0 0.0 0.0 -15.42232 113 6 0.872
7 10176 47 3 NORTH C 200 WILLIAMS R. 0.117 -0.901 0.0 0.0 0.0 -27.65876 91 14 0.738
7 8837 47 4 NORTH C 200 STANFORD 0.047 -0.835 0.0 0.0 0.0 -28.65919 135 15 0.809
7 4426 47 6 NORTH C 1 HILL 0.597 -0.554 0.0 0.0 0.0 -10.72042 75 2 0.867
7 3859 47 7 NORTH C 1 GROVE 0.684 0.209 0.0 0.0 0.0 -13.26818 80 7 0.847
7 4946 47 8 NORTH C 200 JOHNSON 0.158 -0.987 0.0 0.0 0.0 -10.95038 44 6 0.780
7 10407 47 8 NORTH C 200 WYNNS 0.003 0.035 0.0 0.0 0.0 -15.97116 43 5 0.690
7 148 47 9 NORTH C 200 ALSTON 0.032 0.536 0.0 0.0 0.0 -48.64912 126 28 0.680
7 8842 4710 NORTH C 1 STANLY 0.515 -0.373 0.0 0.0 0.0 -23.27759 120 6 0.824
7 5066 14 1 PENNSYL 200 JONES 0.104 -0.216 0.0 0.0 0.0 -15.91176 58 8 0.760
7 5594 14 2 PENNSYL 200 LEIB -0.165 -0.234 0.0 0.0 0.0 -28.24742 123 11 0.795
7 4298 14 3 PENNSYL 1 HEMPHILL 0.640 0.277 0.0 0.0 0.0 -11.46873 83 7 0.871
7 9621 14 4 PENNSYL 200 VANHORNE -0.104 0.023 0.0 0.0 0.0 -24.70453 113 12 0.804
7 1132 14 4 PENNSYL 200 BROWN -0.201 0.263 0.0 0.0 0.0 -28.09751 130 15 0.806
7 4394 14 5 PENNSYL 200 HIESTER J. -0.009 -0.050 0.0 0.0 0.0 -24.80295 115 14 0.806
7 4037 14 6 PENNSYL 200 HANNA 0.103 -0.041 0.0 0.0 0.0 -36.10080 105 16 0.709
7 877 14 7 PENNSYL 1 BOUDE 0.722 -0.449 0.0 0.0 0.0 -16.70136 91 8 0.832
7 8938 14 8 PENNSYL 200 STEWART -0.119 0.128 0.0 0.0 0.0 -33.84464 109 15 0.733
7 3805 14 9 PENNSYL 200 GREGG -0.025 0.184 0.0 0.0 0.0 -26.76335 94 13 0.752
7 10352 1410 PENNSYL 1 WOODS 0.710 -0.260 0.0 0.0 0.0 -12.62122 93 7 0.873
7 8596 1411 PENNSYL 200 SMILIE -0.137 0.388 0.0 0.0 0.0 -32.66642 135 11 0.785
7 4502 1412 PENNSYL 200 HOGE 0.263 -0.965 0.0 0.0 0.0 -44.16469 96 23 0.631
7 8845 598 RHODE I 200 STANTON -0.097 0.698 0.0 0.0 0.0 -29.15397 133 13 0.803
7 9385 598 RHODE I 200 TILLINGHAS 0.431 0.808 0.0 0.0 0.0 -25.80844 109 13 0.789
7 5814 48 1 SOUTH C 1 LOWNDES 0.575 0.298 0.0 0.0 0.0 -13.21370 103 4 0.880
7 8153 48 2 SOUTH C 1 RUTLEDGE 0.747 0.045 0.0 0.0 0.0 -7.02301 65 3 0.898
7 4694 48 3 SOUTH C 1 HUGER 0.509 -0.129 0.0 0.0 0.0 -24.35561 119 10 0.815
7 10275 48 4 SOUTH C 200 WINN -0.045 0.176 0.0 0.0 0.0 -8.82994 25 4 0.702
7 1354 48 5 SOUTH C 200 BUTLER -0.110 -0.328 0.0 0.0 0.0 -32.37689 128 19 0.777
7 6653 48 6 SOUTH C 200 MOORE -0.027 -0.527 0.0 0.0 0.0 -30.83413 115 18 0.765
7 2583 54 1 TENNESS 200 DICKSON 0.074 0.169 0.0 0.0 0.0 -41.36459 118 22 0.704
7 8639 6 1 VERMONT 200 SMITH I. 0.150 -0.989 0.0 0.0 0.0 -38.23499 90 18 0.654
7 6710 6 2 VERMONT 1 MORRIS L.R. 0.743 -0.082 0.0 0.0 0.0 -8.06307 107 3 0.927
7 8646 40 1 VIRGINI 200 SMITH J. 0.036 -0.146 0.0 0.0 0.0 -33.48907 115 15 0.747
7 4532 40 2 VIRGINI 200 HOLMES -0.001 -0.220 0.0 0.0 0.0 -27.62147 129 12 0.807
7 4850 40 3 VIRGINI 200 JACKSON -0.321 0.177 0.0 0.0 0.0 -25.30791 107 10 0.789
7 9468 40 4 VIRGINI 200 TRIGG A. 0.021 -0.986 0.0 0.0 0.0 -26.06471 132 15 0.821
7 9470 40 5 VIRGINI 200 TRIGG J.J. 0.036 -0.185 0.0 0.0 0.0 -33.66003 130 21 0.772
7 1815 40 6 VIRGINI 200 CLAY -0.101 -0.406 0.0 0.0 0.0 -28.76522 126 11 0.796
7 7723 40 7 VIRGINI 200 RANDOLPH 0.181 -1.386 0.0 0.0 0.0 -30.06817 109 15 0.759
7 1739 40 8 VIRGINI 200 CLAIBORNE -0.010 -0.011 0.0 0.0 0.0 -29.01808 104 14 0.757
7 3582 40 9 VIRGINI 200 GILES -0.029 -0.077 0.0 0.0 0.0 -10.11153 48 6 0.810
7 3763 4010 VIRGINI 200 GRAY 0.159 -0.751 0.0 0.0 0.0 -41.34979 118 20 0.704
7 6905 4011 VIRGINI 200 NEWTON -0.182 -0.013 0.0 0.0 0.0 -21.90046 127 11 0.842
7 9014 4012 VIRGINI 1 STRATTON 0.533 0.327 0.0 0.0 0.0 -18.18605 79 11 0.794
7 1853 4013 VIRGINI 200 CLOPTON -0.120 -0.124 0.0 0.0 0.0 -19.63487 112 6 0.839
7 1380 4014 VIRGINI 200 CABELL -0.307 -0.033 0.0 0.0 0.0 -10.82478 78 4 0.870
7 2426 4015 VIRGINI 200 DAWSON -0.039 0.062 0.0 0.0 0.0 -33.45539 114 18 0.746
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7 1007 4017 VIRGINI 200 BRENT 0.036 -0.084 0.0 0.0 0.0 -17.75199 74 8 0.787
7 9335 4018 VIRGINI 200 THOMPSON 0.032 -0.862 0.0 0.0 0.0 -19.25388 125 6 0.857
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8 2357 198 CONNECT 1 DAVENPORT 0.747 0.047 0.0 0.0 0.0 -14.79744 118 3 0.882
8 3846 198 CONNECT 1 GRISWOLD R 0.728 0.182 0.0 0.0 0.0 -9.40640 105 1 0.914
8 3639 198 CONNECT 1 GODDARD 0.820 0.573 0.0 0.0 0.0 -5.64447 59 2 0.909
8 8651 198 CONNECT 1 SMITH J.C. 0.736 0.083 0.0 0.0 0.0 -8.81311 93 1 0.910
8 392 198 CONNECT 1 BALDWIN 0.713 0.425 0.0 0.0 0.0 -18.08268 108 7 0.846
8 2316 198 CONNECT 1 DANA 0.757 0.174 0.0 0.0 0.0 -19.25934 97 4 0.820
8 9169 198 CONNECT 1 TALLMADGE 0.697 0.034 0.0 0.0 0.0 -4.97044 48 2 0.902
8 8013 11 1 DELAWAR 200 RODNEY 0.127 -0.849 0.0 0.0 0.0 -29.18174 79 14 0.691
8 6422 4498 GEORGIA 200 MERIWETHER -0.044 -0.999 0.0 0.0 0.0 -36.43449 117 22 0.732
8 1174 4498 GEORGIA 200 BRYAN -0.016 -1.000 0.0 0.0 0.0 -27.06082 98 10 0.759
8 2819 4498 GEORGIA 200 EARLY 0.098 0.062 0.0 0.0 0.0 -52.12663 103 29 0.603
8 4012 4498 GEORGIA 200 HAMMOND -0.033 -0.101 0.0 0.0 0.0 -28.21627 75 13 0.686
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8 941 51 2 KENTUCK 200 BOYLE -0.043 0.057 0.0 0.0 0.0 -35.91766 98 20 0.693
8 9798 51 3 KENTUCK 200 WALTON -0.041 0.322 0.0 0.0 0.0 -43.13546 92 25 0.626
8 8203 51 4 KENTUCK 200 SANDFORD -0.107 -0.565 0.0 0.0 0.0 -35.21196 121 18 0.748
8 3324 51 5 KENTUCK 200 FOWLER 0.066 0.458 0.0 0.0 0.0 -34.47642 80 19 0.650
8 571 51 6 KENTUCK 200 BEDINGER -0.072 -0.423 0.0 0.0 0.0 -46.55404 121 25 0.681
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8 911 52 2 MARYLAN 200 BOWIE 0.110 -0.521 0.0 0.0 0.0 -16.70687 41 10 0.665
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8 6643 52 5 MARYLAN 200 MOORE N.R. -0.101 0.133 0.0 0.0 0.0 -34.35070 115 17 0.742
8 6198 52 5 MARYLAN 200 MCCREERY 0.058 0.135 0.0 0.0 0.0 -47.01210 104 21 0.636
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8 3001 3 1 MASSACH 200 EUSTIS 0.191 0.160 0.0 0.0 0.0 -30.97512 79 14 0.676
8 2214 3 2 MASSACH 200 CROWNINSHIE -0.034 0.961 0.0 0.0 0.0 -42.93868 124 16 0.707
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8 9650 3 4 MASSACH 200 VARNUM -0.098 1.014 0.0 0.0 0.0 -49.20081 122 28 0.668
8 2801 3 5 MASSACH 1 DWIGHT 0.847 0.278 0.0 0.0 0.0 -13.04554 117 3 0.894
8 9154 3 6 MASSACH 1 TAGGART 0.495 0.098 0.0 0.0 0.0 -18.52650 96 6 0.824
8 6571 3 7 MASSACH 1 MITCHELL N 0.513 0.140 0.0 0.0 0.0 -22.50358 114 10 0.821
8 10169 3 8 MASSACH 1 WILLIAMS L 0.764 0.646 0.0 0.0 0.0 -10.01542 93 4 0.898
8 733 3 9 MASSACH 200 BISHOP -0.057 0.748 0.0 0.0 0.0 -30.38634 62 9 0.613
8 4181 310 MASSACH 1 HASTINGS 0.581 0.205 0.0 0.0 0.0 -25.04186 109 13 0.795
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8 5477 312 MASSACH 200 LARNED -0.068 0.998 0.0 0.0 0.0 -13.03401 34 5 0.682
8 8338 313 MASSACH 200 SEAVER -0.203 0.739 0.0 0.0 0.0 -43.07338 111 20 0.678
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8 9707 315 MASSACH 1 WADSWORTH 0.638 -0.002 0.0 0.0 0.0 -24.31230 97 13 0.778
8 9271 316 MASSACH 1 THATCHER 0.657 0.452 0.0 0.0 0.0 -10.82673 97 3 0.894
8 1732 498 NEW HAM 1 CLAGETT 0.485 -0.721 0.0 0.0 0.0 -41.60844 113 21 0.692
8 9257 498 NEW HAM 1 TENNEY 0.619 0.056 0.0 0.0 0.0 -25.30841 125 11 0.817
8 4600 498 NEW HAM 1 HOUGH 0.606 0.104 0.0 0.0 0.0 -24.19057 95 16 0.775
8 679 498 NEW HAM 1 BETTON 0.758 -0.277 0.0 0.0 0.0 -17.19592 108 7 0.853
8 4748 498 NEW HAM 1 HUNT 0.565 0.057 0.0 0.0 0.0 -13.88776 74 3 0.829
8 4293 1298 NEW JER 200 HELMS 0.072 0.509 0.0 0.0 0.0 -31.18962 57 17 0.579
8 2942 1298 NEW JER 200 ELMER 0.061 0.678 0.0 0.0 0.0 -44.34917 95 21 0.627
8 6760 1298 NEW JER 200 MOTT 0.095 0.996 0.0 0.0 0.0 -32.14853 55 21 0.557
8 8581 1298 NEW JER 200 SLOAN 0.083 0.119 0.0 0.0 0.0 -72.51408 99 43 0.481
8 8748 1298 NEW JER 200 SOUTHARD -0.052 0.563 0.0 0.0 0.0 -47.01535 90 24 0.593
8 927 1298 NEW JER 200 BOYD 0.024 -0.087 0.0 0.0 0.0 -54.83012 93 28 0.555
8 8647 13 1 NEW YOR 200 SMITH J. 0.105 -0.082 0.0 0.0 0.0 -27.05109 50 10 0.582
8 7907 13 1 NEW YOR 200 RIKER 0.076 0.114 0.0 0.0 0.0 -16.11746 31 7 0.595
8 8206 13 2 NEW YOR 1 SANDS 0.494 0.114 0.0 0.0 0.0 -10.60404 30 5 0.702
8 6577 13 3 NEW YOR 200 MITCHILL S 0.079 0.351 0.0 0.0 0.0 -39.15791 77 18 0.601
8 9602 13 4 NEW YOR 200 VANCORTLAND 0.191 -0.142 0.0 0.0 0.0 -30.55550 63 18 0.616
8 6172 13 5 NEW YOR 200 MCCORD -0.289 -0.027 0.0 0.0 0.0 -30.13380 127 14 0.789
8 9662 13 6 NEW YOR 200 VERPLANCK 0.110 0.014 0.0 0.0 0.0 -32.63226 70 16 0.627
8 4169 13 7 NEW YOR 200 HASBROUCK 0.026 -0.007 0.0 0.0 0.0 -39.36486 108 18 0.695
8 5716 13 8 NEW YOR 1 LIVINGSTON 0.540 -0.140 0.0 0.0 0.0 -23.37194 106 11 0.802
8 9630 13 9 NEW YOR 1 VANRENSSELA 0.765 -0.110 0.0 0.0 0.0 -16.70438 74 6 0.798
8 9376 1310 NEW YOR 1 TIBBITTS 0.964 -0.268 0.0 0.0 0.0 -10.81932 64 7 0.844
8 7173 1311 NEW YOR 200 PALMER -0.683 0.731 0.0 0.0 0.0 -31.20499 106 15 0.745
8 9290 1312 NEW YOR 200 THOMAS -0.045 0.817 0.0 0.0 0.0 -34.68266 99 19 0.704
8 8187 1313 NEW YOR 200 SAMMONS 0.083 -0.156 0.0 0.0 0.0 -53.70547 113 27 0.622
8 8056 1314 NEW YOR 200 ROOT 0.022 0.351 0.0 0.0 0.0 -48.47081 111 30 0.646
8 3840 1315 NEW YOR 1 GRISWOLD G 0.998 0.065 0.0 0.0 0.0 -23.78512 120 14 0.820
8 7230 1316 NEW YOR 200 PATTERSON -0.115 0.523 0.0 0.0 0.0 -18.00295 63 11 0.751
8 7403 1317 NEW YOR 200 PHELPS -0.045 0.875 0.0 0.0 0.0 -11.33696 33 4 0.709
8 10407 47 1 NORTH C 200 WYNNS 0.003 0.035 0.0 0.0 0.0 -37.91903 89 20 0.653
8 148 47 2 NORTH C 200 ALSTON 0.001 0.566 0.0 0.0 0.0 -55.24491 122 32 0.636
8 5186 47 3 NORTH C 200 KENNEDY -0.012 -0.299 0.0 0.0 0.0 -63.54626 123 33 0.597
8 754 47 4 NORTH C 200 BLACKLEDGE -0.053 0.036 0.0 0.0 0.0 -70.73491 121 39 0.557
8 3594 47 5 NORTH C 200 GILLESPIE 0.001 0.149 0.0 0.0 0.0 -28.50255 93 8 0.736
8 7658 47 7 NORTH C 1 PURVIANCE 0.434 -0.648 0.0 0.0 0.0 -28.77336 75 18 0.681
8 8837 47 8 NORTH C 200 STANFORD 0.051 -0.901 0.0 0.0 0.0 -50.78323 126 29 0.668
8 10171 47 9 NORTH C 200 WILLIAMS M 0.086 0.013 0.0 0.0 0.0 -72.31744 122 42 0.553
8 88 4710 NORTH C 200 ALEXANDER 0.114 0.146 0.0 0.0 0.0 -30.16425 71 15 0.654
8 4519 4711 NORTH C 200 HOLLAND -0.212 0.398 0.0 0.0 0.0 -48.22641 113 24 0.653
8 10280 4712 NORTH C 200 WINSTON -0.031 -0.032 0.0 0.0 0.0 -53.94375 121 24 0.640
8 6728 24 1 OHIO 200 MORROW -0.038 0.259 0.0 0.0 0.0 -47.72387 114 26 0.658
8 7867 14 1 PENNSYL 200 RICHARDS -0.124 -0.190 0.0 0.0 0.0 -42.08945 110 27 0.682
8 5594 14 1 PENNSYL 200 LEIB -0.165 -0.234 0.0 0.0 0.0 -40.02059 124 24 0.724
8 1814 14 1 PENNSYL 200 CLAY J. -0.068 -0.404 0.0 0.0 0.0 -45.28117 115 24 0.675
8 9621 14 2 PENNSYL 200 VAN HORNE -0.104 0.023 0.0 0.0 0.0 -31.74427 103 17 0.735
8 1132 14 2 PENNSYL 200 BROWN -0.211 0.242 0.0 0.0 0.0 -33.59117 126 13 0.766
8 2005 14 2 PENNSYL 200 CONRAD -0.307 0.655 0.0 0.0 0.0 -36.63650 117 19 0.731
8 169 14 3 PENNSYL 200 ANDERSON -0.228 0.205 0.0 0.0 0.0 -35.69259 118 14 0.739
8 4394 14 3 PENNSYL 200 HIESTER 0.046 -0.051 0.0 0.0 0.0 -44.09455 92 27 0.619
8 10054 14 3 PENNSYL 200 WHITEHILL -0.350 0.637 0.0 0.0 0.0 -25.42002 83 10 0.736
8 429 14 4 PENNSYL 200 BARD -0.219 0.183 0.0 0.0 0.0 -34.38223 114 15 0.740
8 4037 14 4 PENNSYL 200 HANNA 0.103 -0.041 0.0 0.0 0.0 -46.37379 95 25 0.614
8 3805 14 5 PENNSYL 200 GREGG -0.076 0.284 0.0 0.0 0.0 -46.44387 107 29 0.648
8 8938 14 6 PENNSYL 200 STEWART -0.119 0.128 0.0 0.0 0.0 -41.99005 112 18 0.687
8 7760 14 7 PENNSYL 200 REA -0.178 -0.580 0.0 0.0 0.0 -26.87051 121 13 0.801
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11 9630 13 7 NEW YOR 1 VAN RENSSEL 0.744 0.130 0.0 0.0 0.0 -44.23712 244 23 0.834
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11 5895 47 6 NORTH C 200 MACON -0.096 -1.130 0.0 0.0 0.0 -125.87718 243 65 0.596
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11 6539 14 2 PENNSYL 1 MILNOR 0.573 0.509 0.0 0.0 0.0 -51.96885 240 19 0.805
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11 8071 14 2 PENNSYL 200 ROSS 0.196 0.045 0.0 0.0 0.0 -64.10431 123 32 0.594
11 7873 14 3 PENNSYL 200 RICHARDS -0.076 0.200 0.0 0.0 0.0 -72.72219 275 35 0.768
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11 8620 14 5 PENNSYL 200 SMITH G. -0.059 0.278 0.0 0.0 0.0 -88.50469 277 37 0.727
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11 7760 14 7 PENNSYL 200 REA -0.107 -0.080 0.0 0.0 0.0 -81.03167 271 31 0.742
11 3147 14 8 PENNSYL 200 FINDLEY -0.028 0.603 0.0 0.0 0.0 -80.52925 273 34 0.745
11 8596 14 9 PENNSYL 200 SMILIE -0.122 0.244 0.0 0.0 0.0 -70.04142 266 41 0.769
11 5846 1410 PENNSYL 200 LYLE -0.193 0.166 0.0 0.0 0.0 -60.83383 276 32 0.802
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11 1419 48 6 SOUTH C 200 CALHOUN -0.305 0.218 0.0 0.0 0.0 -42.17662 275 16 0.858
11 6653 48 7 SOUTH C 200 MOORE T. -0.154 -0.415 0.0 0.0 0.0 -94.20472 241 42 0.676
11 146 48 8 SOUTH C 200 ALSTON L. -0.051 -0.253 0.0 0.0 0.0 -52.84095 187 23 0.754
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11 5792 40 9 VIRGINI 200 LOVE -0.111 0.315 0.0 0.0 0.0 -51.22590 195 22 0.769
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11 7723 4015 VIRGINI 200 RANDOLPH 0.086 -0.996 0.0 0.0 0.0 -28.58921 114 8 0.778
11 2977 4016 VIRGINI 200 EPPES -0.113 -0.153 0.0 0.0 0.0 -40.79236 157 22 0.771
11 3552 4017 VIRGINI 200 GHOLSON -0.097 -0.017 0.0 0.0 0.0 -89.02463 274 44 0.723
11 3691 4018 VIRGINI 200 GOODWYN -0.154 -0.209 0.0 0.0 0.0 -52.04335 225 23 0.793
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12 7469 198 CONNECT 1 PITKIN T. 0.665 -0.285 0.0 0.0 0.0 -23.42367 260 8 0.914
12 9063 198 CONNECT 1 STURGES L. 0.634 -0.344 0.0 0.0 0.0 -26.75068 291 14 0.912
12 1632 198 CONNECT 1 CHAMPION E 0.621 -0.558 0.0 0.0 0.0 -53.43697 277 22 0.825
12 9169 198 CONNECT 1 TALLMADGE 0.703 -0.251 0.0 0.0 0.0 -32.16561 241 13 0.875
12 2357 198 CONNECT 1 DAVENPORT 0.804 -0.297 0.0 0.0 0.0 -37.33672 269 16 0.870
12 6746 198 CONNECT 1 MOSELEY J. 0.670 -0.298 0.0 0.0 0.0 -27.01755 282 11 0.909
12 5503 198 CONNECT 1 LAW L. 0.781 -0.624 0.0 0.0 0.0 -20.18783 236 7 0.918
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12 686 4498 GEORGIA 200 BIBB W.W. -0.203 -0.465 0.0 0.0 0.0 -110.56223 295 55 0.687
12 3933 4498 GEORGIA 200 HALL B. -0.896 -0.444 0.0 0.0 0.0 -70.20255 299 30 0.791
12 450 4498 GEORGIA 200 BARNETT W. -0.175 -0.058 0.0 0.0 0.0 -14.79568 57 5 0.771
12 6886 51 1 KENTUCK 200 NEW A. -0.150 0.035 0.0 0.0 0.0 -54.73365 216 24 0.776
12 6290 51 2 KENTUCK 200 MC KEE S. -0.038 -0.431 0.0 0.0 0.0 -99.56906 228 49 0.646
12 7083 51 3 KENTUCK 200 ORMSBY S. -0.080 0.194 0.0 0.0 0.0 -87.26731 272 36 0.726
12 4988 51 4 KENTUCK 200 JOHNSON R. -0.147 -0.132 0.0 0.0 0.0 -54.04623 225 20 0.786
12 2527 51 6 KENTUCK 200 DESHA J. -0.158 -0.281 0.0 0.0 0.0 -76.84380 279 27 0.759
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12 9055 52 1 MARYLAN 1 STUART P. 0.241 -0.724 0.0 0.0 0.0 -42.63618 237 15 0.835
12 5194 52 2 MARYLAN 200 KENT J. 0.028 -0.012 0.0 0.0 0.0 -120.08435 276 55 0.647
12 5225 52 3 MARYLAN 1 KEY P.B. 0.351 -0.543 0.0 0.0 0.0 -36.78242 156 18 0.790
12 7910 52 4 MARYLAN 200 RINGGOLD S -0.085 0.169 0.0 0.0 0.0 -62.67979 210 28 0.742
12 6306 52 5 MARYLAN 200 MC KIM A. -0.064 0.643 0.0 0.0 0.0 -95.90004 272 51 0.703
12 5704 52 5 MARYLAN 200 LITTLE P. -0.247 0.312 0.0 0.0 0.0 -95.08381 281 44 0.713
12 223 52 6 MARYLAN 200 ARCHER S. -0.038 0.065 0.0 0.0 0.0 -123.65784 269 71 0.631
12 10396 52 7 MARYLAN 200 WRIGHT R. -0.013 0.041 0.0 0.0 0.0 -151.01718 263 74 0.563
12 3652 52 8 MARYLAN 1 GOLDSBOROUG 0.361 -0.615 0.0 0.0 0.0 -37.05444 195 14 0.827
12 7675 3 1 MASSACH 1 QUINCY J. 0.739 0.232 0.0 0.0 0.0 -39.45253 237 20 0.847
12 7797 3 2 MASSACH 1 REED W. 0.790 0.486 0.0 0.0 0.0 -38.56834 248 17 0.856
12 10038 3 3 MASSACH 1 WHITE L. 0.756 0.008 0.0 0.0 0.0 -25.40520 289 5 0.916
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12 9154 3 6 MASSACH 1 TAGGART S. 0.679 -0.228 0.0 0.0 0.0 -29.10170 258 11 0.893
12 9510 3 7 MASSACH 200 TURNER C. 0.050 0.957 0.0 0.0 0.0 -92.62267 300 48 0.734
12 3779 3 8 MASSACH 200 GREEN I.L. -0.029 0.619 0.0 0.0 0.0 -127.45093 309 67 0.662
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12 323 312 MASSACH 200 BACON E. 0.158 0.570 0.0 0.0 0.0 -94.35004 235 41 0.669
12 8338 313 MASSACH 200 SEAVER E. -0.182 0.146 0.0 0.0 0.0 -79.27536 233 40 0.712
12 2294 314 MASSACH 200 CUTTS R. 0.057 0.860 0.0 0.0 0.0 -37.31693 104 14 0.699
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12 9173 316 MASSACH 200 TALLMAN P. 0.416 0.703 0.0 0.0 0.0 -21.16314 75 11 0.754
12 1527 317 MASSACH 200 CARR F. 0.151 0.881 0.0 0.0 0.0 -35.41556 109 14 0.723
12 2607 498 NEW HAM 200 DINSMOOR S -0.046 0.423 0.0 0.0 0.0 -94.10109 284 48 0.718
12 4088 498 NEW HAM 200 HARPER J.A -0.052 0.978 0.0 0.0 0.0 -76.29941 265 38 0.750
12 491 498 NEW HAM 200 BARTLETT J 0.220 0.976 0.0 0.0 0.0 -19.99564 65 12 0.735
12 3950 498 NEW HAM 200 HALL O. -0.185 -0.053 0.0 0.0 0.0 -95.24857 302 46 0.730
12 9067 498 NEW HAM 1 SULLIVAN G 0.404 0.042 0.0 0.0 0.0 -13.50341 56 6 0.786
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12 6095 1298 NEW JER 200 MAXWELL G. 0.032 -0.280 0.0 0.0 0.0 -94.70835 157 52 0.547
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12 1972 1298 NEW JER 200 CONDICT L. -0.203 0.417 0.0 0.0 0.0 -87.49364 263 39 0.717
12 8173 13 1 NEW YOR 200 SAGE E. -0.105 0.480 0.0 0.0 0.0 -73.51242 274 29 0.765
12 7269 13 2 NEW YOR 200 PAULDING W 0.219 0.976 0.0 0.0 0.0 -2.85991 28 2 0.903
12 6577 13 2 NEW YOR 200 MITCHILL S 0.079 0.351 0.0 0.0 0.0 -97.54814 232 43 0.657
12 9603 13 3 NEW YOR 200 VAN CORTLAN 0.516 0.747 0.0 0.0 0.0 -34.65099 187 13 0.831
12 2961 13 4 NEW YOR 1 EMOTT J. 0.551 -0.120 0.0 0.0 0.0 -30.89295 225 17 0.872
12 2031 13 5 NEW YOR 200 COOKE T.B. 0.454 0.205 0.0 0.0 0.0 -13.41579 116 5 0.891
12 3175 13 6 NEW YOR 1 FITCH A. 0.667 -0.161 0.0 0.0 0.0 -38.15787 288 13 0.876
12 5719 13 6 NEW YOR 1 LIVINGSTON 0.679 0.228 0.0 0.0 0.0 -20.65512 136 7 0.859
12 3856 13 6 NEW YOR 1 GROSVENOR 0.746 0.666 0.0 0.0 0.0 -8.94297 42 4 0.808
12 793 13 7 NEW YOR 1 BLEECKER H 0.605 0.178 0.0 0.0 0.0 -37.92935 262 16 0.865
12 7520 13 8 NEW YOR 200 POND B. 0.031 0.784 0.0 0.0 0.0 -79.48363 231 34 0.709
12 8187 13 9 NEW YOR 200 SAMMONS T. 0.083 -0.156 0.0 0.0 0.0 -123.64246 213 71 0.560
12 9005 1310 NEW YOR 200 STOW S. 0.219 0.146 0.0 0.0 0.0 -93.21185 197 48 0.623
12 3648 1311 NEW YOR 1 GOLD T.R. 0.703 0.353 0.0 0.0 0.0 -35.00257 240 14 0.864
12 6440 1312 NEW YOR 200 METCALF A. -0.017 -0.109 0.0 0.0 0.0 -114.43427 245 60 0.627
12 9451 1313 NEW YOR 200 TRACY U. 0.319 0.841 0.0 0.0 0.0 -67.45089 238 31 0.753
12 299 1314 NEW YOR 200 AVERY D. 0.011 0.525 0.0 0.0 0.0 -43.08830 111 22 0.678
12 7539 1315 NEW YOR 200 PORTER P.B 0.225 0.942 0.0 0.0 0.0 -40.63999 112 22 0.696
12 8237 47 1 NORTH C 200 SAWYER L. -0.034 0.219 0.0 0.0 0.0 -33.93420 79 14 0.651
12 148 47 2 NORTH C 200 ALSTON W. -0.124 0.688 0.0 0.0 0.0 -93.92339 262 42 0.699
12 805 47 3 NORTH C 200 BLOUNT T. -0.205 -0.421 0.0 0.0 0.0 -9.31259 41 4 0.797
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12 754 47 4 NORTH C 200 BLACKLEDGE -0.053 0.036 0.0 0.0 0.0 -95.42908 272 38 0.704
12 5275 47 5 NORTH C 200 KING W.R. -0.099 -0.200 0.0 0.0 0.0 -103.89561 274 41 0.684
12 5895 47 6 NORTH C 200 MACON N. -0.106 -1.196 0.0 0.0 0.0 -103.73540 243 49 0.653
12 6128 47 7 NORTH C 1 MC BRYDE A 0.325 -0.777 0.0 0.0 0.0 -39.93330 215 16 0.830
12 8837 47 8 NORTH C 200 STANFORD R 0.066 -1.161 0.0 0.0 0.0 -104.26817 293 48 0.701
12 1880 47 9 NORTH C 200 COCHRAN J. -0.182 -0.746 0.0 0.0 0.0 -73.14891 231 34 0.729
12 7288 4710 NORTH C 1 PEARSON J. 0.315 -0.949 0.0 0.0 0.0 -38.80181 271 15 0.867
12 7427 4711 NORTH C 200 PICKENS I. -0.126 -0.156 0.0 0.0 0.0 -100.79790 297 47 0.712
12 3344 4712 NORTH C 200 FRANKLIN M -0.214 -0.413 0.0 0.0 0.0 -71.75420 241 34 0.742
12 6728 24 1 OHIO 200 MORROW J. 0.131 0.252 0.0 0.0 0.0 -177.52878 275 98 0.524
12 8381 14 1 PENNSYL 200 SEYBERT A. 0.049 0.247 0.0 0.0 0.0 -135.38054 279 57 0.616
12 184 14 1 PENNSYL 200 ANDERSON W -0.084 0.674 0.0 0.0 0.0 -77.85290 288 39 0.763
12 6538 14 1 PENNSYL 1 MILNOR J. 0.571 0.062 0.0 0.0 0.0 -26.53619 203 12 0.877
12 7953 14 2 PENNSYL 200 ROBERTS J. -0.288 -0.139 0.0 0.0 0.0 -82.52122 307 37 0.764
12 1132 14 2 PENNSYL 200 BROWN R. -0.248 0.158 0.0 0.0 0.0 -73.04102 291 36 0.778
12 8011 14 2 PENNSYL 200 RODMAN W. 0.167 -0.604 0.0 0.0 0.0 -118.77867 256 68 0.629
12 4789 14 3 PENNSYL 200 HYNEMAN J. -0.147 0.291 0.0 0.0 0.0 -92.07531 279 39 0.719
12 5577 14 3 PENNSYL 200 LEFEVER J. -0.112 -0.340 0.0 0.0 0.0 -63.21661 173 33 0.694
12 2409 14 3 PENNSYL 200 DAVIS R. -0.060 0.229 0.0 0.0 0.0 -95.48163 272 54 0.704
12 429 14 4 PENNSYL 200 BARD D. -0.317 -0.206 0.0 0.0 0.0 -65.38174 238 33 0.760
12 10055 14 4 PENNSYL 200 WHITEHILL -0.206 0.001 0.0 0.0 0.0 -109.54818 274 45 0.670
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