This tutorial introduces you to marks by creating a heatmap visualization. The heatmap shows contribution level to the Republican party within the continental United States:
The contribution data are obtained using the following SQL query:
"data": [
{
"name": "heatmap_query",
"sql": "SELECT rect_pixel_bin(conv_4326_900913_x(lon), -13847031.457875465, -7451726.712679257, 733, 733) as x,
rect_pixel_bin(conv_4326_900913_y(lat), 2346114.147993467, 6970277.197053557, 530, 530) as y,
SUM(amount) as cnt
FROM contributions
WHERE (lon >= -124.39000000000038 AND lon <= -66.93999999999943) AND
(lat >= 20.61570573311549 AND lat <= 52.93117449504004) AND
amount > 0 AND
recipient_party = 'R'
GROUP BY x, y"
}
]
The visualization uses a Symbol Type marks type to represent each data item in the heatmap_query data table:
Notice that the data x and y location values do not reference a scale. The location values are the values of the SQL query, transformed using extension functions.
The fill color of the square uses the heat_color scale to determine the color used to represent the data item.
Quantize scales are similar to linear scales, except they use a discrete rather than continuous range. The continuous input domain is divided into uniform segments based on the number of values in the output range.
A heatmap shows a continuous input domain divided into uniform segments based on the number of values in the output range. This is a quantize scales type. In the example, dollar amounts between $10,000 and $1 million are uniformly divided among 21 range values, where the larger amounts are represented by brighter colors.
Values outside the domain and null values are rendered as dark blue, #0d0887.
