City governments collect a significant amount of data that is valuable to a vast cross-section of the public. Cities sift through a lot of data from departments including public safety, environment, education, housing, health, business, transportation, social services, etc. Managing all this data in the various departments to achieve results can often be overwhelming.
With visualizations, cities can extract actionable information and insights, analyze data, track performance, look at trends, answer questions of interest, and improve service delivery. In most cases cities value the importance of using charts and graphs to understand data but often confront the challenge of choosing the right type of visualization for the right kind of question.
Understanding the reason why you might need a visualization for a particular report or analysis is the first and critical step to choosing a chart. Your choice of chart/graph would depend on the type of question you want to answer. There is an inexhaustible number of chart types but the following are common and can be used to meet your data visualization needs.
Circle chart divided into slices to illustrate numerical proportions or percentages. Size of each slice is proportional to the quantity it represents.
Displays data using bars of same width for categories of observations.
Displays information using rectangles of equal width to show the frequency of data in consecutive numerical intervals of equal class size.
A set of individual dots displayed in a Cartesian plane where each dot denotes an observation for a set of data.
Created by connecting a series of data points with a straight line.
Made up of a series of horizontal lines displaying the amount of work or production completed and uncompleted at different time periods.
A line chart with areas below the lines filled with colors.
A column chart stacked in groups to show the value of the data category it represents.
Rectangular chart split up into subrectangles that are sized and ordered in quantitative magnitude.
A variation of a scatter plot that displays three dimensions of data.
A map showing the distribution of variables across geographic boundaries.