A data analyst for a national retail chain is tasked with identifying promising locations for new stores. The analyst has a dataset containing the exact street addresses of all current stores, along with census tract data that includes median household income and population density across the country. To effectively visualize which geographic areas have high income and are far from existing stores, which of the following visualizations would be most appropriate?
The correct answer is a choropleth map. This type of map is specifically designed to represent statistical data, such as median income or population density, over predefined geographical areas like census tracts by using different shades or colors. This allows the analyst to instantly identify high-income areas. The locations of existing stores can then be overlaid as points on this map, making it easy to see which high-income areas are geographically distant from current stores.
A pivot table is incorrect because it is a tool for summarizing and aggregating data into a tabular format, not for visualizing spatial relationships. A bar chart is also incorrect as it would compare the income levels of different census tracts but would lose all geographic context, making it impossible to determine proximity to stores. A scatter plot could be used to plot points on a coordinate system (e.g., latitude vs. longitude), but it is less effective for displaying data that is aggregated into predefined regions like census tracts. A choropleth map is the most suitable choice because it directly visualizes the data within its geographic boundaries.
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