Visualizing Crime Trends with the Data Rose Method

Displaying categorical information that changes across geography and time presents a persistent challenge in spatial analysis. Traditional reporting formats—such as tables listing crime types against neighborhoods or police beats—are useful for documentation but often fall short when the goal is pattern recognition. Even conventional thematic maps can struggle to simultaneously communicate multiple categories and temporal variation. To address this limitation, a visual method known as the “Data Rose” was implemented to explore crime statistics in Salt Lake City.

Structure of the Data Rose

The Data Rose condenses complex spatiotemporal information into a compact, radial graphic. Its structure consists of twelve concentric rings, each representing a month of the year. January occupies the innermost circle, while December forms the outermost ring. Each ring is divided into ten sectors corresponding to different crime categories. The result is a circular matrix that displays 120 distinct data points for a single geographic unit.

Color gradients provide the quantitative dimension. Within each crime category, a five-class color scheme ranges from green to red, representing increasing frequency. Because the color ramp is standardized per crime type across the entire city, comparisons within a category are consistent geographically. However, the scale is not standardized across categories. A red segment in arson may represent far fewer incidents than a red segment in car prowls, reflecting the relative rarity or prevalence of each crime type. Importantly, the data are presented as raw counts and are not adjusted for population.

Interpreting Crime Patterns

Beyond its ability to aggregate large datasets visually, the Data Rose introduces intuitive categorical organization. In the Salt Lake City implementation, crimes directed at individuals appear on one side of the circular graphic, while property-related crimes are positioned on the opposite side. This arrangement allows users to quickly distinguish thematic groupings at a glance.

The design makes temporal patterns immediately visible. Seasonal fluctuations, such as increases in vandalism during summer months, emerge clearly through intensified color bands in specific rings. Conversely, crime categories that maintain relatively steady levels throughout the year display consistent coloration across rings. This radial configuration enables straightforward temporal comparison within a specific crime type, something that is often difficult to achieve using other graphical techniques.

Comparison with Conventional Graphs

Alternative visualization strategies, such as stacked bar charts placed within each police beat, can technically accommodate multiple variables. In such charts, individual bars may represent months, with stacked segments indicating crime categories. However, these approaches introduce alignment challenges. Because each category occupies a different vertical position within stacked bars, tracking changes over time for a single crime type becomes visually cumbersome. The Data Rose resolves this by anchoring each category to a fixed angular position, making longitudinal comparison intuitive.

Aesthetically, the circular format also offers advantages. It communicates density and variation without overwhelming the viewer, while preserving the spatial integrity of the mapped area. Each police beat can be represented by its own rose symbol, maintaining geographic reference while embedding detailed temporal information directly into the map.

Conceptual Origins and Adaptation

The Data Rose approach applied in Salt Lake City draws inspiration from earlier work by Guilan Huang of Georgetown University’s Division of Integrated Biodefense. Huang’s design featured concentric rings encircling an entire county, with sectors representing the evolution of a single variable over time within ZIP Codes. The Salt Lake City adaptation modifies this framework by embedding smaller roses within each geographic unit and expanding the sectors to display multiple crime categories simultaneously rather than a single variable.

Broader Applications

While demonstrated through crime statistics, the Data Rose is not limited to public safety analysis. Any dataset composed of categorical variables that shift across time and space—such as bird migration counts, disease incidence patterns or retail sales categories—could benefit from this visualization method. By merging spatial reference with temporal sequencing and categorical differentiation, the Data Rose offers a compact and effective solution for interpreting multidimensional geographic data.

In environments where traditional maps and tables fail to expose subtle patterns, the Data Rose provides an alternative lens—one capable of revealing both seasonal dynamics and categorical contrasts within a single, cohesive graphic.