EDBT 2026 Demo / reviewers in the wild / expert
Jaqueline Silveira
dblp:279/9124
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
0000-0002-3106-7027ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Data mining · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visualization and visual analytics › visual analytics
spatiotemporal visual analytics |
0.5 | 1 | 2021 | CrimAnalyzer: Understanding Crime Patterns in São Paulo · IEEE Trans. Vis. Comput. Graph. 2021 |
Visualization and visual analytics › geospatial visualization
urban data visualization |
0.5 | 1 | 2021 | CrimAnalyzer: Understanding Crime Patterns in São Paulo · IEEE Trans. Vis. Comput. Graph. 2021 |
Visualization and visual analytics
visual analytics |
0.5 | 1 | 2021 | CrimAnalyzer: Understanding Crime Patterns in São Paulo · IEEE Trans. Vis. Comput. Graph. 2021 |
Data mining
spatiotemporal data mining |
0.1 | 1 | 2021 | CrimAnalyzer: Understanding Crime Patterns in São Paulo · IEEE Trans. Vis. Comput. Graph. 2021 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | CrimAnalyzer: Understanding Crime Patterns in São PauloabstractSão Paulo is the largest city in South America, with crime rates that reflect its size. The number and type of crimes vary considerably around the city, assuming different patterns depending on urban and social characteristics of each particular location. Previous works have mostly focused on the analysis of crimes with the intent of uncovering patterns associated to social factors, seasonality, and urban routine activities. Therefore, those studies and tools are more global in the sense that they are not designed to investigate specific regions of the city such as particular neighborhoods, avenues, or public areas. Tools able to explore specific locations of the city are essential for domain experts to accomplish their analysis in a bottom-up fashion, revealing how urban features related to mobility, passersby behavior, and presence of public infrastructures (e.g., terminals of public transportation and schools) can influence the quantity and type of crimes. In this paper, we present CrimAnalyzer, a visual analytic tool that allows users to study the behavior of crimes in specific regions of a city. The system allows users to identify local hotspots and the pattern of crimes associated to them, while still showing how hotspots and corresponding crime patterns change over time. CrimAnalyzer has been developed from the needs of a team of experts in criminology and deals with three major challenges: i) flexibility to explore local regions and understand their crime patterns, ii) identification of spatial crime hotspots that might not be the most prevalent ones in terms of the number of crimes but that are important enough to be investigated, and iii) understand the dynamic of crime patterns over time. The effectiveness and usefulness of the proposed system are demonstrated by qualitative and quantitative comparisons as well as by case studies run by domain experts involving real data. The experiments show the capability of CrimAnalyzer in identifying crime-related phenomena. Germain García-Zanabria, Jaqueline Silveira, Jorge Poco, Afonso Paiva 0001, Marcelo Batista Nery, Cláudio T. Silva, Sergio Adorno, Luis Gustavo Nonato |
IEEE Trans. Vis. Comput. Graph. | 2 |