Visual Analytics: Traffic Collisions in Italy
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This document describes a visual analytics project analyzing traffic collision data in Italy from 2003 to 2013. It discusses the tasks, dataset, data preprocessing with PCA, and various visualizations and interactive elements in the dashboard, including an interactive map of Italy, histograms, and slider filters for year and PCA scaler. The project aims to provide insights into traffic collisions and identify relationships between different factors.
Visual Analytics: Traffic Collisions in Italy
- 1. Visual Analytics project Traffic collisions in Italy Roberto Falconi AY 2018/19
- 2. Information visualization application design • Task, subtask and problem • Dataset • Scalers • PCA Algorithm • Representation and presentation • Overview and interactive objects • Filtering • Significant objects • Navigational guidance
- 3. Analytic and visual solutions DATA TO ANALYZE FILTERS SHOWING QUANTITATIVE INFORMATION UI AND UX VISUALIZATIONS FRAMEWORKS
- 4. Task, subtask and problem To perform main task, subtasks are needed: we have to gain insights into a collection and to understand the set of characteristics. The project task is to evaluate statistics of traffic collision in Italy, looking for a correlation between the actors of these accidents.
- 5. Dataset • Data are provided by ISTAT and contains information about the Italians traffic collisions, including injured and types of incident, organized by year (from 2003 to 2013) and by Italian region. • The AS (AngeliniSantucci) index is defined as: AS = #tuples * #dimensions. In this project we have an AS = 10850.
- 6. Standardization and PCA algorithm • Principal Component Analysis is a method of extracting important variables (in form of components) from a large set of variables available in a dataset. It extracts low dimensional set of features from a high dimensional dataset with a motive to capture as much information as possible. • PCA is affected by scale, so we need to scale features in the dataset before applying PCA, e.g. standardization involves rescaling the features such that they have the properties of a standard normal distribution with a mean of zero and a standard deviation of one.
- 7. Scalers and PCA algorithm • The original data has many columns. After applying a scaler, PCA transforms the original data which is multidimensional into 2 dimensions, as the following example. • Finally, we can plot our data.
- 8. 2-component PCA output • In these graphs, according to original data, Standard Scaler, MinMax Scaler, Quantile Transformer and Power Transformer, we can see regions with low-rate of accidents nearby on the left, while regions with high-rate of accidents are detached, far on the right. • On the other hand, in Normalizer, we have regions with low collisions on the right, while the others are on the left.
- 9. Representation and presentation • First, we have to map data values to visual attributes to represent a value. • Then, it is possible to show relations among values and apply useful interaction technique; in this project, as an example, are applied the mouse hover technique, clickable regions, filters and sliders. • The project uses modern solutions, including easily visualizing numbers, large explained details and big digits.
- 10. Overview and interactive objects • Data information is shown using histograms and graphs. • The project includes many interactive tools, e.g. time series slider, mouse-hovering and clickable Italian regions and selectable options filters.
- 11. Data filtering • A system for suppressing not relevant data is required. The dataset provided by ISTAT was full of (for my purposes) useless information, such as car brands involved in accidents. So, these columns, were deleted.
- 12. Significant objects • To simplify the exploration of the dataset, it is useful to mark and define some significant and interesting objects • With significant data it is involved information that the user was looking for, statistics and/or graphs that the users will look again
- 13. Navigational guidance • To help users to navigate visualized data, the project fits into one fixed-size page. No multiple pages or infinite scrolling are needed, because they can confuse users. • All the information and the data are in one place and they are interactive and clickable, useful, good-looking and they also provide powerful items to satisfy user requirements.
- 14. Data to analyze The data to analyze includes numbers of circulation crashes in Italy per year in each Italian region, examining both urban and extra-urban roads, investigating deaths and injured. Other information is about number of licensed people and the number of collisions, in order to make ratio between this two numbers and to make relationships between each Italian region according to its ratio. Also, selecting a region it is possible to inspect collisions on this region and the related severity.
- 15. Filters #1 • The solution makes it possible to let users to use filters on the data to analyze, such as the type of collision, including only vehicles, only vehicles and pedestrians or both vehicles and pedestrian. • It is also possible to review the seriousness of the collisions, analyzing data if there is at least one injured or at least one dead; another filter is the possibility to make analysis on data about collisions only on urban roads, only on extra-urban roads or both urban and on extra-urban roads.
- 16. Filters #2 • Another available filter is the year: it is possible to choose the year of data to analyze thanks to a time series slider, this will have repercussion on all the investigated information. • It is also possible to select regions, showing, by year and by all the previously discussed and chosen filters, the results of the analysis and the number of persons with driving license and how many of them are injured.
- 17. Showing quantitative information • After choosing filters, it is possible to show various data both in percentage and in absolute numbers, using the right length and the right scale avoiding pie charts or 3D charts, avoiding also areas, angles and volumes comparisons and using opportune colors scales only when strictly needed. • A second slider also allows users to switch between the used PCA scalers.
- 18. UI and UX In this project, it is implemented the most user-friendly UI and UX I could design, applying information visualization representing abstract data to amplify cognition. The actual understanding of information visualization involves cognitive activity and interactive activity. The first is about computer-based visualization, the second allows the user to manipulate the visualization to better reach his goals. Scrolling is boring, consumes a lot of time and most content is hidden from the view. Distortion is also disturbing, so I don’t use 3D objects neither perspective walls in the project. Suppression and zoom are not needed because the interactive map of Italy, the histograms, the graphs and the sliders perfectly fit in the monitor page.
- 19. Interactive map of Italy • The visual part provides to the user a computer software dashboard with an interactive map of Italy on the left, with big and clickable regions, each one with a color density for quantitative comparison using as colors shades of green (from dark green to light green), according to the key legend on the left. • Hovering each region of Italy with the mouse, it pops up a little window showing by the year and by all the selected filters the number of licensed people, the number of accidents and the ratio of accidents over licensed.
- 20. Histograms showing data • Histograms show data about traffic collisions on urban roads and on extra- urban road and they also show the information about the number of dead and injured.
- 21. PCA Scalers and Time Series Sliders • Time series slider allows users to change the year of analyzed data. This action changes all the other visualizations: the PCA output changes according to the selected year, each single Italian region change its own color of shades of green according to the key legend; histograms changes its own rectangles length maintaining the same lie factor and the rate scale on both x and y axes, in order to avoid distortion and to not deform the data representation. • PCA scalers slider changes the used scaler to visualize the PCA algorithm output.
- 22. Frameworks and languages • Project’s frontend is developed using D3.js JavaScript library. • Then, there is a huge range of visualization functionality available for Python, with a diversity in approach and focus that is reflected in the large number of libraries used in backend.
- 23. Visual Analytics cycle • Available data are elaborated in order to make a functional and useful dataset to be suitable for the project purposes. • Through scripts it is applied a parameter refinement and it makes a mapping to build a visualizable and interactive dashboard. • Through the view, users are able to interact with filters, with the interactive map of Italy and with the other visualizations such as histograms, time series and PCA sliders in order to read in a good way the data, to acquire knowledge about Italian traffic collisions, and, hopefully, trying to sensitize drivers and pedestrian about road hazards.
- 24. Conclusion • Road traffic accidents is the leading cause of death by injury and the tenth-leading cause of all deaths globally. • If present trends continue, road traffic injuries are predicted to be the third-leading contributor to the global burden of disease and injury by 2020. • We get two points: most accidented regions are not only the most peopled and the situation is not getting better over the year. • We absolutely need to set stronger road and safety rules, securing compliance, and improving transport policy.
- 25. ➢ Check out more amazing projects by Roberto Falconi http://www.robertodaguarcino.com https://github.com/RobertoFalconi https://www.linkedin.com/in/roberto-falconi https://www.slideshare.net/RobertoFalconi4 Thank you!