Carto locations9/9/2023 ![]() Human Mobility (footfall) dataset is provided by Vodafone and consists of anonymized counts of unique visitors and total visits to an area during a time window segmented by age gender visitor profile and economic level in a 250x250m cell grid.POIs are classified at different category levels the highest being by trade division (retail transportation tourism etc.). Point of Interest (POI) dataset is provided by Pitney Bowes.We are using a month of data because we are interested in trends not exact numbers so one month is enough to identify hourly and daily trends. Traffic stats dataset is provided by TomTom and contains information about traffic density and speed per street segment.All this data is provided by a range of CARTO data partners through CARTO's Data Observatory. We also identified other features that can influence accidents such as traffic density and points of interest (POI). Traffic accident data is organized in 5 different datasets and contains very detailed information such as geolocation date and hour number of injured people age and gender of people involved type and years of driving license main cause type of accident and the type and color of vehicle. AEMET for historical daily weather data.OpenStreetMaps (OSM) for building footprints and road intersections.Barcelona Open Data Catalog for accident and road traffic signalling data.Note that data is only shown for an area of the city. The layer selector on the legends can be used to activate/deactivate one layer and analyze them. The following map shows all the datasets we used for this model. All premium datasets were obtained through CARTO's Data Observatory. We identified different data sources that can influence accidents and worked with open and premium data. Barcelona was selected as it is a mid-large city and has available a rich Open Data catalog. The analysis focuses on the city of Barcelona (Spain) using traffic accident data from 2019 and can be replicated in other cities and regions of the world. In this blog post we present a detailed analysis and a powerful predictive model that can help identify the factors affecting accident concentration and how these results can be further used to define dynamic hotspots. For example logistics companies can use this information to avoid specific routes insurance companies can share this information with their clients and cities can assign their traffic police to the dynamic hotspots. ![]() Knowing the conditions under which accidents happen and where they happen is very powerful information that can be used to take action to avoid them. In this case it would be useful to identify this location as a hotspot only when those circumstances take place i.e. For example one location can have a relatively low concentration of accidents but all occurring under a same set of rare circumstances. However this information is available either post accident or it is static. ![]()
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