CARTO User Manual

CARTO User Manual

    Build a categories & bubbles visualization

    Understanding population distribution has important implications in a wide range of geospatial analysis such as human exposure to hazards and climate change or improving geomarketing and site selection strategies. In this tutorial we are going to represent the distribution of the most populated places by applying colours to each type of place and a point size based on the maximum population. Therefore, we can easily understand how the human settlement areas is distributed with a simple visualization that we can use in further analysis.

    Build a dashboard with a local CSV file

    In this tutorial we are going to showcase how to upload a local CSV file to your CARTO Data Warehouse and then use it to build an interactive dashboard with our map-making tool, Builder.

    Subscribe to public data from the Data Observatory

    In this tutorial we are going to showcase how to leverage the public data offering from our Data Observatory and use the data from a subscription to build an interactive dashboard in our map-making tool, Builder.

    Build an animated visualization with time series

    There is an increasing need for conservation and connection with nature in cities. In this sense, geospatial analysis plays an important role in the effective management of our natural resources. In this tutorial we are going to represent the distribution of tree species in the streets of San Francisco by color and we will add some interaction through widgets, which will allow us to explore the map by selecting targered filters of interest. In this example, filters are applied by specie and date of planting.

    Create a tileset and build a basic visualization

    Understanding the urban areas has important implications in a wide range of geospatial analysis, for example, to inform decision-makers in sectors such as urban planning. In this example we are creating a tileset in which each building in Madrid is represented by a polygon and each of them is assigned a graduated color from the lowest to the highest value of the gross floor area. This visualisation allows us to represent at a glance how the surface area in Madrid is distributed.

    Pinpoint new store locations closest to your customers

    Understanding & analyzing spatial data is critical to the future of your business. CARTO 3 Location Intelligence platform allows organizations to store, enrich,analyze & visualize their data to make spatially-aware decisions. In this example we are going to use points clustering to analyze how to find the best place to locate six stores in Portland city based on proximity to customers.