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Automatic Area Mapper

Overview

The Automatic Area Mapper simplifies the process of associating point datasets with polygon datasets, enabling spatial analysis. This feature is designed for technical users building projects in CleverMaps. The mapping occurs automatically during data loading, making it easier to set up area-based data relationships.

The Automatic Area Mapper computes geometric intersections between points and polygons (areas) and appends the IDs of intersecting polygons to the points table. If an intersection exists, the corresponding polygon ID is added to the point record. For example, this could involve mapping customer locations to administrative units. Once mapped, users can calculate metrics such as the number of customers in each city district, city, or county, enabling insightful analyses in a spatial context.

How It Works

The Automatic Area Mapper:

  • Maps point datasets to one or more polygon datasets.

  • Requires the point dataset to include a foreign key dwh property with calculated set to true that references polygon dataset.

  • Requires the polygon dataset to include a dwh property of type geometry that describes the polygons.

Each point is mapped to a polygon on a 1:1 basis for each dwh property. Note that one point dataset may mapped to multiple polygon datasets, such as zsjd or cast_obec, based on the relevant dwh properties. For each point to polygon mapping you must define a foreign key dwh property with calculated set to true in point dataset and also each polygon dataset must contain dwh property of type geometry.

Edge cases

  1. Points without intersects with polygons: If a point does not fall within any polygon, its intersect value will be null.

    • These cases can be diagnosed using the validate command in CleverMaps Shell, which prints a message and provides sample IDs where mapping is null. This behavior may be acceptable in certain scenarios, such as mapping points outside predefined zones.

  2. Overlapping polygons: When multiple polygons overlap with each other, the mapper selects one polygon randomly. This behavior cannot be configured, and nulls are used as a last resort if no valid mapping exists.

Supported data formats

Dataset examples

These datasets demonstrate the required structure, with the dwh properties linking the point and polygon datasets. Note the dwh property with name neighborhood_code in point dataset with "calculated": true and dwh property with name WKT polygon in polygon dataset that has "type": "geometry". These are the properties that make the automatic area mapping happen.

Example of point dataset

CODE 
{
    "name": "customers",
    "type": "dataset",
    "title": "Customers",
    "description": "Customers registered in the loyalty program.",
    "properties": {
        "featureTitle": {
            "type": "property",
            "value": "customer_id"
        },
        "featureSubtitle": {
            "type": "property",
            "value": "address"
        }
    },
    "ref": {
        "type": "dwh",
        "subtype": "geometryPoint",
        "h3Geometries": [
            "h3_grid_6",
            "h3_grid_7",
            "h3_grid_8",
            "h3_grid_9"
        ],
        "visualizations": [
            {
                "type": "dotmap"
            },
            {
                "type": "heatmap"
            }
        ],
        "primaryKey": "customer_id",
        "categorizable": true,
        "fullTextIndex": false,
        "properties": [
            {
                "name": "customer_id",
                "title": "Customer ID",
                "column": "customer_id",
                "type": "integer",
                "filterable": false
            },
            {
                "foreignKey": "zsj_d_dwh",
                "name": "neighborhood_code",
                "title": "Neighborhood code",
                "column": "neighborhood_code",
                "type": "string",
                "filterable": false,
                "calculated": true
            },
            {
                "name": "city",
                "title": "City",
                "column": "city",
                "type": "string",
                "filterable": false
            },
            {
                "name": "address",
                "title": "Aaddress",
                "column": "address",
                "type": "string",
                "filterable": false
            },
            {
                "name": "sex",
                "title": "Sex",
                "column": "sex",
                "type": "string",
                "filterable": true
            },
            {
                "name": "age_group",
                "title": "Age group",
                "column": "age_group",
                "type": "string",
                "filterable": true
            },
            {
                "name": "lat",
                "title": "Address latitude",
                "column": "lat",
                "type": "latitude",
                "filterable": false
            },
            {
                "name": "lng",
                "title": "Address longitude",
                "column": "lng",
                "type": "longitude",
                "filterable": false
            }
        ],
        "zoom": {
            "min": 7,
            "optimal": 9,
            "max": 18
        }
    }
}

Example of polygon dataset

CODE 
{
    "name": "zsj_d_dwh",
    "type": "dataset",
    "title": "Zakladni sidelni jednotky",
    "properties": {
        "featureTitle": {
            "type": "property",
            "value": "nazev"
        },
        "featureSubtitle": {
            "type": "function",
            "value": "concat",
            "content": [
                {
                    "type": "text",
                    "value": "Municipality "
                },
                {
                    "type": "property",
                    "value": "upper_name"
                }
            ]
        }
    },
    "ref": {
        "type": "dwh",
        "subtype": "geometryPolygon",
        "geometry": "zsjd-9n74cw",
        "primaryKey": "kod_zsj_d",
        "categorizable": false,
        "fullTextIndex": false,
        "properties": [
            {
                "name": "kod_zsj_d",
                "title": "kod_zsj_d",
                "column": "kod_zsj_d",
                "type": "string",
                "filterable": true
            },
            {
                "name": "x_min",
                "title": "x_min",
                "column": "x_min",
                "type": "decimal(19,6)",
                "filterable": false
            },
            {
                "name": "x_max",
                "title": "x_max",
                "column": "x_max",
                "type": "decimal(19,6)",
                "filterable": false
            },
            {
                "name": "y_min",
                "title": "y_min",
                "column": "y_min",
                "type": "decimal(19,6)",
                "filterable": false
            },
            {
                "name": "y_max",
                "title": "y_max",
                "column": "y_max",
                "type": "decimal(19,6)",
                "filterable": false
            },
            {
                "name": "nazev",
                "title": "Name",
                "column": "nazev",
                "type": "string",
                "filterable": true
            },
            {
                "name": "popis",
                "title": "Description",
                "column": "popis",
                "type": "string",
                "filterable": false
            },
            {
                "foreignKey": "zsj_dwh",
                "name": "kod_zsj",
                "title": "kod_zsj",
                "column": "kod_zsj",
                "type": "string",
                "filterable": false
            },
            {
                "foreignKey": "cast_obec_dwh",
                "name": "kod_cast_obec",
                "title": "kod_cast_obec",
                "column": "kod_cast_obec",
                "type": "string",
                "filterable": false
            },
            {
                "name": "kod_zuj",
                "title": "kod_zuj",
                "column": "kod_zuj",
                "type": "string",
                "filterable": false
            },
            {
                "name": "upper_name",
                "title": "Upper name",
                "column": "upper_name",
                "type": "string",
                "filterable": false
            },
            {
                "name": "area",
                "title": "Area",
                "column": "area",
                "type": "decimal(19,6)",
                "filterable": true
            },
            {
                "name": "kod_pou",
                "title": "kod_pou",
                "column": "kod_pou",
                "type": "string",
                "filterable": false
            },
            {
                "name": "WKT polygon",
                "title": "WKT polygon definition",
                "column": "wkt",
                "type": "geometry",
                "filterable": true
            }
        ]
    },
    "dataSources": [
        {
            "licenceHolder": "RUIAN - ČÚZK",
            "licenceHolderUrl": "http://vdp.cuzk.cz/",
            "licenceUrl": "https://www.cuzk.cz/Uvod/Produkty-a-sluzby/RUIAN/RUIAN-(1).aspx"
        },
        {
            "licenceHolder": "Czech statistical office",
            "licenceHolderUrl": "https://www.czso.cz/csu/czso/home",
            "licenceUrl": "https://www.czso.cz/csu/czso/conditions_for_use_and_further_dissemination"
        }
    ]
}

CleverMaps Studio data model visualization

The point dataset customers is mapped to polygon dataset zsj_d_dwh and this reference is also visible in Data model visualization of your project. You can also combine automatic area mapping with H3 geometries like in this example.

image-20250126-153148.png

Loading Data

Loading data into CleverMaps follows the standard process for all datasets, using the CSV format. The key difference with the Automatic Area Mapper is that point datasets will have fewer columns, as calculated columns are added automatically during the loading process.

Example of data

Point data

CODE 
customer_id,city,address,sex,age_group,lat,lng
700415,Brno,U dráhy 545/9,Female,20-29,49.1383569,16.6554016
172694,Brno,Kameníčkova 1108/29,Male,20-29,49.2096947,16.5799253

Polygon data

CODE 
kod_zsj_d,x_min,x_max,y_min,y_max,nazev,popis,kod_zsj,kod_cast_obec,kod_zuj,upper_name,area,kod_pou,wkt
0000190,12.807847,12.849966,50.354597,50.386389,Abertamy,ZSJ-D,000019,000019,554979,Abertamy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2D4940CBBA7F2C44AF29403946B247A82D494015713AC956AF29407444BE4BA92D494053CA6B2574AF2940D8D5E429AB2D4940925D6919A9AF2940B954A52DAE2D494089EDEE01BAAF2940535BEA20AF2D494050FF59F3E3AF2940226FB9FAB12D4940B6A0F7C610B02940B5503239B52D4940DD2230D637B0294084640113B82D49404F2157EA59B029402FA2ED98BA2D4940D44334BA83B02940E09C11A5BD2D4940

Steps

  1. Upload polygon datasets first: Polygon datasets must be uploaded before point datasets to enable intersection calculations. Without polygons, no calculations can occur.

  2. Upload point datasets: After polygons are loaded, upload the point datasets. The system will automatically calculate the intersects and append the necessary data.

Automated processing

The Automatic Area Mapper recalculates intersects in the following scenarios:

  • Full load of point dataset: The entire point dataset is recalculated.

  • Incremental load of point dataset: The data is refreshed to reflect changes.

  • Full load of polygon dataset: The associated point dataset is recalculated.

  • Incremental load of polygon dataset: The associated point dataset is refreshed.

Technically speaking, the calculated properties are stored in materialized views, identifiable by the _mv suffix in table name. These views are refreshed whenever point or polygon data changes to ensure consistency.

Performance Considerations

  • Calculating intersects between points and polygons can slow down the data loading process, especially for large datasets.

  • To speed up loading polygon datasets, you can disable materialized view refreshing by using the skipRefreshingMaterializedViews option in CleverMaps Shell with the loadCsv command or when using the CleverMaps REST API. Note that disabling this refreshing of materialized view requires a full load of the point dataset afterward, as previously stored data will be deleted.

Validation and Troubleshooting

The validate command in CleverMaps Shell is the primary tool for identifying mapping issues. It checks for potential problems with intersections, missing calculated columns, missing data tables and other anomalies.

Was this helpful? Do you have any additional questions? Do not hesitate to contact us at support@clevermaps.io.

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