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Site Selection 1

Site Selection - Geospatial Analtycs

In this straightforward example, we perform a typical geospatial analysis: Site Selection.
We want to open a new Italian restaurant in one of the most competitive markets, New York.
Five commercial spaces available have passed a first human screening, considering the budget and the position(Manhattan).
But now we need some geospatial data to make a solid decision on where to open our Restaurant.
To do this, we use the new KNIME extension Geospatial Analytics, with a bunch of nodes that allow us to retrieve spatial statistics. Specifically, we want to know the following:

How many Italian restaurants are already near each commercial space available (within a 300m Area)?
To answer this question, we use the Open Street Map Point of Interests node (OSM POIs) and the Buffer node to define the radius area.

What are the principal inhabitants features of each Area? What is the socio-economic level, etc.? Are there students?
To fetch this data, the Open Datasets nodes US2020 Census Data and US2020 TIGER Map came to help us.

Then we put it together, generating five potential areas with associated geospatial data. We use this data to calculate a dynamic weighted Score to help us to make the correct decision.

Finally, using the Geospatial View node, we can visualize the five areas with all the metrics and the other Italian restaurants, assigning a colour based on the previously calculated weighted Score. The green the better is our Area to open our new Italian Restaurant.

Geospatial Analytics is fully developed in Python, e.g. the Geopandas library, which was heavily used to write the nodes. All the nodes provided with the extension are the perfect toolkit to apply geospatial technologies in a no-code/low-code way, so also beginners can benefit from this kind of analysis.


Site Selection - Geospatial Analytics In this example, we perform a prevalent geospatial analytics task: Site selection. This is a crucial task to measure the merits of potential locations. For further information, refer to the workflow description. Click View > Description. Table Creator contains five potential on-sale locals in Manhattan to place our new Italian Restaurant with address, neighbourhood, latitude, and longitude. Using Open Datasets nodes from the Geospatial extension to retrieve Us 2020 census data with geospatial information for the Manhattan area. Each on-sale commercial space is an Area centre, and applying a buffer with a radius (meter) generates a target area. This, combined with the OSM POIs (Points of Interest) node, lets us know how many Italian restaurants, our competitors, are in the area. Us Census 2020 data for NYC is grouped by tracts, which are small statistical subdivisions of a county. Each parcel has demographic information from the Census. Overlaying the Target Area created above with Manhattan's lots, we append demographic data to the restaurant's in-area information. 1) Avalaible commercial Spaces and competitors are represented as points in the Map using the Geospatial View node. 3) Final visualization: the five areas and the points are concatenated and visualized in the Geospatial View node. 2) The five areas with competitors' information and demographic data represented with the Geospatial View node. The final output are five areas within a radius with competitors' information and demographic data. Available CommercialSpaces in NYCfor my Italian restaurantSpatial DataChange CRSDefine how big is yourTarget AreaFetching New Yorkcensus data Enter your Census API key (https://bit.ly/3iuc0f1)Census CodesrenamingAdding GeometryData to CensusDataNYC GeometryDataOverlaying Target Areasto NYC Census DataGeometry Polygonsfor NYCVisualizeTarget Areaswith Census DataVisualize All Restaurants (Points)in the MapConcatenate and Visualize Restaurants pointsand Target Areas withCensus DataRight-click> Interactive ViewOSM POIsto fetch competitorsrestaurants in the Area Table Creator Lat/Lon to Geometry Projection Buffer US2020 Census Data Column Rename Joiner Column Filter US2020 TIGER Map Overlay Column Rename Geospatial View Geospatial View Site Selection View #Restaurants indefined Areas Calculating AreaRestaurant Features Site Selection - Geospatial Analytics In this example, we perform a prevalent geospatial analytics task: Site selection. This is a crucial task to measure the merits of potential locations. For further information, refer to the workflow description. Click View > Description. Table Creator contains five potential on-sale locals in Manhattan to place our new Italian Restaurant with address, neighbourhood, latitude, and longitude. Using Open Datasets nodes from the Geospatial extension to retrieve Us 2020 census data with geospatial information for the Manhattan area. Each on-sale commercial space is an Area centre, and applying a buffer with a radius (meter) generates a target area. This, combined with the OSM POIs (Points of Interest) node, lets us know how many Italian restaurants, our competitors, are in the area. Us Census 2020 data for NYC is grouped by tracts, which are small statistical subdivisions of a county. Each parcel has demographic information from the Census. Overlaying the Target Area created above with Manhattan's lots, we append demographic data to the restaurant's in-area information. 1) Avalaible commercial Spaces and competitors are represented as points in the Map using the Geospatial View node. 3) Final visualization: the five areas and the points are concatenated and visualized in the Geospatial View node. 2) The five areas with competitors' information and demographic data represented with the Geospatial View node. The final output are five areas within a radius with competitors' information and demographic data. Available CommercialSpaces in NYCfor my Italian restaurantSpatial DataChange CRSDefine how big is yourTarget AreaFetching New Yorkcensus data Enter your Census API key (https://bit.ly/3iuc0f1)Census CodesrenamingAdding GeometryData to CensusDataNYC GeometryDataOverlaying Target Areasto NYC Census DataGeometry Polygonsfor NYCVisualizeTarget Areaswith Census DataVisualize All Restaurants (Points)in the MapConcatenate and Visualize Restaurants pointsand Target Areas withCensus DataRight-click> Interactive ViewOSM POIsto fetch competitorsrestaurants in the Area Table Creator Lat/Lon to Geometry Projection Buffer US2020 Census Data Column Rename Joiner Column Filter US2020 TIGER Map Overlay Column Rename Geospatial View Geospatial View Site Selection View #Restaurants indefined Areas Calculating AreaRestaurant Features

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

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Created by: diego.rodriguez
Created at: 2022-12-05
On NodePit since: 2023-09-02
Last update: 2024-11-23
Created with KNIME version: v4.7.0
Tags: Site SelectionSpatial AnalysisGis AnalysisRelease 4.7Geospatial Analytics

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