AI enhanced GIS solutions2022-03-07T12:51:52+01:00

Thinking Ahead: Artificial Intelligence & GIS

The creative disruption of location intelligence enhanced by AI

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To unlock the full predictive power of geo-demographic data our latest solutions use Artificial Intelligence.

Building on GeoX’s traditional strengths in GIS business solutions, incorporating Artificial Intelligence takes our location analytics to the next level.

We build models capable of handling enormous location-based datasets to predict outcomes. This approach ensures that your decisions are data driven and evidence based.

To put it very simply the task of the AI model is to identify patterns in the data based on all the parameters we provide it with. ​​We use several approaches to build our AI/ML models in our work, sometimes combining different models into a single ensembled model to provide more robust outcomes and higher overall accuracy.

Incorporating AI, primarily Machine Learning, into the analysis also means that we are not only looking for correlations evident to a human analyst, but that we uncover hidden patterns not visible to the human eye.

Use cases

The biggest strength of our approach is in our unique datasets. We possess the best, most granular and detailed spatial data for our region. This means that we are better positioned than others to build models yielding relevant, actionable outcomes. Both in the model building and the production system the AI/ML module works together with a built-in GIS engine to use all available spatial raw and calculated data.

Case Studies

Detailed description

The use of Artificial Intelligence allows for analyzing very large sets of variables, in this case spatial variables. The exact technical parameters vary based on the individual solution.

The spatial data is accessed accessed and pre-processed for the the AI module by a spatial motor capable of calculating the location dependent input variables for each location needed. This provides the general spatial analysis system that can receive raw spatial data for modeling through an API, as well as provide real time answers to the spatial requests of the model through a previously defined interface (API).

In general our team will use Scaled Evolutional Auto Machine Learning Agile approach when building our AI model. This is based on standard and widely used CRISP-DM methodology. Based on this the model building will consist of the following steps:

a)business understanding

b)data understanding, data preparation for modeling

c) Machine Learning based model building

d) refining the model and input data

e) validation and repeat of processes a-d as needed

f) production/ deployment

​​We use several approaches to build our AI/ML models in our work, sometimes combining different models into a single ensembled model to provide more robust outcomes and higher overall accuracy.

AI enhanced GIS analysis solutions are customized to the needs of each customer and so our pricing is individualized.

In general our prices are determined by the hours of work our analysts have to commit to the project and the type of data we use.

If you have any questions about our products or pricing please contact us

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