GeoAI: Esri and Microsoft Team up
for Better AI Applications
Deploying Esri’s ArcGIS Pro on Microsoft Azure brings artificial intelligent (AI), cloud technology, geospatial analytics and visualization together to help create more advanced and powerful AI applications.
Long-term business partners Microsoft and Esri have come together to offer the GeoAI Data Science Virtual Machine (DSVM) on Azure as part of the Microsoft’s Data Science Virtual Machine/Deep Learning Virtual Machine family of products. On the backend, ArcGIS Pro is the core technology running on Esri’s next-gen 64-bit desktop Geographic Information Systems (GIS) in the core of the virtual machine which provides professional 2D and 3D mapping in an intuitive user interface. ArcGIS Pro leaps a big step forward in advancing visualization, analytics, image processing, data management and integration.
In a survey, 93% of C-level executives said their companies are investing in AI and AI has already been put to help generate business intelligence in organizations worldwide. Unlike other technologies, the most prominent features of AI include its ability to keep learning from every type of data, predicting outcomes, making decisions and working without being programmed explicitly.
For example, online food delivery firm Deliveroo is using location data and machine learning to increase its business efficiency. Social media Snapchat has added a suite of location target features for its advertisers to offer more specific targeting information. Taxi drivers in Japan are using AI and location data to predict demand for a ride. All of above formulation and prediction rely heavily on the location data being fed into the machines for their “learning” [PDF].
Adding Location Data to Business Intelligence (BI)
The more relevant data we feed, the better the decision-making process becomes. As a result, it is widely suggested that we should start feeding spatial data to drive wider applications of AI. We can understand the importance of getting data feed into the AI applications as some analysts describe “data” as “new oil” and “oxygen”. But why location data are relevant data?
GIS, aided by spatial statistics and statistical tools embedded in ArcGIS Pro and Insights for ArcGIS to analyze the spatial data sets, can correlate and analyze location data in time and space and integrate them into many other types of information, including information in the AI machines, in turn provide organizations with additional and more relevant contextual information that enriches observations, leading to better predictions and decision making.
For example, a bank is considering building a new branch, and it hopes to maximize the operation efficiency and profits by choosing a suitable location. Finding a new location can be as simple as going where the competitors are. However, we can use AI and GIS to make a smarter decision as location data reveals contextual information for optimizing workflow and visualizing proximity relationship that could be missed in traditional graphs and charts. Azure, an AI-enabled platform with pre-installed ArcGIS Pro, can leverage the bank portfolio, bank surroundings, demographic data on where people live, their economic characteristics, and loan forecasting to help the bank make a data-backed decision for the next location of its branch.
The other example is some governmental organizations are using AI with location data to manage the traffic jam situation. In Kuwait, the Public Authority for Civil Information launches a mobile app that uses AI to perform analysis so as to predict the traffic in coming hours and release the traffic information to drivers on the road. The key to the analysis is that the AI algorithm can understand the concept of traffic conditions and patterns without explicit instructions from the programmers. Esri is working with other government agencies to look at how this combination of geography and AI, or GeoAI can be applied to tackle other issues, like food security, flu epidemics, and crime.
Filling the Gap between GIS Analysts and BI Roles
Integrating location data into AI to generate more useful business intelligence requires a close working relationship between GIS analysts and BI professionals. A recent study found that 66% of businesses consider location intelligence critical or very important to revenue growth. Business executives should collaborate with GIS analysts to perform spatial data analysis and develop suitable marketing campaigns. GIS analysts know how to create and use multilayered maps on granular level for data-backed decisions. The number of applications and opportunities created by GeoAI is simply unlimited.