Use Cases for Improved Public Health Outcomes with GeoAI

big data fusion through geospatial technologies and ai for analysis

Leverage AI-driven geospatial intelligence for agile responses to dynamic public health challenges

Let us shape the future of public health together

Let's partner

Use Case 1: Disease Prediction

Use Case 2: Data Generation

Use Case 1: Predicting Disease Outbreaks

Imagine a world where predicting a health crisis is as straightforward as forecasting the weather. How could this change the future of public health?

A future where we can predict disease outbreaks as straightforward as predicting the weather.

I can solve your complex public health challenge

By fusing diverse big data using location.

Earth observation satellites
demographic studies
cultural settlements
POIs
landforms
land cover
weather and climate
and infrastructure

Then, analyze to find patterns, hot spots, and outliers, and model using machine learning to identify critical factors and predict disease outbreaks for improved planning and immediate intervention.

My comprehensive analysis workflow adheres to strict scientific and best practices to produce confident answers.

Data collection from diverse sources and storage
Data analysis and machine learning predictions
Results visualization to decision-makers
Improved public health outcomes

I Provide a Comprehensive Analysis Workflow

data analysis predictions with machine learning for public health

ai and geospatial technologies results to drive improved public health outcomes

Drive market leadership in public health with integrated AI and geospatial innovations.

You can click below to learn how I predicted disease outbreaks for the World Health Organization (WHO).

WHO Case Study

Use Case 2: Scale-up your dataset assets

We have all heard that data is the new oil. However, data collection is sometimes expensive, especially if you do it correctly. Also, our data is often unorganized, messy, out-of-date, and complicated to turn into useful information. How can we use AI and geospatial technologies to scale up our datasets to cover vast geographical areas while decreasing temporal gaps?

use AI and geospatial technologies to scale up your datasets and reduce temporal gaps

Scale-up your datasets geographic coverage and reduce temporal gaps.

AI and geospatial technologies can use data fusion and predictions to scale up the geographic coverage of your datasets, thereby increasing their value and effectiveness.

They can also greatly reduce expensive resource consumption in creating the datasets to allow for rapid creating and regeneration of datasets filling lengthy temporal gaps.

Techniques like those used by WorldPop to interpolate or infer data fills gaps expanding your data's effectiveness and multiplying potential applications and users exponentially.

Fuse data from satellites, IoT devices, social media, and smartphones.
Use geospatial technologies to process, filter, and interpolate data.
Use GeoAI to extract features from satellite imagery like houses, roads, trash dumping sites, musquito breeding sites, or any other feature of interest.
Use GeoAI to extract structured information or infer new data to fill gaps.
Use AI and geospatial technologies to automate tasks, allowing for the rapid regeneration of data updates.

GeoAI extracts human or natural features from remotely sensed data. It can extract buildings, cars, roads, paths, forest cover, archeological sites, water body boundaries, damaged facilities, illegal waste dumps, or other key features. Powering up your data collection from remotely sensed data means:

Scaling your data collection geographically over large areas.
Scaling your data collection temporally at regular intervals.
Being prepared to generate data in extreme events like natural and human-produced disasters.
Reduce intensive labor-intensive work.

A. Automated feature extraction

B. Upscaling aggregated data

C. Fill gaps in sparse data

GeoAI infers and interpolates high-resolution data from aggregate data. It transforms your standard country—or state-level education, healthcare, disease, poverty, etc. data into valuable, high-resolution, precise data, increasing its value and applications enormously. Other high-resolution data about contributing factors and GeoAI models are used to infer detailed data.

Utilize GeoAI to infer geospatial and temporal gaps in sparse or intermittent data. Detailed disease, demographic, patient, and environmental data collected for specific counties or states generates comprehensive coverage through GeoAI with contributing factor datasets.