Real-Time Gis Analysis For Decision Support Systems

What is Real-Time GIS Analysis?

Real-time geographic information system (GIS) analysis refers to the capability to immediately process and analyze streaming geospatial data to enable quick decision making. It combines real-time data feeds from sensors and devices with GIS analytics and modeling techniques to provide up-to-date situational awareness and insights.

The key advantage of real-time GIS analysis is the ability to detect patterns, trends and events as they occur and trigger responses immediately. This is extremely useful in time-sensitive operations such as disaster response, disease outbreak tracking and traffic monitoring where delays can be very costly.

Some examples of how real-time GIS analysis enables better decision making include:

Identifying optimal evacuation routes based on real-time traffic data during natural disasters
Dispatching first responders to exact incident locations by analyzing real-time 911 call data
Tracking disease outbreaks in real-time to target preventive measures
Monitoring assets in transit to mitigate supply chain disruptions

Real-Time Data Collection and Processing

The first step in enabling real-time GIS analysis is collecting and processing streams of geospatial data in real-time from various sensors, devices and other sources.

Sensors and Devices for Real-Time Data Capture

Common sensors and devices used to collect real-time location-based data include:

GPS trackers – collect real-time location data for vehicle fleets, shipments etc.
Smart meters – collect real-time data on resource consumption from connected meters
Traffic cameras and sensors – provide video feeds and congestion status data
Environmental sensors – collect real-time data like air/water quality, noise levels etc.
Drones – equipped with cameras and sensors, collect aerial real-time data
Satellites – provide streams of imagery and sensor data
Internet of things (IoT) devices – various connected devices collecting location-based data
Human/vehicle data – apps providing real-time location/sensor data

Transmitting and Aggregating Real-Time Data Feeds

The real-time data streams from the aforementioned sources need to be transmitted reliably to central servers for further aggregation and analysis. This involves setting up communication infrastructure like wireless internet networks, cellular networks, satellite links etc. and protocols for secure data transmission.

At the aggregation end, the streaming data is collected in a central data pipeline that aligns and packages it into analysis-ready datasets with necessary data attributes. This involves data cleaning, sorting, joining spatial datasets and efficient data storage using big data techniques.

Data Management Considerations

Managing real-time spatial data pipelines involves setting up processes to handle:

Scalability – The infrastructure must automatically scale up and down based on data velocity. Auto-scaling cloud infrastructure is well suited for this.
Data quality – Need mechanisms to filter noise, detect anomalies etc. to prevent errors downstream. Can use AI for smart data validation.
Data uncertainty – The accuracy of incoming real-time data must be assessed to qualify analyses. Statistical methods help quantify confidence levels.
Metadata management – Capture key contextual metadata on data sources, collection methods etc. for traceability.
Stream processing – Use distributed streaming platforms like Kafka, Spark Streaming etc. for analysis-ready data streams.
Data modeling – Leverage graph databases, timeseries DBs etc. best suited for interconnected real-time data.
Augmentation – Combine real-time data with historic datasets for improved insights.

Real-Time Visualization and Monitoring

Visualizing the real-time data feeds using interactive dashboards and maps enables monitoring the evolving situation on the ground.

Building Real-Time Dashboards and Visuals

GIS focused dashboard tools like ArcGIS Dashboards simplify building web apps with real-time maps, charts and graphics. Display relevant operational metrics updated live for quick scans.

Push Notifications and Alerts

Configure rules to trigger push notifications and SMS/email alerts from the dashboards based on real-time data thresholds. Alerts on critical events like accidents, congestion, power failures etc. enable rapid mobilization of field resources.

Analytics and Modeling

Sophisticated modeling and analysis of real-time data unlocks deep insights not possible with just visualization. Spatial analysis helps understand trends and relationships between interconnected events unfolding across geographies in real-time.

Statistical Analysis on Streaming Data

Specialized algorithms and platforms exist to run statistical analysis like aggregations, distributions, regressions etc. on constantly updating geospatial data streams.

Machine Learning for Real-Time Insights

By combining historical training data with real-time data feeds, machine learning models can continuously learn and adjust predictions in real-time. Useful for time-sensitive decision making across various domains like estimated traffic/crime levels, flood warnings, flight delays etc.

Example Use Cases

Here are some typical examples illustrating the value of real-time GIS analysis:

Traffic Monitoring and Routing

Analyze real-time and forecast traffic conditions based on streaming GPS data to dynamically calculate and route vehicles along the best possible routes while avoiding congestion and accidents.

Natural Disaster Early Warning Systems

Ingest real-time sensor data like rising water levels, seismic activity etc. into AI models combined with weather forecasts to generate accurate disaster predictions and warnings very early on to save lives.

Disease Outbreak Tracking

Continuously analyze real-time health clinic records, pharmacist prescriptions data and online symptoms search trends to identify disease outbreaks at inception and enable swift coordinated responses.

Supply Chain Logistics Optimization

Leverage real-time GPS tracking data from supply trucks combined with weather/traffic feeds to dynamically optimize delivery routes and inventory levels across supply chain nodes minimizing disruptions.

Implementation Challenges

Despite the immense benefits, adopting real-time GIS analysis has its technology and process level challenges.

System Architecture and Scalability

Choosing the right mix of cloud infrastructure, hardware and real-time GIS, database & analytics software is key to handling the volume, velocity and variety of spatial data streams in a secure, reliable and scalable manner. Auto-scaling serverless architectures work best.

Data Quality and Uncertainty

Real-time data streams prone to noise, errors and inconsistency due to multiple devices and environments pose data quality challenges. Statistical methods for dynamic sampling, smart interpolation etc. help overcome some limitations.

Latency and Performance Optimization

Network communication delays coupled with processing overheads impact how fast responses can be triggered from incoming real-time data. Performance profiling and benchmarks help optimize architecture.

Getting Started with Real-Time GIS

For organizations planning real-time GIS implementations, here is a quick guide to key technologies along with a sample solution walkthrough.

Open Source Technologies Overview

Leading open source options include:

Streaming – Kafka, Flink, Storm, Spark Streaming
Stream Processing – GeoMesa, ST-Hadoop
Data Stores – InfluxDB, Cassandra, PostGIS, TimescaleDB
Visualization & Monitoring – Grafana, Kibana
Web GIS Frameworks – GeoServer, MapServer, GeoDjango

Cloud Platforms and Services

Managed platform services like AWS IoT Core, Azure IoT Hub, and Google IoT Core help accelerate real-time GIS cloud development.

Sample Project Walkthrough

As an example, a real-time bus transportation analytics solution can leverage:

GPS trackers on buses streaming locations into AWS IoT Core
AWS Lambda functions preprocess and store data into Amazon Timestream
Timestream provides geospatial querying capabilities
Combined historical and real-time data analyzed using Jupyter notebooks
Interactive real-time dashboard built with GeoServer and OpenLayers
Grafana setup to visualize KPIs and configure alerts

The reference architecture provides a scalable, managed and cloud-native implementation in a cost-effective serverless deployment.