Managing And Analyzing Geospatial Big Data Using Cloud-Based Gis

The Challenges of Big Geospatial Data

Geospatial data such as satellite imagery, sensor data, and location tracking information are being generated in immense volumes. This big geospatial data presents opportunities for gaining valuable insights but also poses multiple challenges:

Storage and management of large, continuously growing datasets
Processing and analysis of data distributed across servers
Visualization of insights from billions of spatial data points
Ensuring security, privacy, and regulatory compliance

Traditional on-premise IT infrastructure often cannot cost-effectively handle big geospatial data. Cloud computing provides versatile, scalable, and economical solutions for working with massive geospatial datasets.

Cloud-Based Storage and Compute Solutions

Cloud Data Lakes

A cloud data lake is a central repository built on cloud object storage for storing any type of structured, semi-structured, and unstructured data. Geo-referenced data from sources like satellites, drones, sensors, GPS trackers, and geo-tagged social media can be ingested into cloud data lakes in native formats.

Table of Contents

Data lakes provide unlimited scalability, high durability, and low storage cost for accumulating vast amounts of geospatial data over time. Metadata catalogs enable organizations to locate, understand context, and utilize relevant subsets of data for analysis.

Serverless Compute

Serverless computing allows running event-driven code without managing backend servers. Cloud functions execute logic in response to events like new data landing in storage. This enables scalable, cost-efficient processing of real-time geospatial data as they arrive in cloud data lakes.

Serverless geo data processing avoids overhead from constantly running compute resources. Organizations pay only for the actual time code executes.

Working with Geospatial Data in Cloud Storage

Formats

Key geospatial data formats used in cloud storage:

GeoJSON – Open standard format for encoding geographic data structures like points, lines, polygons using JSON
Shapefiles – Vector storage format for storing location, shape and attributes of geographic features
Rasters – Matrix format storing images representing satellite data, aerial photos as pixels

These formats integrate location data (latitudes, longitudes, height info) with descriptive attributes about geographic entities. Storage options like cloud data lakes can handle growing volumes of such data.

Metadata Management

Metadata provides critical context on origin, definitions, inter-relationships, and ownership of geospatial data. Accurately tagging geo datasets with metadata makes discovery and governance over data assets more efficient.

Capabilities like data catalogs, tagging, and data lineage tracking in cloud data platforms enable properly documenting and sustaining metadata for geospatial data at any scale.

Analyzing Geospatial Data at Scale

Distributed Processing with Spark

Apache Spark allows large-scale, distributed processing of big geospatial data across clustered compute resources. Spark’s Resilient Distributed Datasets (RDDs) enable parallel execution of analysis tasks on geo data partitioned across nodes.

Spark ecosystem components like GeoMesa, GeoSpark, and Hadoop GIS extend capabilities for spatial querying, indexing, and geometry computations on big geo datasets.

Visualization Options

Cloud-hosted visualization tools like Power BI, Tableau, QGIS can generate insightful interactive maps, clusters, heat maps from vast geospatial data processed in platforms like Spark. These tools connect directly to cloud data platforms.

Dashboards can provide decision-makers real-time visibility into spatial trends, patterns, and hot spots updated continuously as new geo data arrives.

Example Analysis Workflow

Loading Data

Sample workflow for analyzing geospatial data on cloud infrastructure:

Ingest raw geo datasets (like GeoJSON files) into cloud data lake using batch uploads or streaming ingestion
Tag incoming data with metadata like coordinate reference systems and feature definitions

Performing Spatial Joins

Enrich geospatial data by combining attributes from related datasets using spatial joins:

Load geo datasets into Spark RDDs or data frames
Use Spark SQL or GeoSpark Spatial Join to combine geospatial data based on location attributes
Cache transformed datasets in memory to optimize analytics performance

Statistical Analysis

Derive insights from enriched geo data using statistical analysis:

Perform geospatial analysis like distance calculations, geometry transformations at scale
Conduct aggregate analysis using standard SQL or libraries like PySpark to reveal trends
Output results as new dataset files into cloud storage for visualization

Visualizing Hot Spots

Visualize outcomes in business-focused geospatial dashboards:

Connect Power BI to cloud storage and import processed geo data results
Plot layers of spatial entities onto custom maps
Flag hot spots and high-value locations using heat maps
Update dashboards dynamically to reflect latest outputs

Best Practices for Security and Compliance

Handling sensitive geospatial data in the cloud warrants precautions to safeguard confidentiality and ensure regulatory obligations:

Enable encryption for data at transit and at rest to prevent unauthorized access
Restrict exposure of storage access endpoints to only authorized networks and users
Set up user access controls, apply principle of least privilege to data access
Track all data access and changes through detailed audit logging
Comply with regulations like INSPIRE Directive by properly documenting all shared geospatial data

Cost Optimization for Cloud GIS

Steps for optimizing spending on cloud geospatial systems:

Choose cloud regions with lowest bandwidth pricing for moving bulk data sets
Use Infrequent Access for archival data to reduce storage costs
Scale down Spark cluster when not actively processing analytics
Enable auto-pause on Redshift clusters not being queried
Use serverless functions to avoid over-provisioning compute capacity