Planetary-scale answers, unlocked.
A Hands-On Guide for Working with Large-Scale Spatial Data. Learn more.
Authors
This guest post is from a Wherobots user George Chandeep Corea, which covers an exploration of how he used Wherobots MCP with his preferred AI coding tools to build the interactive AI data center site suitability analysis tool below. The following quote is from his own biography.
Below is a working national suitability report for AI data centers. Move the weight sliders and the ranking reorders. Toggle the tailings-dam scenario and the buildable area changes. Everything in it was computed on Wherobots over 15.91 million authoritative geometries drawn from 16 national and state portals, then published as a zero-dependency interactive document that opens in any browser with no compute environment behind it.
Have a look first. The rest of this post explains how it was built and how I shaped it, as well as where I plan to go next.
Open the full report in a new tab →
I learn by doing. I built this on a private basis to contribute to the community discourse. For me it is all about the data; any opinions, perceived or intentional, are my own and have nothing to do with my official job roles. All data is from public sources.
It covers 17 candidate industrial sites across all 8 Australian states and territories, spanning the National Electricity Market and the SWIS grid.
For me, this is a community and conservation-minded project: not about saying no to development, but about helping identify where development can create the most value with the least harm. The point is to make siting decisions more transparent, more constructive, and easier to discuss across stakeholders.
AI data centers are resource-intensive. They depend on power, water, land, and supporting infrastructure, and they can also create real pressures on ecosystems and communities. I wanted to build something that makes those tradeoffs visible instead of hiding them behind a single score or reports that sit on shelves and don’t let users see it through there lenses.
I wanted a platform that could handle real spatial scale without forcing me into a slow, fragile workflow. I had been hearing about it on LinkedIn and Youtube by following Matt Forrest and wanted to learn by doing.
This project queried 15.91 million authoritative geometries across 16 national and state/territory spatial datasets. At the regional modeling scale, the underlying NSW stack ingested 1,751,315 geometries across multiple cloud spatial tables, evaluating 4.92 million spatial join combinations. Using cloud-native GeoParquet inside Wherobots Havasu (Spatially Aware Apache Iceberg) tables, the storage footprint compressed from ~2.9 GB raw equivalent to ~430.7 MB — an 85.2% reduction.
That scale matters because suitability analysis only becomes useful when it can be rerun quickly as assumptions change.
Four different operations, four different numbers. They are not alternatives to each other: 2.4 s for the spatial SQL join execution (distributed joins + net developable area overlays across 1.75M+ geometries); 18.4 s → 3.2 s for the national scan across 15.91M geometries using Hilbert space-filling curve partitioning; 200.6 s for the cold end-to-end batch ETL (uncached ingest, GDA2020 reprojection, ST_MakeValid topology repair, Iceberg writes); and < 1 ms for the client-side What-If recalculation in the browser.
ST_MakeValid
I wanted to understand how far I could push a cloud spatial workflow while still producing something that a non-technical audience could review. The answer was to combine Wherobots for the heavy computation with a document-style HTML report for presentation.
The report is the public-facing layer of the analysis. It includes an interactive map, a ranked candidate leaderboard, benchmarking tables, provenance, methodology, and a what-if sandbox for changing ranking weights.
My recent work covers GDA2020 cadastral modernization at an enterprise level, environmental monitoring at a state level, and disaster modeling and high level coordination with FEMA. As the creator of *AuraSiting Crafter* (currently hunter_spatial crafter), I built an open-source multi-criteria engine benchmarking clean energy, water security, acoustic buffers, and developable land across 15.91 million Australian geometries using Wherobots MCP as the critical foundation. George Chandeep Corea Builder, Learner, Public Sector
Builder, Learner, Public Sector
The HTML report is where the analysis becomes useful for public review. It is not just a visualization — it is a way to interrogate the assumptions behind the ranking.
The map is interactive, searchable, and configurable. Readers can change basemaps, add WMS/WFS services, and bring in external layers to compare candidate sites against their own data or public reference layers (some functionality coming soon!). That matters because spatial decisions are rarely made from one dataset alone. Adding data will make the report more useful for collaboration, because different stakeholders can overlay the information that matters to them: local context, community knowledge, infrastructure plans, environmental constraints, or jurisdictional boundaries.
The sandbox (already available) is especially valuable because readers can adjust the ranking weights and immediately see how the candidate ordering changes. That makes the tradeoffs tangible instead of theoretical.
The pipeline is straightforward:
The scoring model uses many weights:
Those weights reflect the practical requirements of AI infrastructure while keeping environmental and resource considerations visible.
Suitability = 0.40·S_power + 0.25·S_sensitive + 0.20·S_water + 0.15·S_size
Power grid proximity (40%). Proximity to ≥132kV transmission substations. Full points between 100 m and 500 m; linear decay to 0.0 at 5 km; a 0.7 penalty under 100 m for EMF safety and acoustic isolation.
Sensitive receptor buffer (25%). A continuous sigmoidal decay around a 500 m compliance threshold: S_sensitive(d) = 1 / (1 + e^(-0.01·(d - 500))). Hard exclusion under 300 m; acoustic-barrier buffer penalty 300–500 m (0.10–0.50); compliant 500 m–1.5 km (0.50–1.0); optimal workforce balance 1.5–5 km (1.0); linear commute decay beyond 5 km.
S_sensitive(d) = 1 / (1 + e^(-0.01·(d - 500)))
Recycled water proximity (20%). Distance to wastewater treatment plants for sustainable evaporative cooling, decaying linearly from 1.0 at ≤1 km to 0.0 at ≥10 km.
Developable parcel size (15%). Contiguous flat buildable land. Parcels ≥15 ha score 1.0; parcels under 3 ha get a 0.1 baseline, with linear interpolation between.
Slope calculations exclude land above 5% grade to avoid excessive earthworks.
The national model integrates 16 authoritative datasets across all 8 Australian jurisdictions: Geoscape National Cadastre & G-NAF (15,420,800), ABS 2021 Meshblocks & UCL (368,290), National Sensitive Receptors from ACARA / NHSD / OSM (47,510), Geoscience Australia electricity grid (4,820), BoM & GA surface water and outfalls (42,100), and state planning/cadastral portals — NSW SEED, QLD QSpatial, Vicmap, Landgate (27,725). Total: 15,911,245 geometries.
Those totals are the available universe published across the portals — 47,510 POIs and 15.4M cadastral parcels are what ACARA, NHSD, OpenStreetMap and Geoscape publish, not what this pipeline processed. The ingested and audited cohort is 1.75M regional features in the Hunter deep-dive, 368,290 ABS meshblocks partitioned nationally, 17 indexed candidate sites, and a ground-truth QA sample of 33 sensitive receptors (19 schools via ACARA, 14 hospitals via NHSD) sitting inside the candidate industrial zones across all 8 states, used to calibrate the sigmoidal buffer decay curve.
Of the 17 candidates, 4 are micro-sited in detail in the NSW Hunter; the remaining 13 are simulated baselines across interstate transition hubs (Latrobe Valley VIC, Collie WA, Gladstone QLD).
All spatial tables are cataloged under org_catalog.fgsdb.* on Wherobots Cloud and persisted in cloud object storage, in GDA2020 / MGA Zone 56 (EPSG:7856).
org_catalog.fgsdb.*
To avoid real-time WFS/FeatureServer REST API timeouts during Spark runs, every dataset is ingested and hosted as an optimized cloud spatial Iceberg table rather than fetched live.
Building the net developable area mask — unioning riparian, pipeline, and rail buffers and subtracting them from the sub-precinct boundaries:
SELECT p.precinct_key, ST_Difference(p.geom, ST_Union_Aggr(c.geom)) AS net_developable_geom FROM precinct_transform p LEFT JOIN constraints c ON ST_Intersects(p.geom, c.geom) GROUP BY p.precinct_key, p.geom
Computing nearest distances to transmission substations and wastewater treatment outfalls:
SELECT mb.mb_code21, MIN(ST_Distance(mb.mb_geom, ST_Transform(p.geometry, 'EPSG:4326', 'EPSG:7856'))) / 1000.0 AS dist_to_substation_km, MIN(ST_Distance(mb.mb_geom, ST_Transform(w.geometry, 'EPSG:4326', 'EPSG:7856'))) / 1000.0 AS dist_to_wwtw_km FROM industrial_meshblocks mb CROSS JOIN org_catalog.fgsdb.macquarie_energy_infrastructure p CROSS JOIN org_catalog.fgsdb.macquarie_water_hydrography w GROUP BY mb.mb_code21
My background in conservation shaped this project. I’m not trying to use technology to say “no” to development. I’m trying to use it to ask better questions about where development belongs, where it creates value, and how to reduce unnecessary conflict between infrastructure, ecosystems, and communities.
That is why the report includes explicit constraint layers, benchmarking, and scenario controls. The aim is to support better judgment, not replace it.
Four things account for the query speed:
Metadata envelope pruning. Havasu Iceberg stores 2D bounding box envelopes directly inside Iceberg AVRO manifest files. Queries with spatial predicates such as ST_Intersects prune the large majority of irrelevant Parquet files at the metadata layer, before any raw disk bytes are scanned.
ST_Intersects
Hilbert curve spatial clustering. Geometries are sorted with 2D Hilbert space-filling curves during ingestion, so geographically adjacent features land in the same Parquet row groups and storage partitions. That removes random disk I/O seek overhead.
Vectorized memory execution. Apache Sedona operates directly on columnar GeoParquet WKB geometry buffers, avoiding serialization costs between Python, the Spark JVM, and native spatial drivers.
Parallel distributed spatial joins. Quad-tree and R-tree spatial indexes partition the query space across worker nodes, turning expensive O(N × M) cross-joins into O(N log M) parallel bucket joins.
Candidates are benchmarked against simulated local and regional baselines — Latrobe Valley in VIC, Collie in WA, Gladstone in QLD — to position NSW development opportunities within the wider national energy market transition.
The simulation sandbox runs entirely in the browser: no server calls, no network latency, no cloud API charges during a slider session.
The heavy spatial work — topological winding distances, buffer overlaps, elevation head drops, thermodynamic decay rates — is computed at build time on Wherobots and embedded in the report’s JSON payload. Moving a weight slider then normalizes the raw values so the weights sum to 1.0 and recalculates suitability across every candidate record in JavaScript, in under a millisecond. Toggling the tailings dam safety switch swaps pad areas between the declared and de-declared cases (+15.2 ha unlocked) and updates map polygons and audit cards without refetching any GeoJSON. Candidates re-sort by active score, updating the leaderboard, marker radii, and popup badges.
The payoff is threefold: no per-query cloud compute cost during interactive sessions, full interactivity when the HTML report is emailed or opened offline, and slider manipulation that stays smooth without waiting on network round-trips.
The following describes the author’s planned work, not shipped functionality.
Sensitive receptor scoring. The next version will score candidates against sensitive community receptors — schools and early childhood centers, hospitals and aged care, residential meshblocks, and workforce commute bands — to prevent acoustic, thermal, electromagnetic, and visual conflicts. The model uses a continuous sigmoidal penalty around a 500m critical setback threshold, with a workforce accessibility modifier that’s neutral in the 1.5km–5.0km commute band. In practice: under 300m is a critical exclusion, 300–500m carries a buffer penalty requiring an acoustic barrier, 500m–1.5km is compliant, 1.5–5.0km is the optimal community-and-workforce balance, and beyond 5km the score falls off for commute burden.
Grid and water policy analysis. Following the National Cabinet debate on AI data center power demand and regional grid security, the framework will evaluate proximity to 132kV, 330kV, and 500kV bulk transmission, flag candidates adjacent to retiring coal-fired stations that can reuse existing heavy transmission without expensive grid upgrades, map candidates against Renewable Energy Zones and firming assets for 24/7 clean energy matching, and restrict cooling supply strictly to recycled and wastewater sources — scoring zero for any site dependent on potable drinking water reserves or vulnerable aquifers. Candidates then sort into three tiers: grid-ready and fast-track eligible, conditional pending firming storage, or constrained by congestion and potable water reliance.
Open platform integration. To let the public explore these models interactively, hunter_spatial_crafter will integrate with opengeos/GeoLibre. The design point worth noting is zero duplication: Wherobots writes suitability layers to a central cloud bucket as GeoParquet and PMTiles, and GeoLibre’s in-browser DuckDB-WASM engine reads those exact same files over HTTP range requests — fetching only the row groups it needs, with no dataset conversion, no server-side copy, and no large client download.
hunter_spatial_crafter
Total cloud batch compute spend across dozens of iterative development, benchmark, and QA runs was ~$36 AUD (US$24.13) — roughly $1.03 per run across ~35 automated batch runs. Analyzing the execution profile shows how a production pipeline could cut that further:
Decouple heavy geometry joins from lightweight scoring. The pipeline splits into a compute-intensive geometric tier (ingest, GDA2020 reprojection, ST_MakeValid repair, 30 m/20 m buffers, ST_Difference overlays across millions of polygons) and a compute-light scoring tier (decay curves and weighted composites over precomputed distance attributes). Structured as a DAG with intermediate materialised GeoParquet stages, tuning a weight or the sigmoidal threshold never re-runs the heavy joins — only the downstream matrix recalculates.
ST_Difference
Fingerprint sources and memoize snapshots. Baseline layers change infrequently. Content hashing (ETags, GeoParquet file hashes, Iceberg snapshot manifest IDs) lets untouched tables be skipped, reading straight from cached Havasu Iceberg partitions.
Process delta partitions. When state portals publish quarterly cadastral updates, Iceberg’s ACID snapshot metadata lets WherobotsDB (optimized and managed Apache Sedona) isolate only modified parcel geometries instead of full continental scans.
Offload interactive compute to the client. Compiling precomputed distance topologies into the standalone report puts millions of interactive public scenario evaluations at $0.00 cloud compute cost.
Applied together, these reduce continuous CI/CD pipeline cost from ~$36 AUD to under $5 AUD.
This project taught me that Wherobots is more than a faster way to run spatial SQL. It is a way to make large-scale spatial analysis practical for real decision support. It gave me a path from raw geodata to a polished, reviewable spatial document — one that can be opened by a stakeholder, examined by a technical reviewer, and discussed in public.
For a personal project, that was the goal: learn the platform, test it at real scale, create something useful, and contribute to the broader discussion about responsible AI infrastructure.
If you’re interested in spatial analysis, cloud geospatial workflows, or responsible AI infrastructure, feel free to connect with me on LinkedIn.
Key takeaways
This project queried 15.91 million authoritative geometries across 16 national and state/territory spatial datasets. At the regional modeling scale, the underlying NSW stack ingested 1,751,315 geometries across multiple cloud spatial tables, evaluating 4.92 million spatial join combinations. The national scan across 15.91M geometries runs from 18.4 s down to 3.2 s using Hilbert space-filling curve partitioning.
Four things: metadata envelope pruning (Havasu Iceberg stores 2D bounding box envelopes directly inside Iceberg AVRO manifest files, so queries prune irrelevant Parquet files before any disk read); Hilbert curve spatial clustering (geographically adjacent features land in the same Parquet row groups); vectorized memory execution (Apache Sedona operates directly on columnar GeoParquet WKB geometry buffers); and parallel distributed spatial joins (quad-tree and R-tree indexes turn O(N × M) cross-joins into O(N log M) parallel bucket joins).
Total cloud batch compute spend across dozens of iterative development, benchmark, and QA runs was ~$36 AUD (US$24.13), roughly $1.03 per run across ~35 automated batch runs.
The heavy spatial work is computed at build time on Wherobots and embedded in the report’s JSON payload. Moving a weight slider normalizes the raw values so the weights sum to 1.0 and recalculates suitability across every candidate record in JavaScript, in under a millisecond. Toggling the tailings dam safety switch swaps pad areas between the declared and de-declared cases (+15.2 ha unlocked) and updates map polygons and audit cards without refetching any GeoJSON.
Suitability = 0.40·S_power + 0.25·S_sensitive + 0.20·S_water + 0.15·S_size. Power grid proximity is 40%, sensitive receptor buffer is 25%, recycled water proximity is 20%, and developable parcel size is 15%. Slope calculations exclude land above 5% grade to avoid excessive earthworks.
Measuring the Strait of Hormuz shutdown with Sentinel-2, WherobotsDB and Overture Maps
WherobotsDB read 719 Sentinel-2 scenes in place, fetched about 1.5 GB of the 151 GB, and joined the detections against Overture Maps land polygons to measure a 95% fall in traffic through the Strait of Hormuz corridor. Loading at Kharg and waiting at Fujairah held at 2025 levels, and the analysis cost about $63.
RasterFlow is now available in Public Preview
RasterFlow makes planetary-scale earth intelligence workflows easy and costs predictable. We are excited to announce that RasterFlow is now in Public Preview, opening up the power of planetary scale Earth Intelligence to all Wherobots Professional Edition customers! RasterFlow let’s you solve complex monitoring challenges with vision-language models or tailored models for specific use cases, without […]
How to score every building in a state for catastrophe risk: an exploration project
Part 1 of a series: What’s possible? I didn’t start this project to build a real risk model, not one that can be used by an insurer tomorrow. But it is a workflow that an insurer can put into practice to create their own risk scores with Wherobots and their own expertise. The idea was […]
share this article
Awesome that you’d like to share our articles. Where would you like to share it to:
