Make a heatmap showing population concentration in earthquake-affected zones

Make a heatmap showing population concentration in earthquake-affected zones

Task conventions: Use the frozen raster to select source points. Use the released reference's explicit US-raster method, not a global damage assessment: sample the original USA 2020 WorldPop population-count band at each original frozen earthquake point, using its containing original cell with no interpolation or resampling. Eligible points have a finite count at least zero. Include valid zero counts; off-grid, masked or nonfinite samples are unknown, not zero. Weight each eligible event by its sampled people-per-source-cell count. Repeated events can weight the same population repeatedly: this is an event-location population context heatmap, NOT unique affected population, casualty estimates, shaking extent or a claim that all residents experienced damage. The event file is the original 2025-01-16 through 2025-02-15 snapshot; the raster is 2020. This US coverage and the meaning of affected zones are explicit benchmark-derived adaptations of the vague question. Use original point records and the declared attribute/raster selection; do not infer a separate geographic boundary. Unknown country indicators, nonpositive ratio denominators, missing geometry and missing/nonfinite weights are unknown, not zero. Known zero weights remain valid. Exclude valid points outside the specified geography. Include only eligible points with a finite nonnegative weight in the contributing artifact; report other potentially eligible points as unknown. Use this explicit geographic heatmap convention: grid={"bounds": [-18000000, -7325000, 18000000, 7325000], "crs": "EPSG:6933", "resolutionX": 25000, "resolutionY": 25000}, radius 150000 metres = three Gaussian standard deviations. First bin each point into its containing grid cell and sum its weight. Smooth using a normalized separable Gaussian, numerical support four standard deviations, constant-zero exterior; do not renormalize edges. Use both grid resolutions for the two axes; keep original grid alignment. This is a declared metric raster adaptation to the original interactive screen-pixel heatmap, not an equivalent zoom-dependent rendering. Output unit: sum of people per grid cell. Zero cells are valid; this is not density per square kilometre. Do not rescale the values for presentation.



Add the heatmap raster to the map and retain an inspectable selected-point artifact with original geometry and benchmark_row_id. End with one fenced JSON object: {count: contributing point records, unknown_count: records with unknown eligibility or weight, unlocated_count: original points lacking valid geometry, coverage_note: string, selection: {collectionId,itemId,assetKey}, density: {collectionId,itemId,assetKey}, map_layer_id: heatmap_layer_id}. Explain the data edition, numerical weights, grid, smoothing and coverage limitations. The source-point artifact is not itself the requested heatmap.

Delivery format: provide the answer object with all fields specified above as final_response on assess_result, alongside the inspected final artifact_refs. This replaces the JSON-code-block formatting requirement above. Blue attaches your explicit selection to the accepted final answer. Finish with a concise ordinary-language answer agreeing with that selected result; do not duplicate the JSON in chat.

Supplied immutable inputs:
[
  {
    "name": "Earthquake points, frozen 30-day snapshot",
    "edition": "Archive labelled Feb142025; observed event timestamps 2025-01-16 through 2025-02-15 UTC",
    "units": {},
    "collectionId": "blue-generated--[resource id withheld]",
    "itemId": "[resource id withheld]",
    "assetKey": "data"
  },
  {
    "name": "USA population count, WorldPop 2020",
    "edition": "2020 UN-adjusted population counts aggregated to 1 km cells",
    "units": {
      "band1": "people per cell"
    },
    "collectionId": "blue-generated--[resource id withheld]",
    "itemId": "[resource id withheld]",
    "assetKey": "data"
  }
]

{
  "expectedCount": 7270,
  "actualCount": 7270,
  "expectedMetrics": null,
  "actualMetrics": null,
  "computationCorrect": false,
  "answerFulfilled": false,
  "adjudication": null,
  "qualification": null,
  "reasons": [
    "Heatmap units do not match the declared weights per cell."
  ]
}

What fields are present on the frozen earthquake points for benchmark eligibility and row identity?

What fields are present on the frozen earthquake points for benchmark eligibility and row identity?

The frozen source has 9,176 original point records, all with valid Point geometry and benchmark_row_id populated. The inspected schema shows 23 fields, including benchmark_row_id, longitude and latitude. I did not observe a separate country-indicator or ratio-denominator field in this source sample, so the benchmark eligibility test should rely on the original point geometry plus the sampled WorldPop value rather than an added geographic attribute filter.

How does original-cell WorldPop sampling appear on the earthquake points, including off-grid or unknown cases?

Sampling the original WorldPop USA 2020 raster onto the original earthquake points added a numeric `population` field to all 9,176 records. The sampled field has a minimum of 0.0, a maximum of 6862.3154296875, and 1,906 null values. Sample rows show off-coverage or masked locations remain null rather than being filled with zero, while the distribution confirms valid zero-valued samples are present. This supports using finite nonnegative sampled values as eligible weights and treating null sampled values as unknown.

Does the full fixed-grid method produce the required contributing-point selection and weighted heatmap from finite nonnegative sampled values?

The full fixed-grid method produced both requested analytical objects. Filtering the sampled points with population not null and population >= 0 yielded 7,270 contributing records with Point geometry, benchmark_row_id present, and population values ranging from 0.0 to 6862.3154296875. This exactly matches 9,176 total source points minus 1,906 unknown sampled points, so no negative finite weights were observed. The weighted point-density output used the declared EPSG:6933 grid, 25,000 metre cells, and radius 150,000 metres, producing a nonempty raster with valid zero cells, full-grid valid coverage, and producer observations that match the required point-density interpretation and edge behavior.

I produced the requested fixed-grid population-context heatmap and the inspectable contributing-point artifact. Data edition: the earthquake source is the frozen USGS earthquake snapshot labelled Feb142025 with observed event timestamps 2025-01-16 through 2025-02-15 UTC, hosted by Blue and retrieved in this run from the supplied immutable item at 2026-10-07T07:11:39Z; the population source is the supplied WorldPop USA 2020 UN-adjusted population-count raster, hosted by Blue from WorldPop/CIESIN and retrieved in this run at 2026-10-07T07:11:44Z. Numerical weights: each original earthquake point was weighted by the sampled WorldPop 2020 people-per-source-cell value from the containing original raster cell, with valid zero values retained. Coverage and eligibility: 7,270 original earthquake points had finite nonnegative sampled values and contributed; 1,906 original points remained unknown under the USA raster coverage rule and did not contribute; 0 original points lacked valid geometry. Heatmap method: the final raster uses the declared EPSG:6933 grid with bounds [-18000000,-7325000,18000000,7325000], 25,000 metre cells, and Gaussian smoothing radius 150,000 metres interpreted as three sigma with four-sigma numerical support, constant-zero exterior, and no edge renormalization. The final heatmap artifact is on the map as 'Population-context heatmap' layer [resource id withheld].