Smart city sensors

Sample: Smart city sensors, Krakow: report

Project: Sample: Smart city sensors, Krakow
Goal: Describe the project Sample: Smart city sensors, Krakow: run the analyses that tell the most about its data, draw every result, and write a cited report.
Run: 036a3e58-f6a2-4c67-bf86-aaa45b6b5e6f (running, 11 agent run(s))
Generated: 2026-10-18T06:32:42Z, version 1
Model cost: USD 0.0000 over 0 model call(s)

Data used

Timeline

The timeline counts 4,449 dated events in Sensor readings every three hours, Heating and smoke complaints, Traffic counts by day and 1 more per week, from week of 2025-09-29 to week of 2026-03-30 12345. By dataset: Sensor readings every three hours 3,024; Heating and smoke complaints 1,000; Traffic counts by day 420; Smog alert areas 5 1. The trend is +6.72 events per week; the second half of the weeks has 4,016 against 385 in the first (+943.1%) 1.

Busiest weeks

• week of 2026-01-19: 1,260 events 1
• week of 2026-01-12: 1,249 events 1
• week of 2026-01-26: 1,185 events 1

Quietest weeks

• week of 2026-03-30: 12 events 1
• week of 2025-09-29: 13 events 1
• week of 2025-10-06: 21 events 1

Space time hot spots

What this analysis is. Emerging hot spots: Where hot spots are new, consecutive, intensifying, persistent, diminishing, sporadic, oscillating or historical over time 6. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube 6.

Why it was run on this data. Heating and smoke complaints carry dates in 'reported_at': emerging hot spot analysis finds new, intensifying and fading hot spots per week 7.

What it found. Space time hot spots on Heating and smoke complaints: Emerging hot spots: 1 layer saved: Emerging hot spots: Heating and smoke complaints (79 features) 89. It reported result, count 79; result, elapsed ms 13; result, resolution 8; result, features used 1,000; result, time neighbors 0; billing, cost 0.15 8.

Findings

• Evidenced: Space time hot spots on Heating and smoke complaints: Emerging hot spots: 1 layer saved: Emerging hot spots: Heating and smoke complaints (79 features) 89.
• Evidenced: It reported result, count 79; result, elapsed ms 13; result, resolution 8; result, features used 1,000; result, time neighbors 0; billing, cost 0.15 8.

A model draft was discarded because a sentence did not cite a source of this section.

Tool gp_space_time_hot_spots; parameters {"project": "941056ab-e59e-43ce-98fd-5b3678626f78", "features": {"layer": "Heating and smoke complaints (872e2359)"}, "interval": "week", "bin_shape": "h3", "resolution": 8, "time_field": "reported_at"}

Near repeat

What this analysis is. Repeat and near repeat classification: Classify incidents as originators, repeats (same place) or near repeats within a distance and time window, with a Knox test table 10. In ArcGIS it is Repeat and Near Repeat Classification or Near Repeat Calculator 10.

Why it was run on this data. Near repeat analysis tests whether Heating and smoke complaints close in space also follow each other within 18 days 11.

What it found. Near repeat on Heating and smoke complaints: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Heating and smoke complaints (1000 features) 912. It reported result, days 18; result, count 1,000; result, chains 118; result, distance m 200; result, elapsed ms 21; result, pairs linked 557 12.

Findings

• Evidenced: Near repeat on Heating and smoke complaints: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Heating and smoke complaints (1000 features) 912.
• Evidenced: It reported result, days 18; result, count 1,000; result, chains 118; result, distance m 200; result, elapsed ms 21; result, pairs linked 557 12.

Tool gp_near_repeat; parameters {"days": 18, "project": "941056ab-e59e-43ce-98fd-5b3678626f78", "features": {"layer": "Heating and smoke complaints (872e2359)"}, "distance_m": 200.0, "time_field": "reported_at"}

Summarize within

What this analysis is. Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons 13. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths 13.

Why it was run on this data. Count Schools inside each feature of Smog alert areas; the counts map as a choropleth (natural breaks, sequential colors) 14.

What it found. Summarize within on Schools, Smog alert areas: Summarize within: 1 layer saved: Summarize within: Smog alert areas (5 features) 151617. It reported result, count 5; result, elapsed ms 1; billing, cost 0.01112; billing, list price 0.01112; billing, upstream cost 0 15.

Findings

• Evidenced: Summarize within on Schools, Smog alert areas: Summarize within: 1 layer saved: Summarize within: Smog alert areas (5 features) 151617.
• Evidenced: It reported result, count 5; result, elapsed ms 1; billing, cost 0.01112; billing, list price 0.01112; billing, upstream cost 0 15.

Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "pupils"}], "project": "941056ab-e59e-43ce-98fd-5b3678626f78", "features": {"layer": "Schools (04161d9b)"}, "polygons": {"layer": "Smog alert areas (cb491d16)"}}

Summarize within

What this analysis is. Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons 13. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths 13.

Why it was run on this data. Count Schools inside each feature of Smog alert areas; the counts map as a choropleth (natural breaks, sequential colors) 14.

What it found. The process involved using the gp_summarize_within tool 18 to summarize data using smog alert areas 17 and traffic counters 19.

Findings

• Evidenced: Summarize within on Traffic counters, Smog alert areas: Summarize within: 1 layer saved: Summarize within: Smog alert areas (5 features) 171819.
• Evidenced: It reported result, count 5; result, elapsed ms 0; billing, cost 0.01052; billing, list price 0.01052; billing, upstream cost 0 18.

Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "lanes"}], "project": "941056ab-e59e-43ce-98fd-5b3678626f78", "features": {"layer": "Traffic counters (2beeba9d)"}, "polygons": {"layer": "Smog alert areas (cb491d16)"}}

Hot spots

What this analysis is. Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins 20. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) 20.

Why it was run on this data. 50 points: Getis-Ord Gi* tests whether the concentrations in Schools are statistically significant 21.

What it found. Hot spot analysis was run using the Schools layer as input 16, saving 1 layer with 18 features 22.

Findings

• Evidenced: Hot spots on Schools: Hot spot analysis: 1 layer saved: Hot spot analysis: Schools (18 features) 1622.
• Evidenced: It reported result, bins 18; result, count 18; result, elapsed ms 4; result, cell size m 2,000; result, incidents used 50; result, distance band m 2,000 22.

Tool gp_hot_spots; parameters {"project": "941056ab-e59e-43ce-98fd-5b3678626f78", "features": {"layer": "Schools (04161d9b)"}, "aggregation": "hexagon", "cell_size_m": 2000.0, "distance_band_m": 2000.0}

Hot spots

What this analysis is. Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins 20. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) 20.

Why it was run on this data. 1000 points: Getis-Ord Gi* tests whether the concentrations in Heating and smoke complaints are statistically significant 23.

What it found. The hot spot analysis was performed using the Heating and smoke complaints layer 9. This process saved one layer titled Hot spot analysis: Heating and smoke complaints, containing 208 features 24. The analysis utilized 1,000 incidents and generated 208 results 24.

Findings

• Evidenced: Hot spots on Heating and smoke complaints: Hot spot analysis: 1 layer saved: Hot spot analysis: Heating and smoke complaints (208 features) 924.
• Evidenced: It reported result, bins 208; result, count 208; result, elapsed ms 12; result, cell size m 500; result, incidents used 1,000; result, distance band m 500 24.

Tool gp_hot_spots; parameters {"project": "941056ab-e59e-43ce-98fd-5b3678626f78", "features": {"layer": "Heating and smoke complaints (872e2359)"}, "aggregation": "hexagon", "cell_size_m": 500.0, "distance_band_m": 500.0}

Calculate density

What this analysis is. Calculate density: Kernel density per square kilometer on a grid 25. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation 25.

Why it was run on this data. 50 points in Schools: a density surface shows where they concentrate 26.

What it found. The calculation of density was performed using the Schools layer 16 and the gp_calculate_density tool 27. This process successfully analyzed 98 features 27. The maximum density detected over the area was 1.407 per km^2 27. Overall, the billing cost associated with this operation was 0.051 27.

Findings

• Evidenced: Calculate density on Schools: Calculate density: 1 layer saved: Calculate density: Schools (98 features) 1627.
• Evidenced: It reported result, count 98; result, elapsed ms 5; result, bandwidth m 2,000; result, cell size m 1,000; result, excluded null 0; result, max density per km2 1.407 27.

Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "941056ab-e59e-43ce-98fd-5b3678626f78", "features": {"layer": "Schools (04161d9b)"}, "bandwidth_m": 2000.0, "cell_size_m": 1000.0}

Calculate density

What this analysis is. Calculate density: Kernel density per square kilometer on a grid 25. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation 25.

Why it was run on this data. 20 points in Traffic counters: a density surface shows where they concentrate 28.

What it found. Calculate density on Traffic counters: Calculate density: 1 layer saved: Calculate density: Traffic counters (208 features) 1929. It reported result, count 208; result, elapsed ms 6; result, bandwidth m 5,000; result, cell size m 1,000; result, excluded null 0; result, max density per km2 0.4822 29.

Findings

• Evidenced: Calculate density on Traffic counters: Calculate density: 1 layer saved: Calculate density: Traffic counters (208 features) 1929.
• Evidenced: It reported result, count 208; result, elapsed ms 6; result, bandwidth m 5,000; result, cell size m 1,000; result, excluded null 0; result, max density per km2 0.4822 29.

Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "941056ab-e59e-43ce-98fd-5b3678626f78", "features": {"layer": "Traffic counters (2beeba9d)"}, "bandwidth_m": 5000.0, "cell_size_m": 1000.0}

Calculate density

What this analysis is. Calculate density: Kernel density per square kilometer on a grid 25. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation 25.

Why it was run on this data. 1000 points in Heating and smoke complaints: a density surface shows where they concentrate 30.

What it found. The calculation of density for heating and smoke complaints generated 1,379 features 31. This operation had a resulting elapsed time of 41 ms 31. Key metrics provided include a cell size of 200 m and an excluded null count of 0 31. The maximum density recorded was 120.2 per km2 31.

Findings

• Evidenced: Calculate density on Heating and smoke complaints: Calculate density: 1 layer saved: Calculate density: Heating and smoke complaints (1379 features) 931.
• Evidenced: It reported result, count 1,379; result, elapsed ms 41; result, bandwidth m 500; result, cell size m 200; result, excluded null 0; result, max density per km2 120.2 31.

Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "941056ab-e59e-43ce-98fd-5b3678626f78", "features": {"layer": "Heating and smoke complaints (872e2359)"}, "bandwidth_m": 500.0, "cell_size_m": 200.0}

Find point clusters

What this analysis is. Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise 32. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering 32.

Why it was run on this data. DBSCAN finds dense groups of Schools and separates noise 33.

What it found. Find point clusters on Schools: Find point clusters: 2 layers saved: Find point clusters: Schools (50 features); Find point clusters: cluster hulls (7 features) 1634. It reported result, count 50; result, noise 10; result, distance m 1,000; result, elapsed ms 4; result, min points 3; billing, cost 0.051 34.

Findings

• Evidenced: Find point clusters on Schools: Find point clusters: 2 layers saved: Find point clusters: Schools (50 features); Find point clusters: cluster hulls (7 features) 1634.
• Evidenced: It reported result, count 50; result, noise 10; result, distance m 1,000; result, elapsed ms 4; result, min points 3; billing, cost 0.051 34.

Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "941056ab-e59e-43ce-98fd-5b3678626f78", "features": {"layer": "Schools (04161d9b)"}, "distance_m": 1000.0, "min_points": 3}

Find point clusters

What this analysis is. Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise 32. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering 32.

Why it was run on this data. DBSCAN finds dense groups of Traffic counters and separates noise 35.

What it found. Find point clusters on Traffic counters: Find point clusters: 2 layers saved: Find point clusters: Traffic counters (20 features); Find point clusters: cluster hulls (1 feature) 1936. It reported result, count 20; result, noise 1; result, distance m 2,000; result, elapsed ms 1; result, min points 3; billing, cost 0.0504 36.

Findings

• Evidenced: Find point clusters on Traffic counters: Find point clusters: 2 layers saved: Find point clusters: Traffic counters (20 features); Find point clusters: cluster hulls (1 feature) 1936.
• Evidenced: It reported result, count 20; result, noise 1; result, distance m 2,000; result, elapsed ms 1; result, min points 3; billing, cost 0.0504 36.

A model draft was discarded because a sentence did not cite a source of this section.

Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "941056ab-e59e-43ce-98fd-5b3678626f78", "features": {"layer": "Traffic counters (2beeba9d)"}, "distance_m": 2000.0, "min_points": 3}

Find point clusters

What this analysis is. Find point clusters: Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise 32. In ArcGIS it is Find Point Clusters or Density-based Clustering; in QGIS, native:dbscanclustering or native:stdbscanclustering 32.

Why it was run on this data. DBSCAN finds dense groups of Air quality sensors and separates noise 37.

What it found. Find point clusters on Air quality sensors: Find point clusters: 2 layers saved: Find point clusters: Air quality sensors (18 features); Find point clusters: cluster hulls (1 feature) 3839. It reported result, count 18; result, noise 0; result, distance m 2,000; result, elapsed ms 1; result, min points 3; billing, cost 0.05036 38.

Findings

• Evidenced: Find point clusters on Air quality sensors: Find point clusters: 2 layers saved: Find point clusters: Air quality sensors (18 features); Find point clusters: cluster hulls (1 feature) 3839.
• Evidenced: It reported result, count 18; result, noise 0; result, distance m 2,000; result, elapsed ms 1; result, min points 3; billing, cost 0.05036 38.

Tool gp_find_point_clusters; parameters {"method": "dbscan", "project": "941056ab-e59e-43ce-98fd-5b3678626f78", "features": {"layer": "Air quality sensors (b023a603)"}, "distance_m": 2000.0, "min_points": 3}

Outliers

What this analysis is. Find outliers: Anselin local Moran’s I: HH, LL clusters and HL, LH outliers 40. In ArcGIS it is Find Outliers or Cluster and Outlier Analysis (Anselin Local Moran’s I) 40.

Why it was run on this data. Local Moran's I maps clusters and outliers of 'population' 41.

What it found. The outlier analysis utilized the Districts layer as input 43. The process successfully located 12 features within the Districts dataset 42.

Findings

• Evidenced: Outliers on Districts: Find outliers: 1 layer saved: Find outliers: Districts (12 features) 4243.
• Evidenced: It reported result, count 12; result, elapsed ms 4; result, permutations 199; result, excluded null 0; result, distance band m 2,269; billing, cost 0.05036 42.

Tool gp_outliers; parameters {"field": "population", "project": "941056ab-e59e-43ce-98fd-5b3678626f78", "features": {"layer": "Districts (cf8657b3)"}, "aggregation": "hexagon"}

Outliers

What this analysis is. Find outliers: Anselin local Moran’s I: HH, LL clusters and HL, LH outliers 40. In ArcGIS it is Find Outliers or Cluster and Outlier Analysis (Anselin Local Moran’s I) 40.

Why it was run on this data. Local Moran's I maps clusters and outliers of 'trees' 44.

What it found. Outliers on Green areas: Find outliers: 1 layer saved: Find outliers: Green areas (15 features) 4546. It reported result, count 15; result, elapsed ms 2; result, permutations 199; result, excluded null 0; result, distance band m 1,632; billing, cost 0.05045 45.

Findings

• Evidenced: Outliers on Green areas: Find outliers: 1 layer saved: Find outliers: Green areas (15 features) 4546.
• Evidenced: It reported result, count 15; result, elapsed ms 2; result, permutations 199; result, excluded null 0; result, distance band m 1,632; billing, cost 0.05045 45.

Tool gp_outliers; parameters {"field": "trees", "project": "941056ab-e59e-43ce-98fd-5b3678626f78", "features": {"layer": "Green areas (3858215c)"}, "aggregation": "hexagon"}

Spatial autocorrelation

What this analysis is. Spatial autocorrelation: Global Moran’s I: is the pattern clustered, dispersed or random 47. In ArcGIS it is Spatial Autocorrelation (Global Moran’s I) 47.

Why it was run on this data. Global Moran's I says whether similar values of 'population' sit next to each other 48.

What it found. Spatial autocorrelation on Districts: Spatial autocorrelation: done; no map output 4349. It reported result, n 12; result, p value 0.04188; result, z score 2.035; result, morans i 0.3603; result, variance 0.04918; result, elapsed ms 1 49.

Findings

• Evidenced: Spatial autocorrelation on Districts: Spatial autocorrelation: done; no map output 4349.
• Evidenced: It reported result, n 12; result, p value 0.04188; result, z score 2.035; result, morans i 0.3603; result, variance 0.04918; result, elapsed ms 1 49.

Tool gp_spatial_autocorrelation; parameters {"field": "population", "project": "941056ab-e59e-43ce-98fd-5b3678626f78", "features": {"layer": "Districts (cf8657b3)"}}

Summarize within

What this analysis is. Summarize within: Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons 13. In ArcGIS it is Summarize Within or Aggregate Points; in QGIS, native:countpointsinpolygon or native:sumlinelengths 13.

Why it was run on this data. Count Air quality sensors inside each feature of Smog alert areas by model; the counts map as a choropleth (natural breaks, sequential colors) 50.

What it found. Summarize within on Air quality sensors, Smog alert areas: Summarize within: 1 layer saved: Summarize within: Smog alert areas (5 features) 173951. It reported result, count 5; result, elapsed ms 1; billing, cost 0.01048; billing, list price 0.01048; billing, upstream cost 0 51.

Findings

• Evidenced: Summarize within on Air quality sensors, Smog alert areas: Summarize within: 1 layer saved: Summarize within: Smog alert areas (5 features) 173951.
• Evidenced: It reported result, count 5; result, elapsed ms 1; billing, cost 0.01048; billing, list price 0.01048; billing, upstream cost 0 51.

Tool gp_summarize_within; parameters {"stats": [{"stat": "sum", "field": "height_m"}], "project": "941056ab-e59e-43ce-98fd-5b3678626f78", "features": {"layer": "Air quality sensors (b023a603)"}, "group_by": "model", "polygons": {"layer": "Smog alert areas (cb491d16)"}}

Maps and charts

The run also made 20 map image(s) 5253545556575859606162636465666768697071 and 26 chart(s) 7273747576777879808182838485868788899091929394959697.

What the map shows. The map displays the analysis of "Heating and smoke complaints" across a grid of cells, according to the provided legend. The overwhelming majority of the mapped area is coloured grey, indicating that "No pattern detected" was found in most locations. There is a small cluster of adjacent hexagonal cells near the center of the map that are coloured orange, signifying a "Sporadic hot spot." These few sporadic hot spots are tightly grouped together in a specific area, standing out against the wide expanse of areas showing no discernible pattern. 52

Inferred: read from the image by gemma4:e4b, not evidence.

Changes since the last run

This is the first report of this project.

Sources

1. 1Rendering: Chart: Timeline: events per week by dataset. 108 rows id a084bcb1-4ebb-4e02-a2b5-e0989f373da4
2. 2Dataset: Sensor readings every three hours. table, 3024 rows id 0d15a1c1-4e15-46ed-8bc1-8aa3d29ccbcd
3. 3Dataset: Heating and smoke complaints. vector, 1000 rows id f4580557-af68-44bb-8bac-c0ee2ee81164
4. 4Dataset: Traffic counts by day. table, 420 rows id 4dddb911-3782-4522-b33f-66a0810f4308
5. 5Dataset: Smog alert areas. vector, 5 rows id 1797fddc-1ecb-4d65-bb9d-8c24fbce0b84
6. 6Catalog: Emerging hot spots (operation catalog, gp_space_time_hot_spots). Where hot spots are new, consecutive, intensifying, persistent, diminishing, sporadic, oscillating or historical over time. ArcGIS: Emerging Hot Spot Analysis, Create Space Time Cube. id gp_space_time_hot_spots
7. 7Plan: Space time hot spots of Heating and smoke complaints (the Start plan's candidate c_d98590d93cd6). Heating and smoke complaints carry dates in 'reported_at': emerging hot spot analysis finds new, intensifying and fading hot spots per week. id 73000d9f-3676-479d-bf5e-7209c2669a39:c_d98590d93cd6
8. 8Tool result: gp_space_time_hot_spots, step 0. Emerging hot spots: 1 layer saved: Emerging hot spots: Heating and smoke complaints (79 features). id e2bffa10-d83f-4078-a978-5b42d657bad7:0
9. 9Layer: Heating and smoke complaints. 1,000 features id 872e2359-4c4f-4b1c-a353-f558b419ae1b
10. 10Catalog: Repeat and near repeat classification (operation catalog, gp_near_repeat). Classify incidents as originators, repeats (same place) or near repeats within a distance and time window, with a Knox test table. ArcGIS: Repeat and Near Repeat Classification, Near Repeat Calculator. id gp_near_repeat
11. 11Plan: Near repeat of Heating and smoke complaints (the Start plan's candidate c_ba6e8fc6e4b1). Near repeat analysis tests whether Heating and smoke complaints close in space also follow each other within 18 days. id 73000d9f-3676-479d-bf5e-7209c2669a39:c_ba6e8fc6e4b1
12. 12Tool result: gp_near_repeat, step 1. Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Heating and smoke complaints (1000 features). id e2bffa10-d83f-4078-a978-5b42d657bad7:1
13. 13Catalog: Summarize within (operation catalog, gp_summarize_within). Count and aggregate the features inside each polygon (or bin), proportionally for lines and polygons. ArcGIS: Summarize Within, Aggregate Points. QGIS: native:countpointsinpolygon, native:sumlinelengths. id gp_summarize_within
14. 14Plan: Schools per feature of Smog alert areas (the Start plan's candidate c_fe1717bec4eb). Count Schools inside each feature of Smog alert areas; the counts map as a choropleth (natural breaks, sequential colors). id 73000d9f-3676-479d-bf5e-7209c2669a39:c_fe1717bec4eb
15. 15Tool result: gp_summarize_within, step 0. Summarize within: 1 layer saved: Summarize within: Smog alert areas (5 features). id 11b23637-45df-4d60-b9d3-92ec6e1a9142:0
16. 16Layer: Schools. 50 features id 04161d9b-b772-49f5-b451-f05a72c11340
17. 17Layer: Smog alert areas. 5 features id cb491d16-8d66-4ca9-8a0c-b0cca331df73
18. 18Tool result: gp_summarize_within, step 1. Summarize within: 1 layer saved: Summarize within: Smog alert areas (5 features). id 11b23637-45df-4d60-b9d3-92ec6e1a9142:1
19. 19Layer: Traffic counters. 20 features id 2beeba9d-323f-4f1e-919a-5e066c70311f
20. 20Catalog: Hot spot analysis (operation catalog, gp_hot_spots). Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins. ArcGIS: Find Hot Spots, Hot Spot Analysis (Getis-Ord Gi*). QGIS: qgis:hotspot (plugin). id gp_hot_spots
21. 21Plan: Hot spots of Schools (the Start plan's candidate c_b9bfdeef42df). 50 points: Getis-Ord Gi* tests whether the concentrations in Schools are statistically significant. id 73000d9f-3676-479d-bf5e-7209c2669a39:c_b9bfdeef42df
22. 22Tool result: gp_hot_spots, step 0. Hot spot analysis: 1 layer saved: Hot spot analysis: Schools (18 features). id ce105be5-3779-4e96-a7f2-284b760484a5:0
23. 23Plan: Hot spots of Heating and smoke complaints (the Start plan's candidate c_8ba28202f6a2). 1000 points: Getis-Ord Gi* tests whether the concentrations in Heating and smoke complaints are statistically significant. id 73000d9f-3676-479d-bf5e-7209c2669a39:c_8ba28202f6a2
24. 24Tool result: gp_hot_spots, step 1. Hot spot analysis: 1 layer saved: Hot spot analysis: Heating and smoke complaints (208 features). id ce105be5-3779-4e96-a7f2-284b760484a5:1
25. 25Catalog: Calculate density (operation catalog, gp_calculate_density). Kernel density per square kilometer on a grid. ArcGIS: Calculate Density, Kernel Density. QGIS: native:heatmapkerneldensityestimation. id gp_calculate_density
26. 26Plan: Kernel density of Schools (the Start plan's candidate c_b5b20e7ef97c). 50 points in Schools: a density surface shows where they concentrate. id 73000d9f-3676-479d-bf5e-7209c2669a39:c_b5b20e7ef97c
27. 27Tool result: gp_calculate_density, step 0. Calculate density: 1 layer saved: Calculate density: Schools (98 features). id f7011d1b-ef58-4060-8cda-1a0a0ec3fb50:0
28. 28Plan: Kernel density of Traffic counters (the Start plan's candidate c_5380a4ae623b). 20 points in Traffic counters: a density surface shows where they concentrate. id 73000d9f-3676-479d-bf5e-7209c2669a39:c_5380a4ae623b
29. 29Tool result: gp_calculate_density, step 1. Calculate density: 1 layer saved: Calculate density: Traffic counters (208 features). id f7011d1b-ef58-4060-8cda-1a0a0ec3fb50:1
30. 30Plan: Kernel density of Heating and smoke complaints (the Start plan's candidate c_0cb7c2c39d34). 1000 points in Heating and smoke complaints: a density surface shows where they concentrate. id 73000d9f-3676-479d-bf5e-7209c2669a39:c_0cb7c2c39d34
31. 31Tool result: gp_calculate_density, step 2. Calculate density: 1 layer saved: Calculate density: Heating and smoke complaints (1379 features). id f7011d1b-ef58-4060-8cda-1a0a0ec3fb50:2
32. 32Catalog: Find point clusters (operation catalog, gp_find_point_clusters). Density-based clusters (DBSCAN, HDBSCAN or OPTICS, optionally space-time) with hulls and noise. ArcGIS: Find Point Clusters, Density-based Clustering. QGIS: native:dbscanclustering, native:stdbscanclustering. id gp_find_point_clusters
33. 33Plan: Cluster points of Schools (the Start plan's candidate c_9dae65ce7c51). DBSCAN finds dense groups of Schools and separates noise. id 73000d9f-3676-479d-bf5e-7209c2669a39:c_9dae65ce7c51
34. 34Tool result: gp_find_point_clusters, step 0. Find point clusters: 2 layers saved: Find point clusters: Schools (50 features); Find point clusters: cluster hulls (7 features). id 17da64d0-c62f-4bc1-b36b-a20be986149f:0
35. 35Plan: Cluster points of Traffic counters (the Start plan's candidate c_5a43af034293). DBSCAN finds dense groups of Traffic counters and separates noise. id 73000d9f-3676-479d-bf5e-7209c2669a39:c_5a43af034293
36. 36Tool result: gp_find_point_clusters, step 1. Find point clusters: 2 layers saved: Find point clusters: Traffic counters (20 features); Find point clusters: cluster hulls (1 feature). id 17da64d0-c62f-4bc1-b36b-a20be986149f:1
37. 37Plan: Cluster points of Air quality sensors (the Start plan's candidate c_82728615a157). DBSCAN finds dense groups of Air quality sensors and separates noise. id 73000d9f-3676-479d-bf5e-7209c2669a39:c_82728615a157
38. 38Tool result: gp_find_point_clusters, step 2. Find point clusters: 2 layers saved: Find point clusters: Air quality sensors (18 features); Find point clusters: cluster hulls (1 feature). id 17da64d0-c62f-4bc1-b36b-a20be986149f:2
39. 39Layer: Air quality sensors. 18 features id b023a603-7600-43b0-b1a5-263889cc78ec
40. 40Catalog: Find outliers (operation catalog, gp_outliers). Anselin local Moran’s I: HH, LL clusters and HL, LH outliers. ArcGIS: Find Outliers, Cluster and Outlier Analysis (Anselin Local Moran’s I). id gp_outliers
41. 41Plan: Local morans i of Districts (the Start plan's candidate c_4dc818ee3a6e). Local Moran's I maps clusters and outliers of 'population'. id 73000d9f-3676-479d-bf5e-7209c2669a39:c_4dc818ee3a6e
42. 42Tool result: gp_outliers, step 0. Find outliers: 1 layer saved: Find outliers: Districts (12 features). id d6fde80f-96aa-463a-8668-577f95febf04:0
43. 43Layer: Districts. 12 features id cf8657b3-2d43-4636-9390-17763a61ba4a
44. 44Plan: Local morans i of Green areas (the Start plan's candidate c_ade852c64875). Local Moran's I maps clusters and outliers of 'trees'. id 73000d9f-3676-479d-bf5e-7209c2669a39:c_ade852c64875
45. 45Tool result: gp_outliers, step 1. Find outliers: 1 layer saved: Find outliers: Green areas (15 features). id d6fde80f-96aa-463a-8668-577f95febf04:1
46. 46Layer: Green areas. 15 features id 3858215c-dede-4b5d-bb9d-c7026b082281
47. 47Catalog: Spatial autocorrelation (operation catalog, gp_spatial_autocorrelation). Global Moran’s I: is the pattern clustered, dispersed or random. ArcGIS: Spatial Autocorrelation (Global Moran’s I). id gp_spatial_autocorrelation
48. 48Plan: Global morans i of Districts (the Start plan's candidate c_e7dbd09fdbfe). Global Moran's I says whether similar values of 'population' sit next to each other. id 73000d9f-3676-479d-bf5e-7209c2669a39:c_e7dbd09fdbfe
49. 49Tool result: gp_spatial_autocorrelation, step 2. Spatial autocorrelation: done; no map output. id d6fde80f-96aa-463a-8668-577f95febf04:2
50. 50Plan: Air quality sensors per feature of Smog alert areas (the Start plan's candidate c_9e70122240f8). Count Air quality sensors inside each feature of Smog alert areas by model; the counts map as a choropleth (natural breaks, sequential colors). id 73000d9f-3676-479d-bf5e-7209c2669a39:c_9e70122240f8
51. 51Tool result: gp_summarize_within, step 0. Summarize within: 1 layer saved: Summarize within: Smog alert areas (5 features). id 9293aa84-ac7f-4f7b-98ff-5dd6fd0e80c3:0
52. 52Rendering: Map: Emerging hot spots: Heating and smoke complaints id ef0fba2c-3dab-44c8-8d1b-37950e2aa09f
53. 53Rendering: Map: Repeat and near repeat classification: Heating and smoke complaints id 93441096-27ae-4f30-aade-125dce56048f
54. 54Rendering: Map: Summarize within: Smog alert areas id 407df334-25e6-42fe-8c40-03ead11b55fc
55. 55Rendering: Map: Summarize within: Smog alert areas id 0034d9c7-6324-47a3-b594-1d24ebb1c534
56. 56Rendering: Map: Hot spot analysis: Schools id fad1fd90-84ae-4c56-a7c8-c19968f56251
57. 57Rendering: Map: Hot spot analysis: Heating and smoke complaints id e8903c1c-1976-4634-a49a-1eca16d457d8
58. 58Rendering: Map: Calculate density: Schools id dd937085-125b-4245-b19d-4df13795ffac
59. 59Rendering: Map: Calculate density: Traffic counters id e20a9973-68d1-4830-bbb5-9a780d6a9a33
60. 60Rendering: Map: Calculate density: Heating and smoke complaints id 0160d7fb-b98e-47f4-ba56-8e97beecdc89
61. 61Rendering: Map: Find point clusters: Schools id 2fe195f1-b867-4da5-80ca-f8476d6adc87
62. 62Rendering: Map: Find point clusters: cluster hulls id 0419d3be-d8f9-490f-b1f7-42948c5a9737
63. 63Rendering: Map: Find point clusters: Traffic counters id 0b85a9fc-34fd-43cb-84f9-2f5ffe2f143c
64. 64Rendering: Map: Find point clusters: cluster hulls id d057adbc-5e7a-4438-8d07-3b943401c523
65. 65Rendering: Map: Find point clusters: Air quality sensors id 2cb83ca5-0098-4a79-9790-1e43bfdf1db5
66. 66Rendering: Map: Find point clusters: cluster hulls id a67a4f33-5986-463c-bc2c-acd3018fea50
67. 67Rendering: Map: Districts by population id 2e54a251-6a26-402f-86e8-e6d15cab3ac3
68. 68Rendering: Map: Green areas by trees id 7404178c-fa56-4278-a94d-180735127ec9
69. 69Rendering: Map: Find outliers: Districts id 65c0124e-203f-4f91-873c-12b4b6960ae4
70. 70Rendering: Map: Find outliers: Green areas id dc1a86e3-09d6-402a-a3b2-c3fdd0d3eab5
71. 71Rendering: Map: Summarize within: Smog alert areas id a4d875cc-962b-4e3c-b64e-dd57deee34ed
72. 72Rendering: Chart: Sensor readings every three hours over time (timestamp). 21 rows id 8be9f734-9fc2-4614-9c16-f319eb5bc297
73. 73Rendering: Chart: Heating and smoke complaints over time (reported_at). 108 rows id bafb9708-0830-42a8-9dce-37a4690fb945
74. 74Rendering: Chart: Traffic counts by day over time (date). 21 rows id 71f35ff8-083c-4035-b705-ccab22f7bdf8
75. 75Rendering: Chart: Smog alert areas over time (date). 20 rows id 4033fc71-23c1-4ffa-ae99-ae32670ffd03
76. 76Rendering: Chart: Emerging hot spots: Heating and smoke complaints. 8 rows id 988b5149-df22-4996-bf31-ac55354d51c4
77. 77Rendering: Chart: Repeat and near repeat classification: Heating and smoke complaints. 108 rows id f41c1c14-81d1-4349-8443-09a4f4ba9eb6
78. 78Rendering: Chart: Summarize within: Smog alert areas. 20 rows id 9227b7e1-e625-47f7-80b5-495de4ab4e0a
79. 79Rendering: Chart: Summarize within: Smog alert areas. 20 rows id a7c52833-f238-4226-b84c-fb152e2c2eb1
80. 80Rendering: Chart: Hot spot analysis: Schools. 7 rows id e9ad8aa0-adb4-490e-928f-6b45446a7f1f
81. 81Rendering: Chart: Hot spot analysis: Heating and smoke complaints. 13 rows id d96b848c-1ca5-4e56-a8a0-bd5c1a5ae7d0
82. 82Rendering: Chart: Calculate density: Schools. 8 rows id 063ac921-93d3-4e7d-ab62-58eb4e9b60e6
83. 83Rendering: Chart: Calculate density: Traffic counters. 9 rows id 43c8bec3-da9f-4830-912a-49013656b2a0
84. 84Rendering: Chart: Calculate density: Heating and smoke complaints. 12 rows id 55617b3d-063a-4f9c-a0dc-71b273d1c5c5
85. 85Rendering: Chart: Find point clusters: Schools. 8 rows id 6776d720-700c-42c8-803c-ba17f7028e7e
86. 86Rendering: Chart: Find point clusters: cluster hulls. 7 rows id 9bde57a2-00d0-4bd3-a720-f74fb52e323e
87. 87Rendering: Chart: Find point clusters: Traffic counters. 2 rows id 7ca9a164-4408-4af2-8a82-d20106e3d63e
88. 88Rendering: Chart: Find point clusters: cluster hulls. 1 rows id 468903ad-2588-4bd9-b8d4-6010b36560ca
89. 89Rendering: Chart: Find point clusters: Air quality sensors. 1 rows id 781b4a70-d6e9-4e64-931e-192b47d86fd1
90. 90Rendering: Chart: Find point clusters: cluster hulls. 1 rows id 138b31d8-fc4f-44f8-8117-aecb9b369d31
91. 91Rendering: Chart: Find outliers: Districts. 12 rows id f6661716-8904-4181-817e-f4d3e782e217
92. 92Rendering: Chart: Find outliers: Green areas. 15 rows id b3b4d6ea-4b5e-4cac-8fd3-fcda036bee5c
93. 93Rendering: Chart: Summarize within: Smog alert areas. 20 rows id 1ceece26-9477-4f54-9fa4-1e2dbc1f96a5
94. 94Rendering: Chart: Summarize within: Smog alert areas over time. 20 rows id 76c45baf-feca-4a2f-a3a8-a4aa1cb03271
95. 95Rendering: Chart: Summarize within: Smog alert areas over time. 20 rows id fc1e8aa9-d730-49fa-9bbb-bbeacedfb972
96. 96Rendering: Chart: Summarize within: Smog alert areas over time. 20 rows id f7309174-c972-4de9-853f-0c6929414c1f
97. 97Rendering: Chart: Repeat and near repeat classification: Heating and smoke complaints over time. 108 rows id 5a51d5a0-e784-4a44-a4b6-258ad1f1e695
98. 98Dataset: Districts. vector, 12 rows id 26db6cdc-8ed6-4018-8216-57b31d6b7b10
99. 99Dataset: Air quality sensors. vector, 18 rows id 6baceea7-d2af-4370-8edd-b762c4503296
100. 100Dataset: Major roads. vector, 20 rows id c02faba8-cbb0-4f5c-9b60-817e3cfb35fe
101. 101Dataset: Traffic counters. vector, 20 rows id 3b29eef8-b3c6-476c-b293-75939220204d
102. 102Dataset: Schools. vector, 50 rows id 30a2dc6f-9417-4701-b099-bcebc56bd124
103. 103Dataset: Green areas. vector, 15 rows id 32303dbc-77ff-4931-9a5d-9590fbeb6497
104. 104Dataset: Air quality bulletin. document, ? rows id acc3c65d-3cae-42d7-af0f-109b23544f46
105. 105Record: Sample: Smart city sensors, Krakow id 941056ab-e59e-43ce-98fd-5b3678626f78
106. 106Layer: Emerging hot spots: Heating and smoke complaints. 79 features id bf5f1710-3502-403a-8d6a-ffe2e546b9eb
107. 107Layer: Repeat and near repeat classification: Heating and smoke complaints. 1,000 features id bb01a925-2b04-4941-9f49-710b9a99b0eb
108. 108Layer: Summarize within: Smog alert areas. 5 features id acc65faa-4c2f-47ce-af45-9d9d172f8270
109. 109Layer: Summarize within: Smog alert areas. 5 features id fd04402f-45d7-49e4-91f3-b84e26f37bd8
110. 110Layer: Hot spot analysis: Schools. 18 features id 5228d4e7-3d39-443c-85cd-530e92371dbe
111. 111Layer: Hot spot analysis: Heating and smoke complaints. 208 features id 16bc96b6-9c4f-4c62-b6e2-da0018f9338c
112. 112Layer: Calculate density: Schools. 98 features id 1263d766-433f-4232-a64f-4c671f510b22
113. 113Layer: Calculate density: Traffic counters. 208 features id bcbc8c7e-77b3-4bfb-882d-bad64d25f7f1
114. 114Layer: Calculate density: Heating and smoke complaints. 1,379 features id 54d2c3ab-0779-4796-8ab6-f64429e25aa9
115. 115Layer: Find point clusters: Schools. 50 features id cfc15b6a-1b6a-49f0-babb-0f62493f7ae8
116. 116Layer: Find point clusters: cluster hulls. 7 features id ecce6173-f6a4-413b-95e9-b66d9b514f36
117. 117Layer: Find point clusters: Traffic counters. 20 features id 5b1bb98f-a7b6-4e1d-9746-34bd0adff1d8
118. 118Layer: Find point clusters: cluster hulls. 1 features id c5d42204-a8ab-452e-b226-45831682926b
119. 119Layer: Find point clusters: Air quality sensors. 18 features id c7b1b5b6-45ea-4c4b-89fb-057b73041330
120. 120Layer: Find point clusters: cluster hulls. 1 features id 77120389-d522-4f77-b502-a63eafc89c5d
121. 121Layer: Find outliers: Districts. 12 features id cf03dea3-3fc9-4ee1-beb8-43ec21f5da69
122. 122Layer: Find outliers: Green areas. 15 features id 3e0dd3e3-729e-40f0-80b8-2477238ce9d7
123. 123Layer: Summarize within: Smog alert areas. 5 features id af0ec067-f4d0-4dfb-be36-930e255a6ef8