# 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

| Data | Kind | Rows | Time span | Summary | Source |
| --- | --- | --- | --- | --- | --- |
| Districts | vector, Polygon | 12 |  | 12 polygon (area) features; 'population' 15,889 to 73,746 (mean 3.707e+04); 'coal_heated_homes' 264 to 6,760 (mean 2035); named by 'name' (e.g. Polnoc, Poludnie, Wschod); total area 48.03 km2; extent 19.8885,50.0372 to 20.0014,50.0922. Areas (Districts) with population to compare and map. | [98] |
| Air quality sensors | vector, Point | 18 |  | 18 point features; 'model' has 2 values (top: optical PM 9, reference station 9); 'height_m' 3 to 6 (mean 4.5); named by 'name' (e.g. Air sensor 01 Zachod, Air sensor 02 Rynek Gorny, Air sensor 03 Parkowa); extent 19.8942,50.0421 to 19.9952,50.0874. Point locations of Air quality sensors by model. | [99] |
| Sensor readings every three hours | table | 3,024 | 2026-01-12 to 2026-02-01 | Table of 3,024 rows and 7 columns without geometry; dated by 'timestamp' from 2026-01-12 to 2026-02-01; 'pm25_ugm3' 17.6 to 211 (mean 52.75); 'pm10_ugm3' 23 to 326.5 (mean 76.56); 'no2_ugm3' 15 to 55 (mean 34.97). A table of Sensor readings every three hours without locations. | [2] |
| Major roads | vector, LineString | 20 |  | 20 line features; 'aadt' 8,775 to 60,655 (mean 2.965e+04); 'heavy_pct' 3.4 to 13.8 (mean 7.705); named by 'name' (e.g. ulica Dluga, ulica Ogrodowa, aleja Lipowa); total length 121.1 km; extent 19.8899,50.0380 to 20.0004,50.0913. Lines (Major roads) such as routes or networks. | [100] |
| Heating and smoke complaints | vector, Point | 1,000 | 2025-10-01 to 2026-03-31 | 1,000 point features; dated by 'reported_at' from 2025-10-01 to 2026-03-31; 'issue' has 4 values (top: dust 265, smell 261, smoke from chimney 239); 'channel' has 3 values (top: web 349, phone 326, app 325); extent 19.8915,50.0381 to 20.0005,50.0913. Dated point events by issue: where and when Heating and smoke complaints happen. | [3] |
| Traffic counters | vector, Point | 20 |  | 20 point features; 'lanes' 2 to 4 (mean 3.2); extent 19.8905,50.0388 to 19.9989,50.0888. Point locations of Traffic counters. | [101] |
| Traffic counts by day | table | 420 | 2026-01-12 to 2026-02-01 | Table of 420 rows and 4 columns without geometry; dated by 'date' from 2026-01-12 to 2026-02-01; 'vehicles' 4,398 to 46,448 (mean 2.016e+04); 'heavy_pct' 4 to 13 (mean 8.541). A table of Traffic counts by day without locations. | [4] |
| Schools | vector, Point | 50 |  | 50 point features; 'pupils' 154 to 890 (mean 561.8); named by 'name' (e.g. Szkola 1, Szkola 2, Szkola 3); extent 19.8911,50.0385 to 19.9994,50.0908. Point locations of Schools. | [102] |
| Green areas | vector, Polygon | 15 |  | 15 polygon (area) features; 'trees' 160 to 869 (mean 446.5); named by 'name' (e.g. Park Poludnie, Park Wschod, Park Zachod); total area 0.3318 km2; extent 19.8943,50.0418 to 19.9722,50.0881. Areas (Green areas) with trees to compare and map. | [103] |
| Smog alert areas | vector, Polygon | 5 | 2026-01-16 to 2026-01-25 | 5 polygon (area) features; dated by 'date' from 2026-01-16 to 2026-01-25; 'level' has 2 values (top: level 3 3, level 2 2); 'free_transit' has 1 values (top: True 5); total area 49.02 km2; extent 19.8966,50.0587 to 19.9482,50.0915. Areas (Smog alert areas) to compare and map. | [5] |
| Air quality bulletin | document |  |  | 1-page md document 'Air quality bulletin, January 2026'; language en; mentions 3 place(s): Poludnie (1), Air sensor 02 Rynek Gorny (1), Air sensor 06 Poludnie (1). A document about Poludnie, Air sensor 02 Rynek Gorny, Air sensor 06 Poludnie. | [104] |
| Sample: Smart city sensors, Krakow | record |  |  |  | [105] |
| Emerging hot spots: Heating and smoke complaints | layer, Polygon | 79 |  |  | [106] |
| Repeat and near repeat classification: Heating and smoke complaints | layer, Point | 1,000 |  |  | [107] |
| Summarize within: Smog alert areas | layer, Polygon | 5 |  |  | [108] |
| Summarize within: Smog alert areas | layer, Polygon | 5 |  |  | [109] |
| Hot spot analysis: Schools | layer, Polygon | 18 |  |  | [110] |
| Hot spot analysis: Heating and smoke complaints | layer, Polygon | 208 |  |  | [111] |
| Calculate density: Schools | layer, Polygon | 98 |  |  | [112] |
| Calculate density: Traffic counters | layer, Polygon | 208 |  |  | [113] |
| Calculate density: Heating and smoke complaints | layer, Polygon | 1,379 |  |  | [114] |
| Find point clusters: Schools | layer, Point | 50 |  |  | [115] |
| Find point clusters: cluster hulls | layer, Polygon | 7 |  |  | [116] |
| Find point clusters: Traffic counters | layer, Point | 20 |  |  | [117] |
| Find point clusters: cluster hulls | layer, Polygon | 1 |  |  | [118] |
| Find point clusters: Air quality sensors | layer, Point | 18 |  |  | [119] |
| Find point clusters: cluster hulls | layer, Polygon | 1 |  |  | [120] |
| Find outliers: Districts | layer, Polygon | 12 |  |  | [121] |
| Find outliers: Green areas | layer, Polygon | 15 |  |  | [122] |
| Summarize within: Smog alert areas | layer, Polygon | 5 |  |  | [123] |

## Timeline

![Timeline: events per week by dataset](charts/27-timeline-events-per-week-by-dataset.png)

*Figure 1. Timeline: events per week by dataset [1]. Per week, 2025-09-29 to 2026-03-30. Source: Sensor readings every three hours, Heating and smoke complaints, Traffic counts by day, Smog alert areas (4,449 features). The data is in the appendix.*

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 [1][2][3][4][5]. 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) [8][9]. 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].

| Result | Value |
| --- | --- |
| result, count | 79 |
| result, elapsed ms | 13 |
| result, resolution | 8 |
| result, features used | 1,000 |
| result, time neighbors | 0 |
| billing, cost | 0.15 |
| billing, list price | 0.15 |
| billing, upstream cost | 0 |

**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) [8][9].
- **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) [9][12]. 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].

| Result | Value |
| --- | --- |
| result, days | 18 |
| result, count | 1,000 |
| result, chains | 118 |
| result, distance m | 200 |
| result, elapsed ms | 21 |
| result, pairs linked | 557 |
| billing, cost | 0.08 |
| billing, list price | 0.08 |
| billing, upstream cost | 0 |

**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) [9][12].
- **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) [15][16][17]. It reported result, count 5; result, elapsed ms 1; billing, cost 0.01112; billing, list price 0.01112; billing, upstream cost 0 [15].

| Result | Value |
| --- | --- |
| result, count | 5 |
| result, elapsed ms | 1 |
| billing, cost | 0.01112 |
| billing, list price | 0.01112 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Summarize within on Schools, Smog alert areas: Summarize within: 1 layer saved: Summarize within: Smog alert areas (5 features) [15][16][17].
- **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].

| Result | Value |
| --- | --- |
| result, count | 5 |
| result, elapsed ms | 0 |
| billing, cost | 0.01052 |
| billing, list price | 0.01052 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Summarize within on Traffic counters, Smog alert areas: Summarize within: 1 layer saved: Summarize within: Smog alert areas (5 features) [17][18][19].
- **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].

| Result | Value |
| --- | --- |
| 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 |
| billing, cost | 0.051 |
| billing, list price | 0.051 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Hot spots on Schools: Hot spot analysis: 1 layer saved: Hot spot analysis: Schools (18 features) [16][22].
- **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].

| Result | Value |
| --- | --- |
| 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 |
| billing, cost | 0.07 |
| billing, list price | 0.07 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Hot spots on Heating and smoke complaints: Hot spot analysis: 1 layer saved: Hot spot analysis: Heating and smoke complaints (208 features) [9][24].
- **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].

| Result | Value |
| --- | --- |
| 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 |
| billing, cost | 0.051 |
| billing, list price | 0.051 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Calculate density on Schools: Calculate density: 1 layer saved: Calculate density: Schools (98 features) [16][27].
- **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) [19][29]. 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].

| Result | Value |
| --- | --- |
| 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 |
| billing, cost | 0.0504 |
| billing, list price | 0.0504 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Calculate density on Traffic counters: Calculate density: 1 layer saved: Calculate density: Traffic counters (208 features) [19][29].
- **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].

| Result | Value |
| --- | --- |
| 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 |
| billing, cost | 0.07 |
| billing, list price | 0.07 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Calculate density on Heating and smoke complaints: Calculate density: 1 layer saved: Calculate density: Heating and smoke complaints (1379 features) [9][31].
- **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) [16][34]. 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].

| Result | Value |
| --- | --- |
| result, count | 50 |
| result, noise | 10 |
| result, distance m | 1,000 |
| result, elapsed ms | 4 |
| result, min points | 3 |
| billing, cost | 0.051 |
| billing, list price | 0.051 |
| billing, upstream cost | 0 |

**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) [16][34].
- **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) [19][36]. 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].

| Result | Value |
| --- | --- |
| result, count | 20 |
| result, noise | 1 |
| result, distance m | 2,000 |
| result, elapsed ms | 1 |
| result, min points | 3 |
| billing, cost | 0.0504 |
| billing, list price | 0.0504 |
| billing, upstream cost | 0 |

**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) [19][36].
- **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) [38][39]. 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].

| Result | Value |
| --- | --- |
| result, count | 18 |
| result, noise | 0 |
| result, distance m | 2,000 |
| result, elapsed ms | 1 |
| result, min points | 3 |
| billing, cost | 0.05036 |
| billing, list price | 0.05036 |
| billing, upstream cost | 0 |

**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) [38][39].
- **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].

| Result | Value |
| --- | --- |
| result, count | 12 |
| result, elapsed ms | 4 |
| result, permutations | 199 |
| result, excluded null | 0 |
| result, distance band m | 2,269 |
| billing, cost | 0.05036 |
| billing, list price | 0.05036 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Outliers on Districts: Find outliers: 1 layer saved: Find outliers: Districts (12 features) [42][43].
- **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) [45][46]. 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].

| Result | Value |
| --- | --- |
| result, count | 15 |
| result, elapsed ms | 2 |
| result, permutations | 199 |
| result, excluded null | 0 |
| result, distance band m | 1,632 |
| billing, cost | 0.05045 |
| billing, list price | 0.05045 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Outliers on Green areas: Find outliers: 1 layer saved: Find outliers: Green areas (15 features) [45][46].
- **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 [43][49]. 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].

| Result | Value |
| --- | --- |
| 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 |
| result, expected i | -0.09091 |
| result, excluded null | 0 |
| result, distance band m | 2,269 |
| result, p value normality | 0.03746 |
| result, z score normality | 2.081 |
| result, variance normality | 0.04703 |
| billing, cost | 0.02 |
| billing, list price | 0.02 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Spatial autocorrelation on Districts: Spatial autocorrelation: done; no map output [43][49].
- **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) [17][39][51]. It reported result, count 5; result, elapsed ms 1; billing, cost 0.01048; billing, list price 0.01048; billing, upstream cost 0 [51].

| Result | Value |
| --- | --- |
| result, count | 5 |
| result, elapsed ms | 1 |
| billing, cost | 0.01048 |
| billing, list price | 0.01048 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Summarize within on Air quality sensors, Smog alert areas: Summarize within: 1 layer saved: Summarize within: Smog alert areas (5 features) [17][39][51].
- **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) [52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70][71] and 26 chart(s) [72][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88][89][90][91][92][93][94][95][96][97].

![Emerging hot spots: Heating and smoke complaints](maps/01-emerging-hot-spots-heating-and-smoke-complaints.png)

*Figure 2. Emerging hot spots: Heating and smoke complaints [52]*

![Repeat and near repeat classification: Heating and smoke complaints](maps/02-repeat-and-near-repeat-classification-heating-and-smoke-comp.png)

*Figure 3. Repeat and near repeat classification: Heating and smoke complaints [53]*

![Summarize within: Smog alert areas](maps/03-summarize-within-smog-alert-areas.png)

*Figure 4. Summarize within: Smog alert areas [54]*

![Summarize within: Smog alert areas](maps/04-summarize-within-smog-alert-areas.png)

*Figure 5. Summarize within: Smog alert areas [55]*

![Hot spot analysis: Schools](maps/05-hot-spot-analysis-schools.png)

*Figure 6. Hot spot analysis: Schools [56]*

![Hot spot analysis: Heating and smoke complaints](maps/06-hot-spot-analysis-heating-and-smoke-complaints.png)

*Figure 7. Hot spot analysis: Heating and smoke complaints [57]*

![Calculate density: Schools](maps/07-calculate-density-schools.png)

*Figure 8. Calculate density: Schools [58]*

![Calculate density: Traffic counters](maps/08-calculate-density-traffic-counters.png)

*Figure 9. Calculate density: Traffic counters [59]*

![Calculate density: Heating and smoke complaints](maps/09-calculate-density-heating-and-smoke-complaints.png)

*Figure 10. Calculate density: Heating and smoke complaints [60]*

![Find point clusters: Schools](maps/10-find-point-clusters-schools.png)

*Figure 11. Find point clusters: Schools [61]*

![Find point clusters: cluster hulls](maps/11-find-point-clusters-cluster-hulls.png)

*Figure 12. Find point clusters: cluster hulls [62]*

![Find point clusters: Traffic counters](maps/12-find-point-clusters-traffic-counters.png)

*Figure 13. Find point clusters: Traffic counters [63]*

![Find point clusters: cluster hulls](maps/13-find-point-clusters-cluster-hulls.png)

*Figure 14. Find point clusters: cluster hulls [64]*

![Find point clusters: Air quality sensors](maps/14-find-point-clusters-air-quality-sensors.png)

*Figure 15. Find point clusters: Air quality sensors [65]*

![Find point clusters: cluster hulls](maps/15-find-point-clusters-cluster-hulls.png)

*Figure 16. Find point clusters: cluster hulls [66]*

![Districts by population](maps/16-districts-by-population.png)

*Figure 17. Districts by population [67]*

![Green areas by trees](maps/17-green-areas-by-trees.png)

*Figure 18. Green areas by trees [68]*

![Find outliers: Districts](maps/18-find-outliers-districts.png)

*Figure 19. Find outliers: Districts [69]*

![Find outliers: Green areas](maps/19-find-outliers-green-areas.png)

*Figure 20. Find outliers: Green areas [70]*

![Summarize within: Smog alert areas](maps/20-summarize-within-smog-alert-areas.png)

*Figure 21. Summarize within: Smog alert areas [71]*

**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.*

![Sensor readings every three hours over time (timestamp)](charts/01-sensor-readings-every-three-hours-over-time-timestamp.png)

*Figure 22. Sensor readings every three hours over time (timestamp) [72]. Per day, 2026-01-12 to 2026-02-01. Source: Sensor readings every three hours (3,024 features). The data is in the appendix.*

![Heating and smoke complaints over time (reported_at)](charts/02-heating-and-smoke-complaints-over-time-reported-at.png)

*Figure 23. Heating and smoke complaints over time (reported_at) [73]. Per week, 2025-09-29 to 2026-03-30. Source: Heating and smoke complaints (1,000 features). The data is in the appendix.*

![Traffic counts by day over time (date)](charts/03-traffic-counts-by-day-over-time-date.png)

*Figure 24. Traffic counts by day over time (date) [74]. Per day, 2026-01-12 to 2026-02-01. Source: Traffic counts by day (420 features). The data is in the appendix.*

![Smog alert areas over time (date)](charts/04-smog-alert-areas-over-time-date.png)

*Figure 25. Smog alert areas over time (date) [75]. Per day, 2026-01-16 to 2026-01-25. Source: Smog alert areas (5 features). The data is in the appendix.*

![Emerging hot spots: Heating and smoke complaints](charts/05-emerging-hot-spots-heating-and-smoke-complaints.png)

*Figure 26. Emerging hot spots: Heating and smoke complaints [76]. Source: Emerging hot spots: Heating and smoke complaints (79 features). The data is in the appendix.*

![Repeat and near repeat classification: Heating and smoke complaints](charts/06-repeat-and-near-repeat-classification-heating-and-smoke-comp.png)

*Figure 27. Repeat and near repeat classification: Heating and smoke complaints [77]. Per week, 2025-09-29 to 2026-03-30. Source: Repeat and near repeat classification: Heating and smoke complaints (1,000 features). The data is in the appendix.*

![Summarize within: Smog alert areas](charts/07-summarize-within-smog-alert-areas.png)

*Figure 28. Summarize within: Smog alert areas [78]. Per day, 2026-01-16 to 2026-01-25. Source: Summarize within: Smog alert areas (5 features). The data is in the appendix.*

![Summarize within: Smog alert areas](charts/08-summarize-within-smog-alert-areas.png)

*Figure 29. Summarize within: Smog alert areas [79]. Per day, 2026-01-16 to 2026-01-25. Source: Summarize within: Smog alert areas (5 features). The data is in the appendix.*

![Hot spot analysis: Schools](charts/09-hot-spot-analysis-schools.png)

*Figure 30. Hot spot analysis: Schools [80]. Source: Hot spot analysis: Schools (18 features). The data is in the appendix.*

![Hot spot analysis: Heating and smoke complaints](charts/10-hot-spot-analysis-heating-and-smoke-complaints.png)

*Figure 31. Hot spot analysis: Heating and smoke complaints [81]. Source: Hot spot analysis: Heating and smoke complaints (208 features). The data is in the appendix.*

![Calculate density: Schools](charts/11-calculate-density-schools.png)

*Figure 32. Calculate density: Schools [82]. Source: Calculate density: Schools (98 features). The data is in the appendix.*

![Calculate density: Traffic counters](charts/12-calculate-density-traffic-counters.png)

*Figure 33. Calculate density: Traffic counters [83]. Source: Calculate density: Traffic counters (208 features). The data is in the appendix.*

![Calculate density: Heating and smoke complaints](charts/13-calculate-density-heating-and-smoke-complaints.png)

*Figure 34. Calculate density: Heating and smoke complaints [84]. Source: Calculate density: Heating and smoke complaints (1,379 features). The data is in the appendix.*

![Find point clusters: Schools](charts/14-find-point-clusters-schools.png)

*Figure 35. Find point clusters: Schools [85]. Source: Find point clusters: Schools (50 features). The data is in the appendix.*

![Find point clusters: cluster hulls](charts/15-find-point-clusters-cluster-hulls.png)

*Figure 36. Find point clusters: cluster hulls [86]. Source: Find point clusters: cluster hulls (7 features). The data is in the appendix.*

![Find point clusters: Traffic counters](charts/16-find-point-clusters-traffic-counters.png)

*Figure 37. Find point clusters: Traffic counters [87]. Source: Find point clusters: Traffic counters (20 features). The data is in the appendix.*

![Find point clusters: cluster hulls](charts/17-find-point-clusters-cluster-hulls.png)

*Figure 38. Find point clusters: cluster hulls [88]. Source: Find point clusters: cluster hulls (1 features). The data is in the appendix.*

![Find point clusters: Air quality sensors](charts/18-find-point-clusters-air-quality-sensors.png)

*Figure 39. Find point clusters: Air quality sensors [89]. Source: Find point clusters: Air quality sensors (18 features). The data is in the appendix.*

![Find point clusters: cluster hulls](charts/19-find-point-clusters-cluster-hulls.png)

*Figure 40. Find point clusters: cluster hulls [90]. Source: Find point clusters: cluster hulls (1 features). The data is in the appendix.*

![Find outliers: Districts](charts/20-find-outliers-districts.png)

*Figure 41. Find outliers: Districts [91]. Source: Find outliers: Districts (12 features). The data is in the appendix.*

![Find outliers: Green areas](charts/21-find-outliers-green-areas.png)

*Figure 42. Find outliers: Green areas [92]. Source: Find outliers: Green areas (15 features). The data is in the appendix.*

![Summarize within: Smog alert areas](charts/22-summarize-within-smog-alert-areas.png)

*Figure 43. Summarize within: Smog alert areas [93]. Per day, 2026-01-16 to 2026-01-25. Source: Summarize within: Smog alert areas (5 features). The data is in the appendix.*

![Summarize within: Smog alert areas over time](charts/23-summarize-within-smog-alert-areas-over-time.png)

*Figure 44. Summarize within: Smog alert areas over time [94]. Per day, 2026-01-16 to 2026-01-25. Source: Summarize within: Smog alert areas (5 features). The data is in the appendix.*

![Summarize within: Smog alert areas over time](charts/24-summarize-within-smog-alert-areas-over-time.png)

*Figure 45. Summarize within: Smog alert areas over time [95]. Per day, 2026-01-16 to 2026-01-25. Source: Summarize within: Smog alert areas (5 features). The data is in the appendix.*

![Summarize within: Smog alert areas over time](charts/25-summarize-within-smog-alert-areas-over-time.png)

*Figure 46. Summarize within: Smog alert areas over time [96]. Per day, 2026-01-16 to 2026-01-25. Source: Summarize within: Smog alert areas (5 features). The data is in the appendix.*

![Repeat and near repeat classification: Heating and smoke complaints over time](charts/26-repeat-and-near-repeat-classification-heating-and-smoke-comp.png)

*Figure 47. Repeat and near repeat classification: Heating and smoke complaints over time [97]. Per week, 2025-09-29 to 2026-03-30. Source: Repeat and near repeat classification: Heating and smoke complaints (1,000 features). The data is in the appendix.*

## Changes since the last run

This is the first report of this project.

## Sources

[1] Rendering: Chart: Timeline: events per week by dataset. 108 rows (id a084bcb1-4ebb-4e02-a2b5-e0989f373da4)  
[2] Dataset: Sensor readings every three hours. table, 3024 rows (id 0d15a1c1-4e15-46ed-8bc1-8aa3d29ccbcd)  
[3] Dataset: Heating and smoke complaints. vector, 1000 rows (id f4580557-af68-44bb-8bac-c0ee2ee81164)  
[4] Dataset: Traffic counts by day. table, 420 rows (id 4dddb911-3782-4522-b33f-66a0810f4308)  
[5] Dataset: Smog alert areas. vector, 5 rows (id 1797fddc-1ecb-4d65-bb9d-8c24fbce0b84)  
[6] Catalog: 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] Plan: 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] Tool 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] Layer: Heating and smoke complaints. 1,000 features (id 872e2359-4c4f-4b1c-a353-f558b419ae1b)  
[10] Catalog: 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] Plan: 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] Tool 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] Catalog: 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] Plan: 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] Tool 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] Layer: Schools. 50 features (id 04161d9b-b772-49f5-b451-f05a72c11340)  
[17] Layer: Smog alert areas. 5 features (id cb491d16-8d66-4ca9-8a0c-b0cca331df73)  
[18] Tool 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] Layer: Traffic counters. 20 features (id 2beeba9d-323f-4f1e-919a-5e066c70311f)  
[20] Catalog: 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] Plan: 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] Tool 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] Plan: 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] Tool 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] Catalog: 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] Plan: 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] Tool result: gp_calculate_density, step 0. Calculate density: 1 layer saved: Calculate density: Schools (98 features). (id f7011d1b-ef58-4060-8cda-1a0a0ec3fb50:0)  
[28] Plan: 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] Tool 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] Plan: 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] Tool 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] Catalog: 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] Plan: 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] Tool 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] Plan: 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] Tool 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] Plan: 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] Tool 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] Layer: Air quality sensors. 18 features (id b023a603-7600-43b0-b1a5-263889cc78ec)  
[40] Catalog: 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] Plan: 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] Tool result: gp_outliers, step 0. Find outliers: 1 layer saved: Find outliers: Districts (12 features). (id d6fde80f-96aa-463a-8668-577f95febf04:0)  
[43] Layer: Districts. 12 features (id cf8657b3-2d43-4636-9390-17763a61ba4a)  
[44] Plan: 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] Tool result: gp_outliers, step 1. Find outliers: 1 layer saved: Find outliers: Green areas (15 features). (id d6fde80f-96aa-463a-8668-577f95febf04:1)  
[46] Layer: Green areas. 15 features (id 3858215c-dede-4b5d-bb9d-c7026b082281)  
[47] Catalog: 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] Plan: 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] Tool result: gp_spatial_autocorrelation, step 2. Spatial autocorrelation: done; no map output. (id d6fde80f-96aa-463a-8668-577f95febf04:2)  
[50] Plan: 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] Tool 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] Rendering: Map: Emerging hot spots: Heating and smoke complaints (id ef0fba2c-3dab-44c8-8d1b-37950e2aa09f)  
[53] Rendering: Map: Repeat and near repeat classification: Heating and smoke complaints (id 93441096-27ae-4f30-aade-125dce56048f)  
[54] Rendering: Map: Summarize within: Smog alert areas (id 407df334-25e6-42fe-8c40-03ead11b55fc)  
[55] Rendering: Map: Summarize within: Smog alert areas (id 0034d9c7-6324-47a3-b594-1d24ebb1c534)  
[56] Rendering: Map: Hot spot analysis: Schools (id fad1fd90-84ae-4c56-a7c8-c19968f56251)  
[57] Rendering: Map: Hot spot analysis: Heating and smoke complaints (id e8903c1c-1976-4634-a49a-1eca16d457d8)  
[58] Rendering: Map: Calculate density: Schools (id dd937085-125b-4245-b19d-4df13795ffac)  
[59] Rendering: Map: Calculate density: Traffic counters (id e20a9973-68d1-4830-bbb5-9a780d6a9a33)  
[60] Rendering: Map: Calculate density: Heating and smoke complaints (id 0160d7fb-b98e-47f4-ba56-8e97beecdc89)  
[61] Rendering: Map: Find point clusters: Schools (id 2fe195f1-b867-4da5-80ca-f8476d6adc87)  
[62] Rendering: Map: Find point clusters: cluster hulls (id 0419d3be-d8f9-490f-b1f7-42948c5a9737)  
[63] Rendering: Map: Find point clusters: Traffic counters (id 0b85a9fc-34fd-43cb-84f9-2f5ffe2f143c)  
[64] Rendering: Map: Find point clusters: cluster hulls (id d057adbc-5e7a-4438-8d07-3b943401c523)  
[65] Rendering: Map: Find point clusters: Air quality sensors (id 2cb83ca5-0098-4a79-9790-1e43bfdf1db5)  
[66] Rendering: Map: Find point clusters: cluster hulls (id a67a4f33-5986-463c-bc2c-acd3018fea50)  
[67] Rendering: Map: Districts by population (id 2e54a251-6a26-402f-86e8-e6d15cab3ac3)  
[68] Rendering: Map: Green areas by trees (id 7404178c-fa56-4278-a94d-180735127ec9)  
[69] Rendering: Map: Find outliers: Districts (id 65c0124e-203f-4f91-873c-12b4b6960ae4)  
[70] Rendering: Map: Find outliers: Green areas (id dc1a86e3-09d6-402a-a3b2-c3fdd0d3eab5)  
[71] Rendering: Map: Summarize within: Smog alert areas (id a4d875cc-962b-4e3c-b64e-dd57deee34ed)  
[72] Rendering: Chart: Sensor readings every three hours over time (timestamp). 21 rows (id 8be9f734-9fc2-4614-9c16-f319eb5bc297)  
[73] Rendering: Chart: Heating and smoke complaints over time (reported_at). 108 rows (id bafb9708-0830-42a8-9dce-37a4690fb945)  
[74] Rendering: Chart: Traffic counts by day over time (date). 21 rows (id 71f35ff8-083c-4035-b705-ccab22f7bdf8)  
[75] Rendering: Chart: Smog alert areas over time (date). 20 rows (id 4033fc71-23c1-4ffa-ae99-ae32670ffd03)  
[76] Rendering: Chart: Emerging hot spots: Heating and smoke complaints. 8 rows (id 988b5149-df22-4996-bf31-ac55354d51c4)  
[77] Rendering: Chart: Repeat and near repeat classification: Heating and smoke complaints. 108 rows (id f41c1c14-81d1-4349-8443-09a4f4ba9eb6)  
[78] Rendering: Chart: Summarize within: Smog alert areas. 20 rows (id 9227b7e1-e625-47f7-80b5-495de4ab4e0a)  
[79] Rendering: Chart: Summarize within: Smog alert areas. 20 rows (id a7c52833-f238-4226-b84c-fb152e2c2eb1)  
[80] Rendering: Chart: Hot spot analysis: Schools. 7 rows (id e9ad8aa0-adb4-490e-928f-6b45446a7f1f)  
[81] Rendering: Chart: Hot spot analysis: Heating and smoke complaints. 13 rows (id d96b848c-1ca5-4e56-a8a0-bd5c1a5ae7d0)  
[82] Rendering: Chart: Calculate density: Schools. 8 rows (id 063ac921-93d3-4e7d-ab62-58eb4e9b60e6)  
[83] Rendering: Chart: Calculate density: Traffic counters. 9 rows (id 43c8bec3-da9f-4830-912a-49013656b2a0)  
[84] Rendering: Chart: Calculate density: Heating and smoke complaints. 12 rows (id 55617b3d-063a-4f9c-a0dc-71b273d1c5c5)  
[85] Rendering: Chart: Find point clusters: Schools. 8 rows (id 6776d720-700c-42c8-803c-ba17f7028e7e)  
[86] Rendering: Chart: Find point clusters: cluster hulls. 7 rows (id 9bde57a2-00d0-4bd3-a720-f74fb52e323e)  
[87] Rendering: Chart: Find point clusters: Traffic counters. 2 rows (id 7ca9a164-4408-4af2-8a82-d20106e3d63e)  
[88] Rendering: Chart: Find point clusters: cluster hulls. 1 rows (id 468903ad-2588-4bd9-b8d4-6010b36560ca)  
[89] Rendering: Chart: Find point clusters: Air quality sensors. 1 rows (id 781b4a70-d6e9-4e64-931e-192b47d86fd1)  
[90] Rendering: Chart: Find point clusters: cluster hulls. 1 rows (id 138b31d8-fc4f-44f8-8117-aecb9b369d31)  
[91] Rendering: Chart: Find outliers: Districts. 12 rows (id f6661716-8904-4181-817e-f4d3e782e217)  
[92] Rendering: Chart: Find outliers: Green areas. 15 rows (id b3b4d6ea-4b5e-4cac-8fd3-fcda036bee5c)  
[93] Rendering: Chart: Summarize within: Smog alert areas. 20 rows (id 1ceece26-9477-4f54-9fa4-1e2dbc1f96a5)  
[94] Rendering: Chart: Summarize within: Smog alert areas over time. 20 rows (id 76c45baf-feca-4a2f-a3a8-a4aa1cb03271)  
[95] Rendering: Chart: Summarize within: Smog alert areas over time. 20 rows (id fc1e8aa9-d730-49fa-9bbb-bbeacedfb972)  
[96] Rendering: Chart: Summarize within: Smog alert areas over time. 20 rows (id f7309174-c972-4de9-853f-0c6929414c1f)  
[97] Rendering: Chart: Repeat and near repeat classification: Heating and smoke complaints over time. 108 rows (id 5a51d5a0-e784-4a44-a4b6-258ad1f1e695)  
[98] Dataset: Districts. vector, 12 rows (id 26db6cdc-8ed6-4018-8216-57b31d6b7b10)  
[99] Dataset: Air quality sensors. vector, 18 rows (id 6baceea7-d2af-4370-8edd-b762c4503296)  
[100] Dataset: Major roads. vector, 20 rows (id c02faba8-cbb0-4f5c-9b60-817e3cfb35fe)  
[101] Dataset: Traffic counters. vector, 20 rows (id 3b29eef8-b3c6-476c-b293-75939220204d)  
[102] Dataset: Schools. vector, 50 rows (id 30a2dc6f-9417-4701-b099-bcebc56bd124)  
[103] Dataset: Green areas. vector, 15 rows (id 32303dbc-77ff-4931-9a5d-9590fbeb6497)  
[104] Dataset: Air quality bulletin. document, ? rows (id acc3c65d-3cae-42d7-af0f-109b23544f46)  
[105] Record: Sample: Smart city sensors, Krakow (id 941056ab-e59e-43ce-98fd-5b3678626f78)  
[106] Layer: Emerging hot spots: Heating and smoke complaints. 79 features (id bf5f1710-3502-403a-8d6a-ffe2e546b9eb)  
[107] Layer: Repeat and near repeat classification: Heating and smoke complaints. 1,000 features (id bb01a925-2b04-4941-9f49-710b9a99b0eb)  
[108] Layer: Summarize within: Smog alert areas. 5 features (id acc65faa-4c2f-47ce-af45-9d9d172f8270)  
[109] Layer: Summarize within: Smog alert areas. 5 features (id fd04402f-45d7-49e4-91f3-b84e26f37bd8)  
[110] Layer: Hot spot analysis: Schools. 18 features (id 5228d4e7-3d39-443c-85cd-530e92371dbe)  
[111] Layer: Hot spot analysis: Heating and smoke complaints. 208 features (id 16bc96b6-9c4f-4c62-b6e2-da0018f9338c)  
[112] Layer: Calculate density: Schools. 98 features (id 1263d766-433f-4232-a64f-4c671f510b22)  
[113] Layer: Calculate density: Traffic counters. 208 features (id bcbc8c7e-77b3-4bfb-882d-bad64d25f7f1)  
[114] Layer: Calculate density: Heating and smoke complaints. 1,379 features (id 54d2c3ab-0779-4796-8ab6-f64429e25aa9)  
[115] Layer: Find point clusters: Schools. 50 features (id cfc15b6a-1b6a-49f0-babb-0f62493f7ae8)  
[116] Layer: Find point clusters: cluster hulls. 7 features (id ecce6173-f6a4-413b-95e9-b66d9b514f36)  
[117] Layer: Find point clusters: Traffic counters. 20 features (id 5b1bb98f-a7b6-4e1d-9746-34bd0adff1d8)  
[118] Layer: Find point clusters: cluster hulls. 1 features (id c5d42204-a8ab-452e-b226-45831682926b)  
[119] Layer: Find point clusters: Air quality sensors. 18 features (id c7b1b5b6-45ea-4c4b-89fb-057b73041330)  
[120] Layer: Find point clusters: cluster hulls. 1 features (id 77120389-d522-4f77-b502-a63eafc89c5d)  
[121] Layer: Find outliers: Districts. 12 features (id cf03dea3-3fc9-4ee1-beb8-43ec21f5da69)  
[122] Layer: Find outliers: Green areas. 15 features (id 3e0dd3e3-729e-40f0-80b8-2477238ce9d7)  
[123] Layer: Summarize within: Smog alert areas. 5 features (id af0ec067-f4d0-4dfb-be36-930e255a6ef8)  

## Appendix: the data behind the charts

### Figure 1. Timeline: events per week by dataset [1]

| period | series | value |
| --- | --- | --- |
| 2025-09-29 | Sensor readings every three hours | 0 |
| 2025-09-29 | Heating and smoke complaints | 13 |
| 2025-09-29 | Traffic counts by day | 0 |
| 2025-09-29 | Smog alert areas | 0 |
| 2025-10-06 | Sensor readings every three hours | 0 |
| 2025-10-06 | Heating and smoke complaints | 21 |
| 2025-10-06 | Traffic counts by day | 0 |
| 2025-10-06 | Smog alert areas | 0 |
| 2025-10-13 | Sensor readings every three hours | 0 |
| 2025-10-13 | Heating and smoke complaints | 30 |
| 2025-10-13 | Traffic counts by day | 0 |
| 2025-10-13 | Smog alert areas | 0 |
| 2025-10-20 | Sensor readings every three hours | 0 |
| 2025-10-20 | Heating and smoke complaints | 24 |
| 2025-10-20 | Traffic counts by day | 0 |
| 2025-10-20 | Smog alert areas | 0 |
| 2025-10-27 | Sensor readings every three hours | 0 |
| 2025-10-27 | Heating and smoke complaints | 31 |
| 2025-10-27 | Traffic counts by day | 0 |
| 2025-10-27 | Smog alert areas | 0 |
| 2025-11-03 | Sensor readings every three hours | 0 |
| 2025-11-03 | Heating and smoke complaints | 28 |
| 2025-11-03 | Traffic counts by day | 0 |
| 2025-11-03 | Smog alert areas | 0 |
| 2025-11-10 | Sensor readings every three hours | 0 |
| 2025-11-10 | Heating and smoke complaints | 29 |
| 2025-11-10 | Traffic counts by day | 0 |
| 2025-11-10 | Smog alert areas | 0 |
| 2025-11-17 | Sensor readings every three hours | 0 |
| 2025-11-17 | Heating and smoke complaints | 35 |
| 2025-11-17 | Traffic counts by day | 0 |
| 2025-11-17 | Smog alert areas | 0 |
| 2025-11-24 | Sensor readings every three hours | 0 |
| 2025-11-24 | Heating and smoke complaints | 29 |
| 2025-11-24 | Traffic counts by day | 0 |
| 2025-11-24 | Smog alert areas | 0 |
| 2025-12-01 | Sensor readings every three hours | 0 |
| 2025-12-01 | Heating and smoke complaints | 22 |
| 2025-12-01 | Traffic counts by day | 0 |
| 2025-12-01 | Smog alert areas | 0 |
| 2025-12-08 | Sensor readings every three hours | 0 |
| 2025-12-08 | Heating and smoke complaints | 42 |
| 2025-12-08 | Traffic counts by day | 0 |
| 2025-12-08 | Smog alert areas | 0 |
| 2025-12-15 | Sensor readings every three hours | 0 |
| 2025-12-15 | Heating and smoke complaints | 40 |
| 2025-12-15 | Traffic counts by day | 0 |
| 2025-12-15 | Smog alert areas | 0 |
| 2025-12-22 | Sensor readings every three hours | 0 |
| 2025-12-22 | Heating and smoke complaints | 41 |
| 2025-12-22 | Traffic counts by day | 0 |
| 2025-12-22 | Smog alert areas | 0 |
| 2025-12-29 | Sensor readings every three hours | 0 |
| 2025-12-29 | Heating and smoke complaints | 48 |
| 2025-12-29 | Traffic counts by day | 0 |
| 2025-12-29 | Smog alert areas | 0 |
| 2026-01-05 | Sensor readings every three hours | 0 |
| 2026-01-05 | Heating and smoke complaints | 40 |
| 2026-01-05 | Traffic counts by day | 0 |
| 2026-01-05 | Smog alert areas | 0 |
| 2026-01-12 | Sensor readings every three hours | 1,008 |
| 2026-01-12 | Heating and smoke complaints | 99 |
| 2026-01-12 | Traffic counts by day | 140 |
| 2026-01-12 | Smog alert areas | 2 |
| 2026-01-19 | Sensor readings every three hours | 1,008 |
| 2026-01-19 | Heating and smoke complaints | 109 |
| 2026-01-19 | Traffic counts by day | 140 |
| 2026-01-19 | Smog alert areas | 3 |
| 2026-01-26 | Sensor readings every three hours | 1,008 |
| 2026-01-26 | Heating and smoke complaints | 37 |
| 2026-01-26 | Traffic counts by day | 140 |
| 2026-01-26 | Smog alert areas | 0 |
| 2026-02-02 | Sensor readings every three hours | 0 |
| 2026-02-02 | Heating and smoke complaints | 48 |
| 2026-02-02 | Traffic counts by day | 0 |
| 2026-02-02 | Smog alert areas | 0 |
| 2026-02-09 | Sensor readings every three hours | 0 |
| 2026-02-09 | Heating and smoke complaints | 43 |
| 2026-02-09 | Traffic counts by day | 0 |
| 2026-02-09 | Smog alert areas | 0 |
| 2026-02-16 | Sensor readings every three hours | 0 |
| 2026-02-16 | Heating and smoke complaints | 28 |
| 2026-02-16 | Traffic counts by day | 0 |
| 2026-02-16 | Smog alert areas | 0 |
| 2026-02-23 | Sensor readings every three hours | 0 |
| 2026-02-23 | Heating and smoke complaints | 35 |
| 2026-02-23 | Traffic counts by day | 0 |
| 2026-02-23 | Smog alert areas | 0 |
| 2026-03-02 | Sensor readings every three hours | 0 |
| 2026-03-02 | Heating and smoke complaints | 31 |
| 2026-03-02 | Traffic counts by day | 0 |
| 2026-03-02 | Smog alert areas | 0 |
| 2026-03-09 | Sensor readings every three hours | 0 |
| 2026-03-09 | Heating and smoke complaints | 31 |
| 2026-03-09 | Traffic counts by day | 0 |
| 2026-03-09 | Smog alert areas | 0 |
| 2026-03-16 | Sensor readings every three hours | 0 |
| 2026-03-16 | Heating and smoke complaints | 32 |
| 2026-03-16 | Traffic counts by day | 0 |
| 2026-03-16 | Smog alert areas | 0 |
| 2026-03-23 | Sensor readings every three hours | 0 |
| 2026-03-23 | Heating and smoke complaints | 22 |
| 2026-03-23 | Traffic counts by day | 0 |
| 2026-03-23 | Smog alert areas | 0 |
| 2026-03-30 | Sensor readings every three hours | 0 |
| 2026-03-30 | Heating and smoke complaints | 12 |
| 2026-03-30 | Traffic counts by day | 0 |
| 2026-03-30 | Smog alert areas | 0 |

### Figure 22. Sensor readings every three hours over time (timestamp) [72]

| period | value |
| --- | --- |
| 2026-01-12 | 144 |
| 2026-01-13 | 144 |
| 2026-01-14 | 144 |
| 2026-01-15 | 144 |
| 2026-01-16 | 144 |
| 2026-01-17 | 144 |
| 2026-01-18 | 144 |
| 2026-01-19 | 144 |
| 2026-01-20 | 144 |
| 2026-01-21 | 144 |
| 2026-01-22 | 144 |
| 2026-01-23 | 144 |
| 2026-01-24 | 144 |
| 2026-01-25 | 144 |
| 2026-01-26 | 144 |
| 2026-01-27 | 144 |
| 2026-01-28 | 144 |
| 2026-01-29 | 144 |
| 2026-01-30 | 144 |
| 2026-01-31 | 144 |
| 2026-02-01 | 144 |

### Figure 23. Heating and smoke complaints over time (reported_at) [73]

| period | series | value |
| --- | --- | --- |
| 2025-09-29 | dust | 1 |
| 2025-09-29 | smell | 4 |
| 2025-09-29 | smoke from chimney | 4 |
| 2025-09-29 | waste burning | 4 |
| 2025-10-06 | dust | 6 |
| 2025-10-06 | smell | 4 |
| 2025-10-06 | smoke from chimney | 4 |
| 2025-10-06 | waste burning | 7 |
| 2025-10-13 | dust | 7 |
| 2025-10-13 | smell | 7 |
| 2025-10-13 | smoke from chimney | 8 |
| 2025-10-13 | waste burning | 8 |
| 2025-10-20 | dust | 6 |
| 2025-10-20 | smell | 5 |
| 2025-10-20 | smoke from chimney | 8 |
| 2025-10-20 | waste burning | 5 |
| 2025-10-27 | dust | 6 |
| 2025-10-27 | smell | 13 |
| 2025-10-27 | smoke from chimney | 5 |
| 2025-10-27 | waste burning | 7 |
| 2025-11-03 | dust | 3 |
| 2025-11-03 | smell | 8 |
| 2025-11-03 | smoke from chimney | 6 |
| 2025-11-03 | waste burning | 11 |
| 2025-11-10 | dust | 4 |
| 2025-11-10 | smell | 10 |
| 2025-11-10 | smoke from chimney | 8 |
| 2025-11-10 | waste burning | 7 |
| 2025-11-17 | dust | 11 |
| 2025-11-17 | smell | 8 |
| 2025-11-17 | smoke from chimney | 8 |
| 2025-11-17 | waste burning | 8 |
| 2025-11-24 | dust | 9 |
| 2025-11-24 | smell | 7 |
| 2025-11-24 | smoke from chimney | 6 |
| 2025-11-24 | waste burning | 7 |
| 2025-12-01 | dust | 4 |
| 2025-12-01 | smell | 3 |
| 2025-12-01 | smoke from chimney | 8 |
| 2025-12-01 | waste burning | 7 |
| 2025-12-08 | dust | 9 |
| 2025-12-08 | smell | 11 |
| 2025-12-08 | smoke from chimney | 10 |
| 2025-12-08 | waste burning | 12 |
| 2025-12-15 | dust | 9 |
| 2025-12-15 | smell | 12 |
| 2025-12-15 | smoke from chimney | 12 |
| 2025-12-15 | waste burning | 7 |
| 2025-12-22 | dust | 12 |
| 2025-12-22 | smell | 11 |
| 2025-12-22 | smoke from chimney | 8 |
| 2025-12-22 | waste burning | 10 |
| 2025-12-29 | dust | 11 |
| 2025-12-29 | smell | 15 |
| 2025-12-29 | smoke from chimney | 12 |
| 2025-12-29 | waste burning | 10 |
| 2026-01-05 | dust | 16 |
| 2026-01-05 | smell | 7 |
| 2026-01-05 | smoke from chimney | 10 |
| 2026-01-05 | waste burning | 7 |
| 2026-01-12 | dust | 26 |
| 2026-01-12 | smell | 33 |
| 2026-01-12 | smoke from chimney | 23 |
| 2026-01-12 | waste burning | 17 |
| 2026-01-19 | dust | 27 |
| 2026-01-19 | smell | 33 |
| 2026-01-19 | smoke from chimney | 26 |
| 2026-01-19 | waste burning | 23 |
| 2026-01-26 | dust | 8 |
| 2026-01-26 | smell | 11 |
| 2026-01-26 | smoke from chimney | 6 |
| 2026-01-26 | waste burning | 12 |
| 2026-02-02 | dust | 12 |
| 2026-02-02 | smell | 11 |
| 2026-02-02 | smoke from chimney | 14 |
| 2026-02-02 | waste burning | 11 |
| 2026-02-09 | dust | 17 |
| 2026-02-09 | smell | 8 |
| 2026-02-09 | smoke from chimney | 6 |
| 2026-02-09 | waste burning | 12 |
| 2026-02-16 | dust | 11 |
| 2026-02-16 | smell | 3 |
| 2026-02-16 | smoke from chimney | 8 |
| 2026-02-16 | waste burning | 6 |
| 2026-02-23 | dust | 8 |
| 2026-02-23 | smell | 9 |
| 2026-02-23 | smoke from chimney | 8 |
| 2026-02-23 | waste burning | 10 |
| 2026-03-02 | dust | 8 |
| 2026-03-02 | smell | 5 |
| 2026-03-02 | smoke from chimney | 9 |
| 2026-03-02 | waste burning | 9 |
| 2026-03-09 | dust | 8 |
| 2026-03-09 | smell | 10 |
| 2026-03-09 | smoke from chimney | 8 |
| 2026-03-09 | waste burning | 5 |
| 2026-03-16 | dust | 11 |
| 2026-03-16 | smell | 6 |
| 2026-03-16 | smoke from chimney | 6 |
| 2026-03-16 | waste burning | 9 |
| 2026-03-23 | dust | 11 |
| 2026-03-23 | smell | 6 |
| 2026-03-23 | smoke from chimney | 3 |
| 2026-03-23 | waste burning | 2 |
| 2026-03-30 | dust | 4 |
| 2026-03-30 | smell | 1 |
| 2026-03-30 | smoke from chimney | 5 |
| 2026-03-30 | waste burning | 2 |

### Figure 24. Traffic counts by day over time (date) [74]

| period | value |
| --- | --- |
| 2026-01-12 | 4.817e+05 |
| 2026-01-13 | 4.534e+05 |
| 2026-01-14 | 4.68e+05 |
| 2026-01-15 | 4.975e+05 |
| 2026-01-16 | 4.796e+05 |
| 2026-01-17 | 2.394e+05 |
| 2026-01-18 | 1.577e+05 |
| 2026-01-19 | 4.808e+05 |
| 2026-01-20 | 5.42e+05 |
| 2026-01-21 | 5.074e+05 |
| 2026-01-22 | 4.305e+05 |
| 2026-01-23 | 4.501e+05 |
| 2026-01-24 | 2.25e+05 |
| 2026-01-25 | 1.746e+05 |
| 2026-01-26 | 5.535e+05 |
| 2026-01-27 | 5.047e+05 |
| 2026-01-28 | 4.7e+05 |
| 2026-01-29 | 5.047e+05 |
| 2026-01-30 | 4.434e+05 |
| 2026-01-31 | 2.208e+05 |
| 2026-02-01 | 1.835e+05 |

### Figure 25. Smog alert areas over time (date) [75]

| period | series | value |
| --- | --- | --- |
| 2026-01-16 | level 3 | 1 |
| 2026-01-16 | level 2 | 0 |
| 2026-01-17 | level 3 | 1 |
| 2026-01-17 | level 2 | 0 |
| 2026-01-18 | level 3 | 0 |
| 2026-01-18 | level 2 | 0 |
| 2026-01-19 | level 3 | 0 |
| 2026-01-19 | level 2 | 0 |
| 2026-01-20 | level 3 | 0 |
| 2026-01-20 | level 2 | 0 |
| 2026-01-21 | level 3 | 0 |
| 2026-01-21 | level 2 | 0 |
| 2026-01-22 | level 3 | 0 |
| 2026-01-22 | level 2 | 0 |
| 2026-01-23 | level 3 | 0 |
| 2026-01-23 | level 2 | 1 |
| 2026-01-24 | level 3 | 1 |
| 2026-01-24 | level 2 | 0 |
| 2026-01-25 | level 3 | 0 |
| 2026-01-25 | level 2 | 1 |

### Figure 26. Emerging hot spots: Heating and smoke complaints [76]

| bin_start | bin_end | count |
| --- | --- | --- |
| -1.039 | -0.5783 | 25 |
| -0.5783 | -0.1175 | 35 |
| -0.1175 | 0.3432 | 11 |
| 0.3432 | 0.804 | 0 |
| 0.804 | 1.265 | 2 |
| 1.265 | 1.726 | 2 |
| 1.726 | 2.186 | 3 |
| 2.186 | 2.647 | 1 |

### Figure 27. Repeat and near repeat classification: Heating and smoke complaints [77]

| period | series | value |
| --- | --- | --- |
| 2025-09-29 | dust | 1 |
| 2025-09-29 | smell | 4 |
| 2025-09-29 | smoke from chimney | 4 |
| 2025-09-29 | waste burning | 4 |
| 2025-10-06 | dust | 6 |
| 2025-10-06 | smell | 4 |
| 2025-10-06 | smoke from chimney | 4 |
| 2025-10-06 | waste burning | 7 |
| 2025-10-13 | dust | 7 |
| 2025-10-13 | smell | 7 |
| 2025-10-13 | smoke from chimney | 8 |
| 2025-10-13 | waste burning | 8 |
| 2025-10-20 | dust | 6 |
| 2025-10-20 | smell | 5 |
| 2025-10-20 | smoke from chimney | 8 |
| 2025-10-20 | waste burning | 5 |
| 2025-10-27 | dust | 6 |
| 2025-10-27 | smell | 13 |
| 2025-10-27 | smoke from chimney | 5 |
| 2025-10-27 | waste burning | 7 |
| 2025-11-03 | dust | 3 |
| 2025-11-03 | smell | 8 |
| 2025-11-03 | smoke from chimney | 6 |
| 2025-11-03 | waste burning | 11 |
| 2025-11-10 | dust | 4 |
| 2025-11-10 | smell | 10 |
| 2025-11-10 | smoke from chimney | 8 |
| 2025-11-10 | waste burning | 7 |
| 2025-11-17 | dust | 11 |
| 2025-11-17 | smell | 8 |
| 2025-11-17 | smoke from chimney | 8 |
| 2025-11-17 | waste burning | 8 |
| 2025-11-24 | dust | 9 |
| 2025-11-24 | smell | 7 |
| 2025-11-24 | smoke from chimney | 6 |
| 2025-11-24 | waste burning | 7 |
| 2025-12-01 | dust | 4 |
| 2025-12-01 | smell | 3 |
| 2025-12-01 | smoke from chimney | 8 |
| 2025-12-01 | waste burning | 7 |
| 2025-12-08 | dust | 9 |
| 2025-12-08 | smell | 11 |
| 2025-12-08 | smoke from chimney | 10 |
| 2025-12-08 | waste burning | 12 |
| 2025-12-15 | dust | 9 |
| 2025-12-15 | smell | 12 |
| 2025-12-15 | smoke from chimney | 12 |
| 2025-12-15 | waste burning | 7 |
| 2025-12-22 | dust | 12 |
| 2025-12-22 | smell | 11 |
| 2025-12-22 | smoke from chimney | 8 |
| 2025-12-22 | waste burning | 10 |
| 2025-12-29 | dust | 11 |
| 2025-12-29 | smell | 15 |
| 2025-12-29 | smoke from chimney | 12 |
| 2025-12-29 | waste burning | 10 |
| 2026-01-05 | dust | 16 |
| 2026-01-05 | smell | 7 |
| 2026-01-05 | smoke from chimney | 10 |
| 2026-01-05 | waste burning | 7 |
| 2026-01-12 | dust | 26 |
| 2026-01-12 | smell | 33 |
| 2026-01-12 | smoke from chimney | 23 |
| 2026-01-12 | waste burning | 17 |
| 2026-01-19 | dust | 27 |
| 2026-01-19 | smell | 33 |
| 2026-01-19 | smoke from chimney | 26 |
| 2026-01-19 | waste burning | 23 |
| 2026-01-26 | dust | 8 |
| 2026-01-26 | smell | 11 |
| 2026-01-26 | smoke from chimney | 6 |
| 2026-01-26 | waste burning | 12 |
| 2026-02-02 | dust | 12 |
| 2026-02-02 | smell | 11 |
| 2026-02-02 | smoke from chimney | 14 |
| 2026-02-02 | waste burning | 11 |
| 2026-02-09 | dust | 17 |
| 2026-02-09 | smell | 8 |
| 2026-02-09 | smoke from chimney | 6 |
| 2026-02-09 | waste burning | 12 |
| 2026-02-16 | dust | 11 |
| 2026-02-16 | smell | 3 |
| 2026-02-16 | smoke from chimney | 8 |
| 2026-02-16 | waste burning | 6 |
| 2026-02-23 | dust | 8 |
| 2026-02-23 | smell | 9 |
| 2026-02-23 | smoke from chimney | 8 |
| 2026-02-23 | waste burning | 10 |
| 2026-03-02 | dust | 8 |
| 2026-03-02 | smell | 5 |
| 2026-03-02 | smoke from chimney | 9 |
| 2026-03-02 | waste burning | 9 |
| 2026-03-09 | dust | 8 |
| 2026-03-09 | smell | 10 |
| 2026-03-09 | smoke from chimney | 8 |
| 2026-03-09 | waste burning | 5 |
| 2026-03-16 | dust | 11 |
| 2026-03-16 | smell | 6 |
| 2026-03-16 | smoke from chimney | 6 |
| 2026-03-16 | waste burning | 9 |
| 2026-03-23 | dust | 11 |
| 2026-03-23 | smell | 6 |
| 2026-03-23 | smoke from chimney | 3 |
| 2026-03-23 | waste burning | 2 |
| 2026-03-30 | dust | 4 |
| 2026-03-30 | smell | 1 |
| 2026-03-30 | smoke from chimney | 5 |
| 2026-03-30 | waste burning | 2 |

### Figure 28. Summarize within: Smog alert areas [78]

| period | series | value |
| --- | --- | --- |
| 2026-01-16 | level 3 | 1 |
| 2026-01-16 | level 2 | 0 |
| 2026-01-17 | level 3 | 1 |
| 2026-01-17 | level 2 | 0 |
| 2026-01-18 | level 3 | 0 |
| 2026-01-18 | level 2 | 0 |
| 2026-01-19 | level 3 | 0 |
| 2026-01-19 | level 2 | 0 |
| 2026-01-20 | level 3 | 0 |
| 2026-01-20 | level 2 | 0 |
| 2026-01-21 | level 3 | 0 |
| 2026-01-21 | level 2 | 0 |
| 2026-01-22 | level 3 | 0 |
| 2026-01-22 | level 2 | 0 |
| 2026-01-23 | level 3 | 0 |
| 2026-01-23 | level 2 | 1 |
| 2026-01-24 | level 3 | 1 |
| 2026-01-24 | level 2 | 0 |
| 2026-01-25 | level 3 | 0 |
| 2026-01-25 | level 2 | 1 |

### Figure 29. Summarize within: Smog alert areas [79]

| period | series | value |
| --- | --- | --- |
| 2026-01-16 | level 3 | 1 |
| 2026-01-16 | level 2 | 0 |
| 2026-01-17 | level 3 | 1 |
| 2026-01-17 | level 2 | 0 |
| 2026-01-18 | level 3 | 0 |
| 2026-01-18 | level 2 | 0 |
| 2026-01-19 | level 3 | 0 |
| 2026-01-19 | level 2 | 0 |
| 2026-01-20 | level 3 | 0 |
| 2026-01-20 | level 2 | 0 |
| 2026-01-21 | level 3 | 0 |
| 2026-01-21 | level 2 | 0 |
| 2026-01-22 | level 3 | 0 |
| 2026-01-22 | level 2 | 0 |
| 2026-01-23 | level 3 | 0 |
| 2026-01-23 | level 2 | 1 |
| 2026-01-24 | level 3 | 1 |
| 2026-01-24 | level 2 | 0 |
| 2026-01-25 | level 3 | 0 |
| 2026-01-25 | level 2 | 1 |

### Figure 30. Hot spot analysis: Schools [80]

| bin_start | bin_end | band | band_label | count |
| --- | --- | --- | --- | --- |
| -1.5 | -1 | not_significant | Not significant | 3 |
| -1 | -0.5 | not_significant | Not significant | 3 |
| -0.5 | 0 | not_significant | Not significant | 4 |
| 0 | 0.5 | not_significant | Not significant | 1 |
| 0.5 | 1 | not_significant | Not significant | 3 |
| 1 | 1.5 | not_significant | Not significant | 2 |
| 1.5 | 2 | hot_90 | Hot spot, 90% confidence | 2 |

### Figure 31. Hot spot analysis: Heating and smoke complaints [81]

| bin_start | bin_end | band | band_label | count |
| --- | --- | --- | --- | --- |
| -1 | -0.5 | not_significant | Not significant | 60 |
| -0.5 | 0 | not_significant | Not significant | 78 |
| 0 | 0.5 | not_significant | Not significant | 37 |
| 0.5 | 1 | not_significant | Not significant | 7 |
| 1 | 1.5 | not_significant | Not significant | 6 |
| 1.5 | 2 | hot_90 | Hot spot, 90% confidence | 1 |
| 1.5 | 2 | hot_95 | Hot spot, 95% confidence | 2 |
| 1.5 | 2 | not_significant | Not significant | 4 |
| 2 | 2.5 | hot_95 | Hot spot, 95% confidence | 4 |
| 2.5 | 3 | hot_99 | Hot spot, 99% confidence | 3 |
| 3 | 3.5 | hot_99 | Hot spot, 99% confidence | 3 |
| 3.5 | 4 | hot_99 | Hot spot, 99% confidence | 2 |
| 4.5 | 5 | hot_99 | Hot spot, 99% confidence | 1 |

### Figure 32. Calculate density: Schools [82]

| bin_start | bin_end | count |
| --- | --- | --- |
| 1.017e-05 | 0.1759 | 29 |
| 0.1759 | 0.3518 | 18 |
| 0.3518 | 0.5276 | 3 |
| 0.5276 | 0.7035 | 13 |
| 0.7035 | 0.8794 | 11 |
| 0.8794 | 1.055 | 13 |
| 1.055 | 1.231 | 6 |
| 1.231 | 1.407 | 5 |

### Figure 33. Calculate density: Traffic counters [83]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0 | 1.889 | 12 |
| 1.889 | 3.778 | 21 |
| 3.778 | 5.667 | 25 |
| 5.667 | 7.556 | 28 |
| 7.556 | 9.444 | 30 |
| 9.444 | 11.33 | 31 |
| 11.33 | 13.22 | 28 |
| 13.22 | 15.11 | 23 |
| 15.11 | 17 | 10 |

### Figure 34. Calculate density: Heating and smoke complaints [84]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0 | 3.583 | 108 |
| 3.583 | 7.167 | 136 |
| 7.167 | 10.75 | 101 |
| 10.75 | 14.33 | 135 |
| 14.33 | 17.92 | 99 |
| 17.92 | 21.5 | 134 |
| 21.5 | 25.08 | 131 |
| 25.08 | 28.67 | 93 |
| 28.67 | 32.25 | 126 |
| 32.25 | 35.83 | 99 |
| 35.83 | 39.42 | 134 |
| 39.42 | 43 | 83 |

### Figure 35. Find point clusters: Schools [85]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| Noise | 10 | 0.2 | 20.0% |
| 0 | 8 | 0.16 | 16.0% |
| 1 | 8 | 0.16 | 16.0% |
| 2 | 7 | 0.14 | 14.0% |
| 5 | 6 | 0.12 | 12.0% |
| 3 | 5 | 0.1 | 10.0% |
| 4 | 3 | 0.06 | 6.0% |
| 6 | 3 | 0.06 | 6.0% |

### Figure 36. Find point clusters: cluster hulls [86]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| 0 | 1 | 0.1429 | 14.3% |
| 1 | 1 | 0.1429 | 14.3% |
| 2 | 1 | 0.1429 | 14.3% |
| 3 | 1 | 0.1429 | 14.3% |
| 4 | 1 | 0.1429 | 14.3% |
| 5 | 1 | 0.1429 | 14.3% |
| 6 | 1 | 0.1429 | 14.3% |

### Figure 37. Find point clusters: Traffic counters [87]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| 0 | 19 | 0.95 | 95.0% |
| Noise | 1 | 0.05 | 5.0% |

### Figure 38. Find point clusters: cluster hulls [88]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| 0 | 1 | 1 | 100.0% |

### Figure 39. Find point clusters: Air quality sensors [89]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| 0 | 18 | 1 | 100.0% |

### Figure 40. Find point clusters: cluster hulls [90]

| cluster | features | fraction | share |
| --- | --- | --- | --- |
| 0 | 1 | 1 | 100.0% |

### Figure 41. Find outliers: Districts [91]

| value | lag | type |
| --- | --- | --- |
| -0.0284 | -0.4164 | NS |
| 1.289 | 0.6798 | NS |
| 0.6937 | 0.509 | NS |
| -1.093 | -0.6775 | NS |
| 0.0455 | 0.0657 | NS |
| -1.17 | -0.3544 | NS |
| 2.025 | 0.0455 | NS |
| -1.047 | -1.131 | NS |
| 0.3383 | -0.1681 | NS |
| -0.7767 | -0.4557 | NS |
| 0.6798 | 0.9914 | NS |
| -0.9569 | -0.463 | NS |

### Figure 42. Find outliers: Green areas [92]

| value | lag | type |
| --- | --- | --- |
| 0.5177 | -1.448 | NS |
| -1.387 | 1.326 | NS |
| 0.7551 | -0.3159 | NS |
| -0.1644 | -0.0869 | NS |
| -1.079 | 0.2954 | NS |
| -0.422 | -0.23 | NS |
| 2.134 | 1.003 | NS |
| 0.4369 | -1.185 | NS |
| 1.326 | 0.1405 | NS |
| 0.4571 | -0.222 | NS |
| -1.448 | 0.5177 | NS |
| -1.185 | 0.4369 | NS |
| -0.2957 | -0.0431 | NS |
| 0.6794 | 2.134 | NS |
| -0.326 | 0.3649 | NS |

### Figure 43. Summarize within: Smog alert areas [93]

| period | series | value |
| --- | --- | --- |
| 2026-01-16 | level 3 | 1 |
| 2026-01-16 | level 2 | 0 |
| 2026-01-17 | level 3 | 1 |
| 2026-01-17 | level 2 | 0 |
| 2026-01-18 | level 3 | 0 |
| 2026-01-18 | level 2 | 0 |
| 2026-01-19 | level 3 | 0 |
| 2026-01-19 | level 2 | 0 |
| 2026-01-20 | level 3 | 0 |
| 2026-01-20 | level 2 | 0 |
| 2026-01-21 | level 3 | 0 |
| 2026-01-21 | level 2 | 0 |
| 2026-01-22 | level 3 | 0 |
| 2026-01-22 | level 2 | 0 |
| 2026-01-23 | level 3 | 0 |
| 2026-01-23 | level 2 | 1 |
| 2026-01-24 | level 3 | 1 |
| 2026-01-24 | level 2 | 0 |
| 2026-01-25 | level 3 | 0 |
| 2026-01-25 | level 2 | 1 |

### Figure 44. Summarize within: Smog alert areas over time [94]

| period | series | value |
| --- | --- | --- |
| 2026-01-16 | level 3 | 1 |
| 2026-01-16 | level 2 | 0 |
| 2026-01-17 | level 3 | 1 |
| 2026-01-17 | level 2 | 0 |
| 2026-01-18 | level 3 | 0 |
| 2026-01-18 | level 2 | 0 |
| 2026-01-19 | level 3 | 0 |
| 2026-01-19 | level 2 | 0 |
| 2026-01-20 | level 3 | 0 |
| 2026-01-20 | level 2 | 0 |
| 2026-01-21 | level 3 | 0 |
| 2026-01-21 | level 2 | 0 |
| 2026-01-22 | level 3 | 0 |
| 2026-01-22 | level 2 | 0 |
| 2026-01-23 | level 3 | 0 |
| 2026-01-23 | level 2 | 1 |
| 2026-01-24 | level 3 | 1 |
| 2026-01-24 | level 2 | 0 |
| 2026-01-25 | level 3 | 0 |
| 2026-01-25 | level 2 | 1 |

### Figure 45. Summarize within: Smog alert areas over time [95]

| period | series | value |
| --- | --- | --- |
| 2026-01-16 | level 3 | 1 |
| 2026-01-16 | level 2 | 0 |
| 2026-01-17 | level 3 | 1 |
| 2026-01-17 | level 2 | 0 |
| 2026-01-18 | level 3 | 0 |
| 2026-01-18 | level 2 | 0 |
| 2026-01-19 | level 3 | 0 |
| 2026-01-19 | level 2 | 0 |
| 2026-01-20 | level 3 | 0 |
| 2026-01-20 | level 2 | 0 |
| 2026-01-21 | level 3 | 0 |
| 2026-01-21 | level 2 | 0 |
| 2026-01-22 | level 3 | 0 |
| 2026-01-22 | level 2 | 0 |
| 2026-01-23 | level 3 | 0 |
| 2026-01-23 | level 2 | 1 |
| 2026-01-24 | level 3 | 1 |
| 2026-01-24 | level 2 | 0 |
| 2026-01-25 | level 3 | 0 |
| 2026-01-25 | level 2 | 1 |

### Figure 46. Summarize within: Smog alert areas over time [96]

| period | series | value |
| --- | --- | --- |
| 2026-01-16 | level 3 | 1 |
| 2026-01-16 | level 2 | 0 |
| 2026-01-17 | level 3 | 1 |
| 2026-01-17 | level 2 | 0 |
| 2026-01-18 | level 3 | 0 |
| 2026-01-18 | level 2 | 0 |
| 2026-01-19 | level 3 | 0 |
| 2026-01-19 | level 2 | 0 |
| 2026-01-20 | level 3 | 0 |
| 2026-01-20 | level 2 | 0 |
| 2026-01-21 | level 3 | 0 |
| 2026-01-21 | level 2 | 0 |
| 2026-01-22 | level 3 | 0 |
| 2026-01-22 | level 2 | 0 |
| 2026-01-23 | level 3 | 0 |
| 2026-01-23 | level 2 | 1 |
| 2026-01-24 | level 3 | 1 |
| 2026-01-24 | level 2 | 0 |
| 2026-01-25 | level 3 | 0 |
| 2026-01-25 | level 2 | 1 |

### Figure 47. Repeat and near repeat classification: Heating and smoke complaints over time [97]

| period | series | value |
| --- | --- | --- |
| 2025-09-29 | dust | 1 |
| 2025-09-29 | smell | 4 |
| 2025-09-29 | smoke from chimney | 4 |
| 2025-09-29 | waste burning | 4 |
| 2025-10-06 | dust | 6 |
| 2025-10-06 | smell | 4 |
| 2025-10-06 | smoke from chimney | 4 |
| 2025-10-06 | waste burning | 7 |
| 2025-10-13 | dust | 7 |
| 2025-10-13 | smell | 7 |
| 2025-10-13 | smoke from chimney | 8 |
| 2025-10-13 | waste burning | 8 |
| 2025-10-20 | dust | 6 |
| 2025-10-20 | smell | 5 |
| 2025-10-20 | smoke from chimney | 8 |
| 2025-10-20 | waste burning | 5 |
| 2025-10-27 | dust | 6 |
| 2025-10-27 | smell | 13 |
| 2025-10-27 | smoke from chimney | 5 |
| 2025-10-27 | waste burning | 7 |
| 2025-11-03 | dust | 3 |
| 2025-11-03 | smell | 8 |
| 2025-11-03 | smoke from chimney | 6 |
| 2025-11-03 | waste burning | 11 |
| 2025-11-10 | dust | 4 |
| 2025-11-10 | smell | 10 |
| 2025-11-10 | smoke from chimney | 8 |
| 2025-11-10 | waste burning | 7 |
| 2025-11-17 | dust | 11 |
| 2025-11-17 | smell | 8 |
| 2025-11-17 | smoke from chimney | 8 |
| 2025-11-17 | waste burning | 8 |
| 2025-11-24 | dust | 9 |
| 2025-11-24 | smell | 7 |
| 2025-11-24 | smoke from chimney | 6 |
| 2025-11-24 | waste burning | 7 |
| 2025-12-01 | dust | 4 |
| 2025-12-01 | smell | 3 |
| 2025-12-01 | smoke from chimney | 8 |
| 2025-12-01 | waste burning | 7 |
| 2025-12-08 | dust | 9 |
| 2025-12-08 | smell | 11 |
| 2025-12-08 | smoke from chimney | 10 |
| 2025-12-08 | waste burning | 12 |
| 2025-12-15 | dust | 9 |
| 2025-12-15 | smell | 12 |
| 2025-12-15 | smoke from chimney | 12 |
| 2025-12-15 | waste burning | 7 |
| 2025-12-22 | dust | 12 |
| 2025-12-22 | smell | 11 |
| 2025-12-22 | smoke from chimney | 8 |
| 2025-12-22 | waste burning | 10 |
| 2025-12-29 | dust | 11 |
| 2025-12-29 | smell | 15 |
| 2025-12-29 | smoke from chimney | 12 |
| 2025-12-29 | waste burning | 10 |
| 2026-01-05 | dust | 16 |
| 2026-01-05 | smell | 7 |
| 2026-01-05 | smoke from chimney | 10 |
| 2026-01-05 | waste burning | 7 |
| 2026-01-12 | dust | 26 |
| 2026-01-12 | smell | 33 |
| 2026-01-12 | smoke from chimney | 23 |
| 2026-01-12 | waste burning | 17 |
| 2026-01-19 | dust | 27 |
| 2026-01-19 | smell | 33 |
| 2026-01-19 | smoke from chimney | 26 |
| 2026-01-19 | waste burning | 23 |
| 2026-01-26 | dust | 8 |
| 2026-01-26 | smell | 11 |
| 2026-01-26 | smoke from chimney | 6 |
| 2026-01-26 | waste burning | 12 |
| 2026-02-02 | dust | 12 |
| 2026-02-02 | smell | 11 |
| 2026-02-02 | smoke from chimney | 14 |
| 2026-02-02 | waste burning | 11 |
| 2026-02-09 | dust | 17 |
| 2026-02-09 | smell | 8 |
| 2026-02-09 | smoke from chimney | 6 |
| 2026-02-09 | waste burning | 12 |
| 2026-02-16 | dust | 11 |
| 2026-02-16 | smell | 3 |
| 2026-02-16 | smoke from chimney | 8 |
| 2026-02-16 | waste burning | 6 |
| 2026-02-23 | dust | 8 |
| 2026-02-23 | smell | 9 |
| 2026-02-23 | smoke from chimney | 8 |
| 2026-02-23 | waste burning | 10 |
| 2026-03-02 | dust | 8 |
| 2026-03-02 | smell | 5 |
| 2026-03-02 | smoke from chimney | 9 |
| 2026-03-02 | waste burning | 9 |
| 2026-03-09 | dust | 8 |
| 2026-03-09 | smell | 10 |
| 2026-03-09 | smoke from chimney | 8 |
| 2026-03-09 | waste burning | 5 |
| 2026-03-16 | dust | 11 |
| 2026-03-16 | smell | 6 |
| 2026-03-16 | smoke from chimney | 6 |
| 2026-03-16 | waste burning | 9 |
| 2026-03-23 | dust | 11 |
| 2026-03-23 | smell | 6 |
| 2026-03-23 | smoke from chimney | 3 |
| 2026-03-23 | waste burning | 2 |
| 2026-03-30 | dust | 4 |
| 2026-03-30 | smell | 1 |
| 2026-03-30 | smoke from chimney | 5 |
| 2026-03-30 | waste burning | 2 |


*GeoBit Large Spatial Model. Findings are labeled Evidenced (computed by a tool), Inferred (a reading or reasoning) or Unverified (no evidence yet).*