# Sample: Tourism and events, Barcelona: report

Project: Sample: Tourism and events, Barcelona  
Goal: Describe the project Sample: Tourism and events, Barcelona: run the analyses that tell the most about its data, draw every result, and write a cited report.  
Run: 31472d20-05f3-4c85-8dd4-6c9178c5f64a (running, 11 agent run(s))  
Generated: 2026-10-18T05:55:27Z, version 1  
Model cost: USD 0.0000 over 0 model call(s)  

## Data used

| Data | Kind | Rows | Time span | Summary | Source |
| --- | --- | --- | --- | --- | --- |
| Venues | vector, Point | 40 |  | 40 point features; 'kind' has 8 values (top: beach 5, stadium 5, market 5); 'capacity' 176 to 8,097 (mean 1090); named by 'name' (e.g. Beach La Estacion, Stadium Los Olivos, Market El Mercado); extent 2.1381,41.3693 to 2.2029,41.4077. Point locations of Venues by kind. | [69] |
| Footfall counters | vector, Point | 30 |  | 30 point features; 'installed_year' 2,025 to 2,025 (mean 2025); named by 'name' (e.g. Footfall counter FC01, Footfall counter FC02, Footfall counter FC03); extent 2.1471,41.3678 to 2.1956,41.4005. Point locations of Footfall counters. | [70] |
| Footfall by hour | table | 5,040 | 2026-09-14 to 2026-09-20 | Table of 5,040 rows and 3 columns without geometry; dated by 'timestamp' from 2026-09-14 to 2026-09-20; 'pedestrians' 9 to 8,318 (mean 766.5). A table of Footfall by hour without locations. | [2] |
| Visitor check-ins | vector, Point | 1,787 | 2026-08-01 to 2026-09-30 | 1,787 point features; dated by 'checked_in_at' from 2026-08-01 to 2026-09-30; 'visitor_origin' has 4 values (top: Americas 497, Asia 464, domestic 424); 'category' has 4 values (top: food 460, event 452, sightseeing 440); 'group_size' 1 to 4 (mean 2.144); extent 2.1351,41.3650 to 2.2048,41.4092. Dated point events by visitor_origin: where and when Visitor check-ins happen. | [3] |
| Hotels | vector, Point | 120 |  | 120 point features; 'stars' 2 to 5 (mean 3.408); 'rooms' 17 to 363 (mean 104.1); 'adr_eur' 71 to 510 (mean 173.1); extent 2.1363,41.3675 to 2.2041,41.4087. Point locations of Hotels. | [71] |
| Districts | vector, Polygon | 10 |  | 10 polygon (area) features; 'residents' 13,872 to 162,538 (mean 6.674e+04); 'tourist_beds' 1,594 to 7,527 (mean 4832); named by 'name' (e.g. Valle Hondo, Puente Alto Sur, Las Torres); total area 25.97 km2; extent 2.1338,41.3642 to 2.2059,41.4098. Areas (Districts) with residents to compare and map. | [72] |
| Transit stations | vector, Point | 25 |  | 25 point features; 'lines' 1 to 3 (mean 1.8); named by 'name' (e.g. Metro station La Fuente, Metro station Las Acacias, Metro station El Puerto Viejo); extent 2.1372,41.3652 to 2.2043,41.4051. Point locations of Transit stations. | [73] |
| Events | vector, Point | 60 | 2026-06-02 to 2026-10-31 | 60 point features; dated by 'date' from 2026-06-02 to 2026-10-31; 'type' has 5 values (top: exhibition 16, concert 14, match 13); 'expected_attendance' 397 to 17,281 (mean 3696); extent 2.1476,41.3713 to 2.2029,41.4047. Dated point events by type: where and when Events happen. | [4] |
| Short-term rentals | vector, Point | 359 |  | 359 point features; 'room_type' has 2 values (top: private room 196, entire home 163); 'licensed' has 2 values (top: True 198, False 161); 'nightly_eur' 44 to 363 (mean 125.7); extent 2.1359,41.3649 to 2.2041,41.4087. Point locations of Short-term rentals by room_type. | [74] |
| Hotel occupancy by district and month | table | 120 | 2025-10-01 to 2026-09-01 | Table of 120 rows and 5 columns without geometry; dated by 'month' from 2025-10-01 to 2026-09-01; 'area_code' has 10 values (top: DT01 12, DT02 12, DT03 12); 'occupancy_pct' 41 to 97.3 (mean 69.54); 'adr_eur' 99 to 219 (mean 152.6); 'beds' 809 to 8,983 (mean 4657). A table of Hotel occupancy by district and month without locations. | [5] |
| Walking routes | vector, LineString | 7 |  | 7 line features; 'step_free' has 2 values (top: True 5, False 2); 'km' 0.92 to 4.93 (mean 3.107); named by 'name' (e.g. Walking route Gallery Bajo Rio to Market Los Pinos 2, Walking route Stadium La Loma to Market Los Sauces, Walking route Beach El Faro to Concert hall La Ribera); total length 21.75 km; extent 2.1350,41.3690 to 2.2026,41.4072. Lines (Walking routes) such as routes or networks. | [75] |
| Event mobility plan | document |  |  | 1-page md document 'Event mobility plan: festival weekend 19 and 20 September'; language en; mentions 5 place(s): 41.37806, 2.17872 (1), Stadium Los Olivos (1), Stadium La Alameda (1), Footfall counter FC03 (1), Footfall (1). A document about 41.37806, 2.17872, Stadium Los Olivos, Stadium La Alameda. | [76] |
| Sample: Tourism and events, Barcelona | record |  |  |  | [77] |
| Events per 10,000 people by feature | layer, Polygon | 10 |  |  | [78] |
| Hotels per 10,000 people by feature | layer, Polygon | 10 |  |  | [79] |
| Hot spot analysis: Events | layer, Polygon | 21 |  |  | [80] |
| Hot spot analysis: Hotels | layer, Polygon | 35 |  |  | [81] |
| Emerging hot spots: Events | layer, Polygon | 12 |  |  | [82] |
| Emerging hot spots: Visitor check-ins | layer, Polygon | 247 |  |  | [83] |
| Calculate density: Events | layer, Polygon | 105 |  |  | [84] |
| Calculate density: Hotels | layer, Polygon | 160 |  |  | [85] |
| Transit stations per 10,000 people by feature | layer, Polygon | 10 |  |  | [86] |
| Hot spot analysis: Short-term rentals | layer, Polygon | 120 |  |  | [87] |
| Repeat and near repeat classification: Events | layer, Point | 60 |  |  | [88] |
| Calculate density: Transit stations | layer, Polygon | 60 |  |  | [89] |

## Timeline

![Timeline: events per month by dataset](charts/18-timeline-events-per-month-by-dataset.png)

*Figure 1. Timeline: events per month by dataset [1]. Per month, 2025-10-01 to 2026-10-01. Source: Footfall by hour, Visitor check-ins, Events, Hotel occupancy by district and month (7,007 features). The data is in the appendix.*

The timeline counts 7,007 dated events in Footfall by hour, Visitor check-ins, Events and 1 more per month, from October 2025 to October 2026 [1][2][3][4][5]. By dataset: Footfall by hour 5,040; Visitor check-ins 1,787; Hotel occupancy by district and month 120; Events 60 [1]. The trend is +185 events per month; the second half of the months has 6,937 against 60 in the first (+11461.7%) [1].

**Busiest months**

- September 2026: 6,112 events [1]
- August 2026: 756 events [1]
- June 2026: 21 events [1]

**Quietest months**

- October 2025: 10 events [1]
- November 2025: 10 events [1]
- December 2025: 10 events [1]

## Pipeline

**What this analysis is.** Pipeline: Run up to 20 GIS operations in one engine call; a step's params may use "$id" (an earlier step's result) or "$id.key", and layer references [6]. Every step's map outputs are saved as styled layers [6].

**Why it was run on this data.** Districts holds population in 'residents': Events per 10,000 people in each feature compares features of different size; mapped as a choropleth (natural breaks, sequential colors) [7].

**What it found.** Pipeline on Events, Districts: Pipeline of 2 steps (summarize_within > calculate_field): 1 layer saved [8][9][10]. It reported result, count 10; result, elapsed ms 0; billing, cost 0.0215; billing, list price 0.0215; billing, upstream cost 0; list price 0.0215 [8].

| Result | Value |
| --- | --- |
| result, count | 10 |
| result, elapsed ms | 0 |
| billing, cost | 0.0215 |
| billing, list price | 0.0215 |
| billing, upstream cost | 0 |
| list price | 0.0215 |

**Findings**

- **Evidenced:** Pipeline on Events, Districts: Pipeline of 2 steps (summarize_within > calculate_field): 1 layer saved [8][9][10].
- **Evidenced:** It reported result, count 10; result, elapsed ms 0; billing, cost 0.0215; billing, list price 0.0215; billing, upstream cost 0; list price 0.0215 [8].

*Tool gp_pipeline; parameters {"save": "final", "steps": [{"id": "counts", "params": {"features": {"layer": "Events (48b188aa)"}, "polygons": {"layer": "Districts (2a6df822)"}}, "operation": "summarize_within"}, {"id": "rate", "params": {"field": "rate_per_10k", "features": "$counts", "expression": "count * 10000 / residents"}, "operation": "calculate_field"}], "project": "7e086c07-eb20-44ad-b602-c5958e8ee13e", "output_name": *

## Pipeline

**What this analysis is.** Pipeline: Run up to 20 GIS operations in one engine call; a step's params may use "$id" (an earlier step's result) or "$id.key", and layer references [6]. Every step's map outputs are saved as styled layers [6].

**Why it was run on this data.** Districts holds population in 'residents': Events per 10,000 people in each feature compares features of different size; mapped as a choropleth (natural breaks, sequential colors) [7].

**What it found.** Pipeline on Hotels, Districts: Pipeline of 2 steps (summarize_within > calculate_field): 1 layer saved [10][11][12]. It reported result, count 10; result, elapsed ms 0; billing, cost 0.0227; billing, list price 0.0227; billing, upstream cost 0; list price 0.0227 [11].

| Result | Value |
| --- | --- |
| result, count | 10 |
| result, elapsed ms | 0 |
| billing, cost | 0.0227 |
| billing, list price | 0.0227 |
| billing, upstream cost | 0 |
| list price | 0.0227 |

**Findings**

- **Evidenced:** Pipeline on Hotels, Districts: Pipeline of 2 steps (summarize_within > calculate_field): 1 layer saved [10][11][12].
- **Evidenced:** It reported result, count 10; result, elapsed ms 0; billing, cost 0.0227; billing, list price 0.0227; billing, upstream cost 0; list price 0.0227 [11].

*Tool gp_pipeline; parameters {"save": "final", "steps": [{"id": "counts", "params": {"features": {"layer": "Hotels (3e8f4b82)"}, "polygons": {"layer": "Districts (2a6df822)"}}, "operation": "summarize_within"}, {"id": "rate", "params": {"field": "rate_per_10k", "features": "$counts", "expression": "count * 10000 / residents"}, "operation": "calculate_field"}], "project": "7e086c07-eb20-44ad-b602-c5958e8ee13e", "output_name": *

## Hot spots

**What this analysis is.** Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins [13]. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) [13].

**Why it was run on this data.** 60 points: Getis-Ord Gi* tests whether the concentrations in Events are statistically significant [14].

**What it found.** The hot spot analysis was run using the Events input layer [9], resulting in 1 layer saved labeled hot spot analysis: Events

| Result | Value |
| --- | --- |
| result, bins | 21 |
| result, count | 21 |
| result, elapsed ms | 2 |
| result, cell size m | 1,000 |
| result, incidents used | 60 |
| result, distance band m | 1,000 |
| billing, cost | 0.0512 |
| billing, list price | 0.0512 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Hot spots on Events: Hot spot analysis: 1 layer saved: Hot spot analysis: Events (21 features) [9][15].
- **Evidenced:** It reported result, bins 21; result, count 21; result, elapsed ms 2; result, cell size m 1,000; result, incidents used 60; result, distance band m 1,000 [15].

*Tool gp_hot_spots; parameters {"project": "7e086c07-eb20-44ad-b602-c5958e8ee13e", "features": {"layer": "Events (48b188aa)"}, "aggregation": "hexagon", "cell_size_m": 1000.0, "distance_band_m": 1000.0}*

## Hot spots

**What this analysis is.** Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins [13]. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) [13].

**Why it was run on this data.** 120 points: Getis-Ord Gi* tests whether the concentrations in Hotels are statistically significant [16].

**What it found.** Hot spots on Hotels: Hot spot analysis: 1 layer saved: Hot spot analysis: Hotels (35 features) [12][17]. It reported result, bins 35; result, count 35; result, elapsed ms 3; result, cell size m 1,000; result, incidents used 120; result, distance band m 1,000 [17].

| Result | Value |
| --- | --- |
| result, bins | 35 |
| result, count | 35 |
| result, elapsed ms | 3 |
| result, cell size m | 1,000 |
| result, incidents used | 120 |
| result, distance band m | 1,000 |
| billing, cost | 0.0524 |
| billing, list price | 0.0524 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Hot spots on Hotels: Hot spot analysis: 1 layer saved: Hot spot analysis: Hotels (35 features) [12][17].
- **Evidenced:** It reported result, bins 35; result, count 35; result, elapsed ms 3; result, cell size m 1,000; result, incidents used 120; result, distance band m 1,000 [17].

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

*Tool gp_hot_spots; parameters {"project": "7e086c07-eb20-44ad-b602-c5958e8ee13e", "features": {"layer": "Hotels (3e8f4b82)"}, "aggregation": "hexagon", "cell_size_m": 1000.0, "distance_band_m": 1000.0}*

## 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 [18]. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube [18].

**Why it was run on this data.** Events carry dates in 'date': emerging hot spot analysis finds new, intensifying and fading hot spots per week [19].

**What it found.** The analysis of events used a total of 60 features [20]. This process identified 12 emerging hot spots, which were saved into a layer called Emerging hot spots: Events [20]. Overall, the execution was efficient, taking only 2 elapsed milliseconds [20]. The cost calculated for this analysis was 0.103 [20].

| Result | Value |
| --- | --- |
| result, count | 12 |
| result, elapsed ms | 2 |
| result, resolution | 7 |
| result, features used | 60 |
| result, time neighbors | 0 |
| billing, cost | 0.103 |
| billing, list price | 0.103 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Space time hot spots on Events: Emerging hot spots: 1 layer saved: Emerging hot spots: Events (12 features) [9][20].
- **Evidenced:** It reported result, count 12; result, elapsed ms 2; result, resolution 7; result, features used 60; result, time neighbors 0; billing, cost 0.103 [20].

*Tool gp_space_time_hot_spots; parameters {"project": "7e086c07-eb20-44ad-b602-c5958e8ee13e", "features": {"layer": "Events (48b188aa)"}, "interval": "week", "bin_shape": "h3", "resolution": 7, "time_field": "date"}*

## 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 [18]. In ArcGIS it is Emerging Hot Spot Analysis or Create Space Time Cube [18].

**Why it was run on this data.** Visitor check-ins carry dates in 'checked_in_at': emerging hot spot analysis finds new, intensifying and fading hot spots per week [21].

**What it found.** The `gp_space_time_hot_spots` tool was run using the `Visitor check-ins` dataset [23].

| Result | Value |
| --- | --- |
| result, count | 247 |
| result, elapsed ms | 25 |
| result, resolution | 9 |
| result, features used | 1,787 |
| result, time neighbors | 0 |
| billing, cost | 0.1893 |
| billing, list price | 0.1893 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Space time hot spots on Visitor check-ins: Emerging hot spots: 1 layer saved: Emerging hot spots: Visitor check-ins (247 features) [22][23].
- **Evidenced:** It reported result, count 247; result, elapsed ms 25; result, resolution 9; result, features used 1,787; result, time neighbors 0; billing, cost 0.1893 [22].

*Tool gp_space_time_hot_spots; parameters {"project": "7e086c07-eb20-44ad-b602-c5958e8ee13e", "features": {"layer": "Visitor check-ins (517cd234)"}, "interval": "week", "bin_shape": "h3", "resolution": 9, "time_field": "checked_in_at"}*

## Calculate density

**What this analysis is.** Calculate density: Kernel density per square kilometer on a grid [24]. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation [24].

**Why it was run on this data.** 60 points in Events: a density surface shows where they concentrate [25].

**What it found.** Calculate density on Events: Calculate density: 1 layer saved: Calculate density: Events (105 features) [9][26]. It reported result, count 105; result, elapsed ms 3; result, bandwidth m 1,000; result, cell size m 500; result, excluded null 0; result, max density per km2 13.52 [26].

| Result | Value |
| --- | --- |
| result, count | 105 |
| result, elapsed ms | 3 |
| result, bandwidth m | 1,000 |
| result, cell size m | 500 |
| result, excluded null | 0 |
| result, max density per km2 | 13.52 |
| billing, cost | 0.0512 |
| billing, list price | 0.0512 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Calculate density on Events: Calculate density: 1 layer saved: Calculate density: Events (105 features) [9][26].
- **Evidenced:** It reported result, count 105; result, elapsed ms 3; result, bandwidth m 1,000; result, cell size m 500; result, excluded null 0; result, max density per km2 13.52 [26].

*Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "7e086c07-eb20-44ad-b602-c5958e8ee13e", "features": {"layer": "Events (48b188aa)"}, "bandwidth_m": 1000.0, "cell_size_m": 500.0}*

## Calculate density

**What this analysis is.** Calculate density: Kernel density per square kilometer on a grid [24]. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation [24].

**Why it was run on this data.** 120 points in Hotels: a density surface shows where they concentrate [27].

**What it found.** Calculate density on Hotels: Calculate density: 1 layer saved: Calculate density: Hotels (160 features) [12][28]. It reported result, count 160; result, elapsed ms 5; result, bandwidth m 1,000; result, cell size m 500; result, excluded null 0; result, max density per km2 19.04 [28].

| Result | Value |
| --- | --- |
| result, count | 160 |
| result, elapsed ms | 5 |
| result, bandwidth m | 1,000 |
| result, cell size m | 500 |
| result, excluded null | 0 |
| result, max density per km2 | 19.04 |
| billing, cost | 0.0524 |
| billing, list price | 0.0524 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Calculate density on Hotels: Calculate density: 1 layer saved: Calculate density: Hotels (160 features) [12][28].
- **Evidenced:** It reported result, count 160; result, elapsed ms 5; result, bandwidth m 1,000; result, cell size m 500; result, excluded null 0; result, max density per km2 19.04 [28].

*Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "7e086c07-eb20-44ad-b602-c5958e8ee13e", "features": {"layer": "Hotels (3e8f4b82)"}, "bandwidth_m": 1000.0, "cell_size_m": 500.0}*

## Pipeline

**What this analysis is.** Pipeline: Run up to 20 GIS operations in one engine call; a step's params may use "$id" (an earlier step's result) or "$id.key", and layer references [6]. Every step's map outputs are saved as styled layers [6].

**Why it was run on this data.** Districts holds population in 'residents': Transit stations per 10,000 people in each feature compares features of different size; mapped as a choropleth (natural breaks, sequential colors) [29].

**What it found.** Pipeline on Transit stations, Districts: Pipeline of 2 steps (summarize_within > calculate_field): 1 layer saved [10][30][31]. It reported result, count 10; result, elapsed ms 0; billing, cost 0.0208; billing, list price 0.0208; billing, upstream cost 0; list price 0.0208 [30].

| Result | Value |
| --- | --- |
| result, count | 10 |
| result, elapsed ms | 0 |
| billing, cost | 0.0208 |
| billing, list price | 0.0208 |
| billing, upstream cost | 0 |
| list price | 0.0208 |

**Findings**

- **Evidenced:** Pipeline on Transit stations, Districts: Pipeline of 2 steps (summarize_within > calculate_field): 1 layer saved [10][30][31].
- **Evidenced:** It reported result, count 10; result, elapsed ms 0; billing, cost 0.0208; billing, list price 0.0208; billing, upstream cost 0; list price 0.0208 [30].

*Tool gp_pipeline; parameters {"save": "final", "steps": [{"id": "counts", "params": {"features": {"layer": "Transit stations (05c69c10)"}, "polygons": {"layer": "Districts (2a6df822)"}}, "operation": "summarize_within"}, {"id": "rate", "params": {"field": "rate_per_10k", "features": "$counts", "expression": "count * 10000 / residents"}, "operation": "calculate_field"}], "project": "7e086c07-eb20-44ad-b602-c5958e8ee13e", "outp*

## Hot spots

**What this analysis is.** Hot spot analysis: Getis-Ord Gi* hot and cold spots with z-scores, p-values and confidence bins [13]. In ArcGIS it is Find Hot Spots or Hot Spot Analysis (Getis-Ord Gi*); in QGIS, qgis:hotspot (plugin) [13].

**Why it was run on this data.** 359 points: Getis-Ord Gi* tests whether the concentrations in Short-term rentals are statistically significant [32].

**What it found.** Hot spots on Short-term rentals: Hot spot analysis: 1 layer saved: Hot spot analysis: Short-term rentals (120 features) [33][34]. It reported result, bins 120; result, count 120; result, elapsed ms 6; result, cell size m 500; result, incidents used 359; result, distance band m 500 [33].

| Result | Value |
| --- | --- |
| result, bins | 120 |
| result, count | 120 |
| result, elapsed ms | 6 |
| result, cell size m | 500 |
| result, incidents used | 359 |
| result, distance band m | 500 |
| billing, cost | 0.05718 |
| billing, list price | 0.05718 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Hot spots on Short-term rentals: Hot spot analysis: 1 layer saved: Hot spot analysis: Short-term rentals (120 features) [33][34].
- **Evidenced:** It reported result, bins 120; result, count 120; result, elapsed ms 6; result, cell size m 500; result, incidents used 359; result, distance band m 500 [33].

*Tool gp_hot_spots; parameters {"project": "7e086c07-eb20-44ad-b602-c5958e8ee13e", "features": {"layer": "Short-term rentals (cfe7bd58)"}, "aggregation": "hexagon", "cell_size_m": 500.0, "distance_band_m": 500.0}*

## 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 [35]. In ArcGIS it is Repeat and Near Repeat Classification or Near Repeat Calculator [35].

**Why it was run on this data.** Near repeat analysis tests whether Events close in space also follow each other within 15 days [36].

**What it found.** Near repeat on Events: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Events (60 features) [9][37]. It reported result, days 15; result, count 60; result, chains 12; result, distance m 500; result, elapsed ms 4; result, pairs linked 27 [37].

| Result | Value |
| --- | --- |
| result, days | 15 |
| result, count | 60 |
| result, chains | 12 |
| result, distance m | 500 |
| result, elapsed ms | 4 |
| result, pairs linked | 27 |
| billing, cost | 0.0518 |
| billing, list price | 0.0518 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Near repeat on Events: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Events (60 features) [9][37].
- **Evidenced:** It reported result, days 15; result, count 60; result, chains 12; result, distance m 500; result, elapsed ms 4; result, pairs linked 27 [37].

*Tool gp_near_repeat; parameters {"days": 15, "project": "7e086c07-eb20-44ad-b602-c5958e8ee13e", "features": {"layer": "Events (48b188aa)"}, "distance_m": 500.0, "time_field": "date"}*

## Calculate density

**What this analysis is.** Calculate density: Kernel density per square kilometer on a grid [24]. In ArcGIS it is Calculate Density or Kernel Density; in QGIS, native:heatmapkerneldensityestimation [24].

**Why it was run on this data.** 25 points in Transit stations: a density surface shows where they concentrate [38].

**What it found.** The density calculation was run on the Transit stations input [39]. This process resulted in a count of 60 features, an elapsed time of 2 ms, and a bandwidth of 2,000 m [39]. The analysis reported a maximum density per km2 of 1.239 [39]. The operational cost for the density calculation was 0.0505 [39].

| Result | Value |
| --- | --- |
| result, count | 60 |
| result, elapsed ms | 2 |
| result, bandwidth m | 2,000 |
| result, cell size m | 1,000 |
| result, excluded null | 0 |
| result, max density per km2 | 1.239 |
| billing, cost | 0.0505 |
| billing, list price | 0.0505 |
| billing, upstream cost | 0 |

**Findings**

- **Evidenced:** Calculate density on Transit stations: Calculate density: 1 layer saved: Calculate density: Transit stations (60 features) [31][39].
- **Evidenced:** It reported result, count 60; result, elapsed ms 2; result, bandwidth m 2,000; result, cell size m 1,000; result, excluded null 0; result, max density per km2 1.239 [39].

*Tool gp_calculate_density; parameters {"kernel": "quartic", "project": "7e086c07-eb20-44ad-b602-c5958e8ee13e", "features": {"layer": "Transit stations (05c69c10)"}, "bandwidth_m": 2000.0, "cell_size_m": 1000.0}*

## Maps and charts

The run also made 12 map image(s) [40][41][42][43][44][45][46][47][48][49][50][51] and 17 chart(s) [52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68].

![Events per 10,000 people by feature](maps/01-events-per-10-000-people-by-feature.png)

*Figure 2. Events per 10,000 people by feature [40]*

![Hotels per 10,000 people by feature](maps/02-hotels-per-10-000-people-by-feature.png)

*Figure 3. Hotels per 10,000 people by feature [41]*

![Hot spot analysis: Events](maps/03-hot-spot-analysis-events.png)

*Figure 4. Hot spot analysis: Events [42]*

![Hot spot analysis: Hotels](maps/04-hot-spot-analysis-hotels.png)

*Figure 5. Hot spot analysis: Hotels [43]*

![Emerging hot spots: Events](maps/05-emerging-hot-spots-events.png)

*Figure 6. Emerging hot spots: Events [44]*

![Emerging hot spots: Visitor check-ins](maps/06-emerging-hot-spots-visitor-check-ins.png)

*Figure 7. Emerging hot spots: Visitor check-ins [45]*

![Calculate density: Events](maps/07-calculate-density-events.png)

*Figure 8. Calculate density: Events [46]*

![Calculate density: Hotels](maps/08-calculate-density-hotels.png)

*Figure 9. Calculate density: Hotels [47]*

![Transit stations per 10,000 people by feature](maps/09-transit-stations-per-10-000-people-by-feature.png)

*Figure 10. Transit stations per 10,000 people by feature [48]*

![Hot spot analysis: Short-term rentals](maps/10-hot-spot-analysis-short-term-rentals.png)

*Figure 11. Hot spot analysis: Short-term rentals [49]*

![Repeat and near repeat classification: Events](maps/11-repeat-and-near-repeat-classification-events.png)

*Figure 12. Repeat and near repeat classification: Events [50]*

![Calculate density: Transit stations](maps/12-calculate-density-transit-stations.png)

*Figure 13. Calculate density: Transit stations [51]*

**What the map shows.** The map displays a [42]

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

![Footfall counters over time (installed_year)](charts/01-footfall-counters-over-time-installed-year.png)

*Figure 14. Footfall counters over time (installed_year) [52]. Source: Footfall counters (30 features). The data is in the appendix.*

![Footfall by hour over time (timestamp)](charts/02-footfall-by-hour-over-time-timestamp.png)

*Figure 15. Footfall by hour over time (timestamp) [53]. Per day, 2026-09-14 to 2026-09-20. Source: Footfall by hour (5,040 features). The data is in the appendix.*

![Events over time (date)](charts/03-events-over-time-date.png)

*Figure 16. Events over time (date) [54]. Per week, 2026-06-01 to 2026-10-26. Source: Events (60 features). The data is in the appendix.*

![Hotel occupancy by district and month over time (month)](charts/04-hotel-occupancy-by-district-and-month-over-time-month.png)

*Figure 17. Hotel occupancy by district and month over time (month) [55]. Per week, 2025-09-29 to 2026-08-31. Source: Hotel occupancy by district and month (120 features). The data is in the appendix.*

![Events per 10,000 people by feature](charts/05-events-per-10-000-people-by-feature.png)

*Figure 18. Events per 10,000 people by feature [56]. Source: Events per 10,000 people by feature (10 features). The data is in the appendix.*

![Hotels per 10,000 people by feature](charts/06-hotels-per-10-000-people-by-feature.png)

*Figure 19. Hotels per 10,000 people by feature [57]. Source: Hotels per 10,000 people by feature (10 features). The data is in the appendix.*

![Hot spot analysis: Events](charts/07-hot-spot-analysis-events.png)

*Figure 20. Hot spot analysis: Events [58]. Source: Hot spot analysis: Events (21 features). The data is in the appendix.*

![Hot spot analysis: Hotels](charts/08-hot-spot-analysis-hotels.png)

*Figure 21. Hot spot analysis: Hotels [59]. Source: Hot spot analysis: Hotels (35 features). The data is in the appendix.*

![Emerging hot spots: Events](charts/09-emerging-hot-spots-events.png)

*Figure 22. Emerging hot spots: Events [60]. Source: Emerging hot spots: Events (12 features). The data is in the appendix.*

![Emerging hot spots: Visitor check-ins](charts/10-emerging-hot-spots-visitor-check-ins.png)

*Figure 23. Emerging hot spots: Visitor check-ins [61]. Source: Emerging hot spots: Visitor check-ins (247 features). The data is in the appendix.*

![Calculate density: Events](charts/11-calculate-density-events.png)

*Figure 24. Calculate density: Events [62]. Source: Calculate density: Events (105 features). The data is in the appendix.*

![Calculate density: Hotels](charts/12-calculate-density-hotels.png)

*Figure 25. Calculate density: Hotels [63]. Source: Calculate density: Hotels (160 features). The data is in the appendix.*

![Transit stations per 10,000 people by feature](charts/13-transit-stations-per-10-000-people-by-feature.png)

*Figure 26. Transit stations per 10,000 people by feature [64]. Source: Transit stations per 10,000 people by feature (10 features). The data is in the appendix.*

![Hot spot analysis: Short-term rentals](charts/14-hot-spot-analysis-short-term-rentals.png)

*Figure 27. Hot spot analysis: Short-term rentals [65]. Source: Hot spot analysis: Short-term rentals (120 features). The data is in the appendix.*

![Repeat and near repeat classification: Events](charts/15-repeat-and-near-repeat-classification-events.png)

*Figure 28. Repeat and near repeat classification: Events [66]. Per week, 2026-06-01 to 2026-10-26. Source: Repeat and near repeat classification: Events (60 features). The data is in the appendix.*

![Calculate density: Transit stations](charts/16-calculate-density-transit-stations.png)

*Figure 29. Calculate density: Transit stations [67]. Source: Calculate density: Transit stations (60 features). The data is in the appendix.*

![Repeat and near repeat classification: Events over time](charts/17-repeat-and-near-repeat-classification-events-over-time.png)

*Figure 30. Repeat and near repeat classification: Events over time [68]. Per week, 2026-06-01 to 2026-10-26. Source: Repeat and near repeat classification: Events (60 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 month by dataset. 52 rows (id c28dd5e4-8e5d-4f58-bf01-ecf5befa059e)  
[2] Dataset: Footfall by hour. table, 5040 rows (id 14c3f63e-f77c-42c5-915f-d9c05adec323)  
[3] Dataset: Visitor check-ins. vector, 1787 rows (id c5304978-960d-40ec-bdc4-acd2ea178039)  
[4] Dataset: Events. vector, 60 rows (id 19eebb1c-a097-45a3-b65f-454a721735ff)  
[5] Dataset: Hotel occupancy by district and month. table, 120 rows (id ddefeb0f-7855-4d3a-bdbc-830daaef4145)  
[6] Catalog: Pipeline (tool description, gp_pipeline). Run up to 20 GIS operations in one engine call; a step's params may use "$id" (an earlier step's result) or "$id.key", and layer references. Every step's map outputs are saved as styled layers. Never calls Mapbox. (id gp_pipeline)  
[7] Plan: Events per 10,000 people per feature of Districts (the Start plan's candidate c_fc66f1dd9ab5). Districts holds population in 'residents': Events per 10,000 people in each feature compares features of different size; mapped as a choropleth (natural breaks, sequential colors). (id 0b51cb1d-362e-46b9-bce1-ee8d0334d0d1:c_fc66f1dd9ab5)  
[8] Tool result: gp_pipeline, step 0. Pipeline of 2 steps (summarize_within > calculate_field): 1 layer saved. (id 6f508d4a-8ac6-43c5-a648-3a317b80b725:0)  
[9] Layer: Events. 60 features (id 48b188aa-e96c-47fa-8a28-11e7d0d1265b)  
[10] Layer: Districts. 10 features (id 2a6df822-dc6d-48ab-bb94-b4a34b363160)  
[11] Tool result: gp_pipeline, step 1. Pipeline of 2 steps (summarize_within > calculate_field): 1 layer saved. (id 6f508d4a-8ac6-43c5-a648-3a317b80b725:1)  
[12] Layer: Hotels. 120 features (id 3e8f4b82-1559-4b63-821d-9911e2fb57de)  
[13] 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)  
[14] Plan: Hot spots of Events (the Start plan's candidate c_7a715b334a74). 60 points: Getis-Ord Gi* tests whether the concentrations in Events are statistically significant. (id 0b51cb1d-362e-46b9-bce1-ee8d0334d0d1:c_7a715b334a74)  
[15] Tool result: gp_hot_spots, step 0. Hot spot analysis: 1 layer saved: Hot spot analysis: Events (21 features). (id 84b08bee-0a26-46a8-aa31-3ab02a56479b:0)  
[16] Plan: Hot spots of Hotels (the Start plan's candidate c_79873e91203b). 120 points: Getis-Ord Gi* tests whether the concentrations in Hotels are statistically significant. (id 0b51cb1d-362e-46b9-bce1-ee8d0334d0d1:c_79873e91203b)  
[17] Tool result: gp_hot_spots, step 1. Hot spot analysis: 1 layer saved: Hot spot analysis: Hotels (35 features). (id 84b08bee-0a26-46a8-aa31-3ab02a56479b:1)  
[18] 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)  
[19] Plan: Space time hot spots of Events (the Start plan's candidate c_8f286e9af1c8). Events carry dates in 'date': emerging hot spot analysis finds new, intensifying and fading hot spots per week. (id 0b51cb1d-362e-46b9-bce1-ee8d0334d0d1:c_8f286e9af1c8)  
[20] Tool result: gp_space_time_hot_spots, step 0. Emerging hot spots: 1 layer saved: Emerging hot spots: Events (12 features). (id eb49d63a-8c38-41f3-ab9c-5e8f86d9baac:0)  
[21] Plan: Space time hot spots of Visitor check-ins (the Start plan's candidate c_20af318bcde6). Visitor check-ins carry dates in 'checked_in_at': emerging hot spot analysis finds new, intensifying and fading hot spots per week. (id 0b51cb1d-362e-46b9-bce1-ee8d0334d0d1:c_20af318bcde6)  
[22] Tool result: gp_space_time_hot_spots, step 1. Emerging hot spots: 1 layer saved: Emerging hot spots: Visitor check-ins (247 features). (id eb49d63a-8c38-41f3-ab9c-5e8f86d9baac:1)  
[23] Layer: Visitor check-ins. 1,787 features (id 517cd234-4309-4e40-a7fe-bfcb513649a0)  
[24] 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)  
[25] Plan: Kernel density of Events (the Start plan's candidate c_cf6f233cf11a). 60 points in Events: a density surface shows where they concentrate. (id 0b51cb1d-362e-46b9-bce1-ee8d0334d0d1:c_cf6f233cf11a)  
[26] Tool result: gp_calculate_density, step 0. Calculate density: 1 layer saved: Calculate density: Events (105 features). (id ffcce236-4ccf-4454-afd4-62fde6826f22:0)  
[27] Plan: Kernel density of Hotels (the Start plan's candidate c_f2a0da30a906). 120 points in Hotels: a density surface shows where they concentrate. (id 0b51cb1d-362e-46b9-bce1-ee8d0334d0d1:c_f2a0da30a906)  
[28] Tool result: gp_calculate_density, step 1. Calculate density: 1 layer saved: Calculate density: Hotels (160 features). (id ffcce236-4ccf-4454-afd4-62fde6826f22:1)  
[29] Plan: Transit stations per 10,000 people per feature of Districts (the Start plan's candidate c_058536b42c97). Districts holds population in 'residents': Transit stations per 10,000 people in each feature compares features of different size; mapped as a choropleth (natural breaks, sequential colors). (id 0b51cb1d-362e-46b9-bce1-ee8d0334d0d1:c_058536b42c97)  
[30] Tool result: gp_pipeline, step 0. Pipeline of 2 steps (summarize_within > calculate_field): 1 layer saved. (id dd7063d1-b363-4d94-9eb7-b0637b02bd44:0)  
[31] Layer: Transit stations. 25 features (id 05c69c10-6b81-4264-aa80-dccc82f1a9c9)  
[32] Plan: Hot spots of Short-term rentals (the Start plan's candidate c_c8350b01ed0d). 359 points: Getis-Ord Gi* tests whether the concentrations in Short-term rentals are statistically significant. (id 0b51cb1d-362e-46b9-bce1-ee8d0334d0d1:c_c8350b01ed0d)  
[33] Tool result: gp_hot_spots, step 0. Hot spot analysis: 1 layer saved: Hot spot analysis: Short-term rentals (120 features). (id 90ed7ab8-143b-43f0-876d-def245599177:0)  
[34] Layer: Short-term rentals. 359 features (id cfe7bd58-22ce-4b7e-9b85-2abc3992354a)  
[35] 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)  
[36] Plan: Near repeat of Events (the Start plan's candidate c_f620f1a254d2). Near repeat analysis tests whether Events close in space also follow each other within 15 days. (id 0b51cb1d-362e-46b9-bce1-ee8d0334d0d1:c_f620f1a254d2)  
[37] Tool result: gp_near_repeat, step 0. Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Events (60 features). (id 70382b8c-45cd-4cea-8ff2-7b23d4ff890a:0)  
[38] Plan: Kernel density of Transit stations (the Start plan's candidate c_f08aaf8201f4). 25 points in Transit stations: a density surface shows where they concentrate. (id 0b51cb1d-362e-46b9-bce1-ee8d0334d0d1:c_f08aaf8201f4)  
[39] Tool result: gp_calculate_density, step 0. Calculate density: 1 layer saved: Calculate density: Transit stations (60 features). (id 2052a837-292a-4b03-bff0-c4b2801afc2e:0)  
[40] Rendering: Map: Events per 10,000 people by feature (id eec57722-0ad2-4980-8930-376540984ea4)  
[41] Rendering: Map: Hotels per 10,000 people by feature (id 63d6732e-910d-4395-a217-5bcea906df8f)  
[42] Rendering: Map: Hot spot analysis: Events (id 54872c4e-d24c-440f-8d17-ea1b17fedf7a)  
[43] Rendering: Map: Hot spot analysis: Hotels (id 0332ec77-f165-48dd-8f8b-f78eca70bd2e)  
[44] Rendering: Map: Emerging hot spots: Events (id a797e3a3-c203-46a0-adee-6ae0c49df6f7)  
[45] Rendering: Map: Emerging hot spots: Visitor check-ins (id c742998e-376d-4428-9cd8-ee43aff334d8)  
[46] Rendering: Map: Calculate density: Events (id c4858508-5b9c-4244-9da4-22c00b167e7d)  
[47] Rendering: Map: Calculate density: Hotels (id f95cc319-e8fc-4b4d-b94c-0f32f5d881ba)  
[48] Rendering: Map: Transit stations per 10,000 people by feature (id 490f3ebb-c4cf-4df7-a6b0-8a2608c0ce37)  
[49] Rendering: Map: Hot spot analysis: Short-term rentals (id 5f63f0eb-e874-46e7-9c16-5195d066b68e)  
[50] Rendering: Map: Repeat and near repeat classification: Events (id 6ead692c-c1bf-41c9-b506-1b85a1e7c4e7)  
[51] Rendering: Map: Calculate density: Transit stations (id 9c9b72cd-660b-4431-a878-d8ce46aa23b5)  
[52] Rendering: Chart: Footfall counters over time (installed_year). 1 rows (id c57481bf-4a67-476a-8f46-50e95ee01d34)  
[53] Rendering: Chart: Footfall by hour over time (timestamp). 7 rows (id 68fde6c5-f7cb-4519-beb5-fda2f50b9446)  
[54] Rendering: Chart: Events over time (date). 110 rows (id 7d7c5e9a-3392-4fe8-abd9-3e23dce93372)  
[55] Rendering: Chart: Hotel occupancy by district and month over time (month). 294 rows (id 16057d40-3ace-4329-a918-0640eb5a26c4)  
[56] Rendering: Chart: Events per 10,000 people by feature. 5 rows (id 4b2b3fe6-a4cc-4a5a-8f99-df4cfc48d3db)  
[57] Rendering: Chart: Hotels per 10,000 people by feature. 5 rows (id a4fb08df-9753-4dfe-ae78-a53901591524)  
[58] Rendering: Chart: Hot spot analysis: Events. 5 rows (id df76ff7e-00ff-4fc6-b41d-5534968aa408)  
[59] Rendering: Chart: Hot spot analysis: Hotels. 6 rows (id 49c30dd2-4afd-47ce-9cab-fd5b09afd3e2)  
[60] Rendering: Chart: Emerging hot spots: Events. 5 rows (id b15107ab-80aa-4641-95b1-be5375aa81e8)  
[61] Rendering: Chart: Emerging hot spots: Visitor check-ins. 9 rows (id c4087574-f496-4825-92c9-f13d8ca99c72)  
[62] Rendering: Chart: Calculate density: Events. 8 rows (id cd478e87-79f0-4685-a84f-ef9250389ac5)  
[63] Rendering: Chart: Calculate density: Hotels. 9 rows (id 01e6882d-fc1c-41fe-b9f0-d4c083f8ce50)  
[64] Rendering: Chart: Transit stations per 10,000 people by feature. 5 rows (id 4bfbc9fc-df18-4c54-a1e1-dc5127040af0)  
[65] Rendering: Chart: Hot spot analysis: Short-term rentals. 10 rows (id c2f2f8d1-b71f-40d0-96d6-7cd46f9331df)  
[66] Rendering: Chart: Repeat and near repeat classification: Events. 110 rows (id ae906476-4bb5-48f7-b8ac-00ca31455f0b)  
[67] Rendering: Chart: Calculate density: Transit stations. 7 rows (id d67b5707-4e19-4aa1-9943-011622237776)  
[68] Rendering: Chart: Repeat and near repeat classification: Events over time. 110 rows (id 2885d2a0-a6c2-4e19-8f57-418823738ddf)  
[69] Dataset: Venues. vector, 40 rows (id fc2cd39e-1054-4c59-b6b6-a2f324d6bb3e)  
[70] Dataset: Footfall counters. vector, 30 rows (id e0df87f0-4ea0-4648-bcf4-e339d9dd7a09)  
[71] Dataset: Hotels. vector, 120 rows (id 9b34f752-20f9-4be7-a9c5-ca3e89bf6438)  
[72] Dataset: Districts. vector, 10 rows (id 3113aeda-04c2-4be2-9eb4-1141e74fcf1d)  
[73] Dataset: Transit stations. vector, 25 rows (id a1a99e8d-74be-4d47-b8fc-3d2bc032a85e)  
[74] Dataset: Short-term rentals. vector, 359 rows (id bc26f941-1554-4b84-858e-db36a81426a5)  
[75] Dataset: Walking routes. vector, 7 rows (id 9c7a2aa2-432b-4ee7-8b1e-0b0bddcece5a)  
[76] Dataset: Event mobility plan. document, ? rows (id 552eef78-c8b9-412e-9863-a10cdaee4c8c)  
[77] Record: Sample: Tourism and events, Barcelona (id 7e086c07-eb20-44ad-b602-c5958e8ee13e)  
[78] Layer: Events per 10,000 people by feature. 10 features (id 405dd996-09a6-48ed-ba23-86c7253d6c5c)  
[79] Layer: Hotels per 10,000 people by feature. 10 features (id dafbe175-7ed5-414a-a851-63ce5848851d)  
[80] Layer: Hot spot analysis: Events. 21 features (id a5378e74-77ee-4f8e-af6b-f25d1bd7f814)  
[81] Layer: Hot spot analysis: Hotels. 35 features (id 5d3f8200-70e1-4127-b473-2986b030c3dc)  
[82] Layer: Emerging hot spots: Events. 12 features (id 4ef1cdee-955e-4bd5-8945-dcb53b0bb3b3)  
[83] Layer: Emerging hot spots: Visitor check-ins. 247 features (id 0550d911-8aec-447f-ad8b-d74332094159)  
[84] Layer: Calculate density: Events. 105 features (id db4f4d27-3ab9-4f41-8da9-01e00e1e05fd)  
[85] Layer: Calculate density: Hotels. 160 features (id 6a74aae2-e4d7-44bb-bae5-af7907cd724b)  
[86] Layer: Transit stations per 10,000 people by feature. 10 features (id dc9ada02-031c-44ad-aa58-f2cbb1ad1c06)  
[87] Layer: Hot spot analysis: Short-term rentals. 120 features (id 4e76223c-ab8d-4217-a68c-42960cd0306d)  
[88] Layer: Repeat and near repeat classification: Events. 60 features (id 095f2dfc-b0b5-4811-be3f-338320ff4791)  
[89] Layer: Calculate density: Transit stations. 60 features (id 68427dd3-936e-41d3-93aa-54b77e01fe32)  

## Appendix: the data behind the charts

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

| period | series | value |
| --- | --- | --- |
| 2025-10-01 | Footfall by hour | 0 |
| 2025-10-01 | Visitor check-ins | 0 |
| 2025-10-01 | Hotel occupancy by district and month | 10 |
| 2025-10-01 | Events | 0 |
| 2025-11-01 | Footfall by hour | 0 |
| 2025-11-01 | Visitor check-ins | 0 |
| 2025-11-01 | Hotel occupancy by district and month | 10 |
| 2025-11-01 | Events | 0 |
| 2025-12-01 | Footfall by hour | 0 |
| 2025-12-01 | Visitor check-ins | 0 |
| 2025-12-01 | Hotel occupancy by district and month | 10 |
| 2025-12-01 | Events | 0 |
| 2026-01-01 | Footfall by hour | 0 |
| 2026-01-01 | Visitor check-ins | 0 |
| 2026-01-01 | Hotel occupancy by district and month | 10 |
| 2026-01-01 | Events | 0 |
| 2026-02-01 | Footfall by hour | 0 |
| 2026-02-01 | Visitor check-ins | 0 |
| 2026-02-01 | Hotel occupancy by district and month | 10 |
| 2026-02-01 | Events | 0 |
| 2026-03-01 | Footfall by hour | 0 |
| 2026-03-01 | Visitor check-ins | 0 |
| 2026-03-01 | Hotel occupancy by district and month | 10 |
| 2026-03-01 | Events | 0 |
| 2026-04-01 | Footfall by hour | 0 |
| 2026-04-01 | Visitor check-ins | 0 |
| 2026-04-01 | Hotel occupancy by district and month | 10 |
| 2026-04-01 | Events | 0 |
| 2026-05-01 | Footfall by hour | 0 |
| 2026-05-01 | Visitor check-ins | 0 |
| 2026-05-01 | Hotel occupancy by district and month | 10 |
| 2026-05-01 | Events | 0 |
| 2026-06-01 | Footfall by hour | 0 |
| 2026-06-01 | Visitor check-ins | 0 |
| 2026-06-01 | Hotel occupancy by district and month | 10 |
| 2026-06-01 | Events | 11 |
| 2026-07-01 | Footfall by hour | 0 |
| 2026-07-01 | Visitor check-ins | 0 |
| 2026-07-01 | Hotel occupancy by district and month | 10 |
| 2026-07-01 | Events | 11 |
| 2026-08-01 | Footfall by hour | 0 |
| 2026-08-01 | Visitor check-ins | 736 |
| 2026-08-01 | Hotel occupancy by district and month | 10 |
| 2026-08-01 | Events | 10 |
| 2026-09-01 | Footfall by hour | 5,040 |
| 2026-09-01 | Visitor check-ins | 1,051 |
| 2026-09-01 | Hotel occupancy by district and month | 10 |
| 2026-09-01 | Events | 11 |
| 2026-10-01 | Footfall by hour | 0 |
| 2026-10-01 | Visitor check-ins | 0 |
| 2026-10-01 | Hotel occupancy by district and month | 0 |
| 2026-10-01 | Events | 17 |

### Figure 14. Footfall counters over time (installed_year) [52]

| bin_start | bin_end | count |
| --- | --- | --- |
| 2,025 | 2,025 | 30 |

### Figure 15. Footfall by hour over time (timestamp) [53]

| period | value |
| --- | --- |
| 2026-09-14 | 720 |
| 2026-09-15 | 720 |
| 2026-09-16 | 720 |
| 2026-09-17 | 720 |
| 2026-09-18 | 720 |
| 2026-09-19 | 720 |
| 2026-09-20 | 720 |

### Figure 16. Events over time (date) [54]

| period | series | value |
| --- | --- | --- |
| 2026-06-01 | exhibition | 0 |
| 2026-06-01 | concert | 2 |
| 2026-06-01 | match | 1 |
| 2026-06-01 | market | 0 |
| 2026-06-01 | festival | 0 |
| 2026-06-08 | exhibition | 1 |
| 2026-06-08 | concert | 0 |
| 2026-06-08 | match | 1 |
| 2026-06-08 | market | 1 |
| 2026-06-08 | festival | 0 |
| 2026-06-15 | exhibition | 0 |
| 2026-06-15 | concert | 0 |
| 2026-06-15 | match | 1 |
| 2026-06-15 | market | 1 |
| 2026-06-15 | festival | 0 |
| 2026-06-22 | exhibition | 0 |
| 2026-06-22 | concert | 1 |
| 2026-06-22 | match | 0 |
| 2026-06-22 | market | 0 |
| 2026-06-22 | festival | 0 |
| 2026-06-29 | exhibition | 3 |
| 2026-06-29 | concert | 3 |
| 2026-06-29 | match | 0 |
| 2026-06-29 | market | 0 |
| 2026-06-29 | festival | 1 |
| 2026-07-06 | exhibition | 0 |
| 2026-07-06 | concert | 1 |
| 2026-07-06 | match | 0 |
| 2026-07-06 | market | 0 |
| 2026-07-06 | festival | 0 |
| 2026-07-13 | exhibition | 1 |
| 2026-07-13 | concert | 0 |
| 2026-07-13 | match | 1 |
| 2026-07-13 | market | 0 |
| 2026-07-13 | festival | 0 |
| 2026-07-20 | exhibition | 0 |
| 2026-07-20 | concert | 0 |
| 2026-07-20 | match | 0 |
| 2026-07-20 | market | 2 |
| 2026-07-20 | festival | 0 |
| 2026-07-27 | exhibition | 1 |
| 2026-07-27 | concert | 0 |
| 2026-07-27 | match | 0 |
| 2026-07-27 | market | 0 |
| 2026-07-27 | festival | 0 |
| 2026-08-03 | exhibition | 0 |
| 2026-08-03 | concert | 0 |
| 2026-08-03 | match | 1 |
| 2026-08-03 | market | 1 |
| 2026-08-03 | festival | 0 |
| 2026-08-10 | exhibition | 1 |
| 2026-08-10 | concert | 0 |
| 2026-08-10 | match | 2 |
| 2026-08-10 | market | 0 |
| 2026-08-10 | festival | 0 |
| 2026-08-17 | exhibition | 1 |
| 2026-08-17 | concert | 1 |
| 2026-08-17 | match | 0 |
| 2026-08-17 | market | 0 |
| 2026-08-17 | festival | 0 |
| 2026-08-24 | exhibition | 0 |
| 2026-08-24 | concert | 1 |
| 2026-08-24 | match | 1 |
| 2026-08-24 | market | 0 |
| 2026-08-24 | festival | 1 |
| 2026-08-31 | exhibition | 1 |
| 2026-08-31 | concert | 1 |
| 2026-08-31 | match | 0 |
| 2026-08-31 | market | 0 |
| 2026-08-31 | festival | 0 |
| 2026-09-07 | exhibition | 1 |
| 2026-09-07 | concert | 0 |
| 2026-09-07 | match | 1 |
| 2026-09-07 | market | 1 |
| 2026-09-07 | festival | 0 |
| 2026-09-14 | exhibition | 1 |
| 2026-09-14 | concert | 0 |
| 2026-09-14 | match | 1 |
| 2026-09-14 | market | 1 |
| 2026-09-14 | festival | 3 |
| 2026-09-21 | exhibition | 0 |
| 2026-09-21 | concert | 0 |
| 2026-09-21 | match | 0 |
| 2026-09-21 | market | 0 |
| 2026-09-21 | festival | 0 |
| 2026-09-28 | exhibition | 0 |
| 2026-09-28 | concert | 0 |
| 2026-09-28 | match | 0 |
| 2026-09-28 | market | 0 |
| 2026-09-28 | festival | 0 |
| 2026-10-05 | exhibition | 0 |
| 2026-10-05 | concert | 2 |
| 2026-10-05 | match | 0 |
| 2026-10-05 | market | 1 |
| 2026-10-05 | festival | 1 |
| 2026-10-12 | exhibition | 1 |
| 2026-10-12 | concert | 0 |
| 2026-10-12 | match | 1 |
| 2026-10-12 | market | 2 |
| 2026-10-12 | festival | 0 |
| 2026-10-19 | exhibition | 2 |
| 2026-10-19 | concert | 1 |
| 2026-10-19 | match | 0 |
| 2026-10-19 | market | 0 |
| 2026-10-19 | festival | 1 |
| 2026-10-26 | exhibition | 2 |
| 2026-10-26 | concert | 1 |
| 2026-10-26 | match | 2 |
| 2026-10-26 | market | 0 |
| 2026-10-26 | festival | 0 |

### Figure 17. Hotel occupancy by district and month over time (month) [55]

| period | series | value |
| --- | --- | --- |
| 2025-09-29 | DT06 | 2,138 |
| 2025-09-29 | DT04 | 1,034 |
| 2025-09-29 | DT05 | 2,817 |
| 2025-09-29 | DT10 | 1,781 |
| 2025-09-29 | DT02 | 1,728 |
| 2025-09-29 | Other | 1.651e+04 |
| 2025-10-06 | DT06 | 0 |
| 2025-10-06 | DT04 | 0 |
| 2025-10-06 | DT05 | 0 |
| 2025-10-06 | DT10 | 0 |
| 2025-10-06 | DT02 | 0 |
| 2025-10-06 | Other | 0 |
| 2025-10-13 | DT06 | 0 |
| 2025-10-13 | DT04 | 0 |
| 2025-10-13 | DT05 | 0 |
| 2025-10-13 | DT10 | 0 |
| 2025-10-13 | DT02 | 0 |
| 2025-10-13 | Other | 0 |
| 2025-10-20 | DT06 | 0 |
| 2025-10-20 | DT04 | 0 |
| 2025-10-20 | DT05 | 0 |
| 2025-10-20 | DT10 | 0 |
| 2025-10-20 | DT02 | 0 |
| 2025-10-20 | Other | 0 |
| 2025-10-27 | DT06 | 7,226 |
| 2025-10-27 | DT04 | 8,472 |
| 2025-10-27 | DT05 | 4,464 |
| 2025-10-27 | DT10 | 6,345 |
| 2025-10-27 | DT02 | 2,589 |
| 2025-10-27 | Other | 1.248e+04 |
| 2025-11-03 | DT06 | 0 |
| 2025-11-03 | DT04 | 0 |
| 2025-11-03 | DT05 | 0 |
| 2025-11-03 | DT10 | 0 |
| 2025-11-03 | DT02 | 0 |
| 2025-11-03 | Other | 0 |
| 2025-11-10 | DT06 | 0 |
| 2025-11-10 | DT04 | 0 |
| 2025-11-10 | DT05 | 0 |
| 2025-11-10 | DT10 | 0 |
| 2025-11-10 | DT02 | 0 |
| 2025-11-10 | Other | 0 |
| 2025-11-17 | DT06 | 0 |
| 2025-11-17 | DT04 | 0 |
| 2025-11-17 | DT05 | 0 |
| 2025-11-17 | DT10 | 0 |
| 2025-11-17 | DT02 | 0 |
| 2025-11-17 | Other | 0 |
| 2025-11-24 | DT06 | 0 |
| 2025-11-24 | DT04 | 0 |
| 2025-11-24 | DT05 | 0 |
| 2025-11-24 | DT10 | 0 |
| 2025-11-24 | DT02 | 0 |
| 2025-11-24 | Other | 0 |
| 2025-12-01 | DT06 | 8,013 |
| 2025-12-01 | DT04 | 3,856 |
| 2025-12-01 | DT05 | 5,653 |
| 2025-12-01 | DT10 | 8,983 |
| 2025-12-01 | DT02 | 8,363 |
| 2025-12-01 | Other | 2.188e+04 |
| 2025-12-08 | DT06 | 0 |
| 2025-12-08 | DT04 | 0 |
| 2025-12-08 | DT05 | 0 |
| 2025-12-08 | DT10 | 0 |
| 2025-12-08 | DT02 | 0 |
| 2025-12-08 | Other | 0 |
| 2025-12-15 | DT06 | 0 |
| 2025-12-15 | DT04 | 0 |
| 2025-12-15 | DT05 | 0 |
| 2025-12-15 | DT10 | 0 |
| 2025-12-15 | DT02 | 0 |
| 2025-12-15 | Other | 0 |
| 2025-12-22 | DT06 | 0 |
| 2025-12-22 | DT04 | 0 |
| 2025-12-22 | DT05 | 0 |
| 2025-12-22 | DT10 | 0 |
| 2025-12-22 | DT02 | 0 |
| 2025-12-22 | Other | 0 |
| 2025-12-29 | DT06 | 8,732 |
| 2025-12-29 | DT04 | 3,728 |
| 2025-12-29 | DT05 | 2,054 |
| 2025-12-29 | DT10 | 6,927 |
| 2025-12-29 | DT02 | 7,321 |
| 2025-12-29 | Other | 2.694e+04 |
| 2026-01-05 | DT06 | 0 |
| 2026-01-05 | DT04 | 0 |
| 2026-01-05 | DT05 | 0 |
| 2026-01-05 | DT10 | 0 |
| 2026-01-05 | DT02 | 0 |
| 2026-01-05 | Other | 0 |
| 2026-01-12 | DT06 | 0 |
| 2026-01-12 | DT04 | 0 |
| 2026-01-12 | DT05 | 0 |
| 2026-01-12 | DT10 | 0 |
| 2026-01-12 | DT02 | 0 |
| 2026-01-12 | Other | 0 |
| 2026-01-19 | DT06 | 0 |
| 2026-01-19 | DT04 | 0 |
| 2026-01-19 | DT05 | 0 |
| 2026-01-19 | DT10 | 0 |
| 2026-01-19 | DT02 | 0 |
| 2026-01-19 | Other | 0 |
| 2026-01-26 | DT06 | 2,115 |
| 2026-01-26 | DT04 | 5,118 |
| 2026-01-26 | DT05 | 3,277 |
| 2026-01-26 | DT10 | 5,816 |
| 2026-01-26 | DT02 | 7,018 |
| 2026-01-26 | Other | 2.137e+04 |
| 2026-02-02 | DT06 | 0 |
| 2026-02-02 | DT04 | 0 |
| 2026-02-02 | DT05 | 0 |
| 2026-02-02 | DT10 | 0 |
| 2026-02-02 | DT02 | 0 |
| 2026-02-02 | Other | 0 |
| 2026-02-09 | DT06 | 0 |
| 2026-02-09 | DT04 | 0 |
| 2026-02-09 | DT05 | 0 |
| 2026-02-09 | DT10 | 0 |
| 2026-02-09 | DT02 | 0 |
| 2026-02-09 | Other | 0 |
| 2026-02-16 | DT06 | 0 |
| 2026-02-16 | DT04 | 0 |
| 2026-02-16 | DT05 | 0 |
| 2026-02-16 | DT10 | 0 |
| 2026-02-16 | DT02 | 0 |
| 2026-02-16 | Other | 0 |
| 2026-02-23 | DT06 | 3,579 |
| 2026-02-23 | DT04 | 2,962 |
| 2026-02-23 | DT05 | 7,288 |
| 2026-02-23 | DT10 | 3,713 |
| 2026-02-23 | DT02 | 2,970 |
| 2026-02-23 | Other | 3.058e+04 |
| 2026-03-02 | DT06 | 0 |
| 2026-03-02 | DT04 | 0 |
| 2026-03-02 | DT05 | 0 |
| 2026-03-02 | DT10 | 0 |
| 2026-03-02 | DT02 | 0 |
| 2026-03-02 | Other | 0 |
| 2026-03-09 | DT06 | 0 |
| 2026-03-09 | DT04 | 0 |
| 2026-03-09 | DT05 | 0 |
| 2026-03-09 | DT10 | 0 |
| 2026-03-09 | DT02 | 0 |
| 2026-03-09 | Other | 0 |
| 2026-03-16 | DT06 | 0 |
| 2026-03-16 | DT04 | 0 |
| 2026-03-16 | DT05 | 0 |
| 2026-03-16 | DT10 | 0 |
| 2026-03-16 | DT02 | 0 |
| 2026-03-16 | Other | 0 |
| 2026-03-23 | DT06 | 0 |
| 2026-03-23 | DT04 | 0 |
| 2026-03-23 | DT05 | 0 |
| 2026-03-23 | DT10 | 0 |
| 2026-03-23 | DT02 | 0 |
| 2026-03-23 | Other | 0 |
| 2026-03-30 | DT06 | 5,650 |
| 2026-03-30 | DT04 | 6,950 |
| 2026-03-30 | DT05 | 6,796 |
| 2026-03-30 | DT10 | 4,404 |
| 2026-03-30 | DT02 | 3,479 |
| 2026-03-30 | Other | 1.511e+04 |
| 2026-04-06 | DT06 | 0 |
| 2026-04-06 | DT04 | 0 |
| 2026-04-06 | DT05 | 0 |
| 2026-04-06 | DT10 | 0 |
| 2026-04-06 | DT02 | 0 |
| 2026-04-06 | Other | 0 |
| 2026-04-13 | DT06 | 0 |
| 2026-04-13 | DT04 | 0 |
| 2026-04-13 | DT05 | 0 |
| 2026-04-13 | DT10 | 0 |
| 2026-04-13 | DT02 | 0 |
| 2026-04-13 | Other | 0 |
| 2026-04-20 | DT06 | 0 |
| 2026-04-20 | DT04 | 0 |
| 2026-04-20 | DT05 | 0 |
| 2026-04-20 | DT10 | 0 |
| 2026-04-20 | DT02 | 0 |
| 2026-04-20 | Other | 0 |
| 2026-04-27 | DT06 | 7,955 |
| 2026-04-27 | DT04 | 2,998 |
| 2026-04-27 | DT05 | 2,479 |
| 2026-04-27 | DT10 | 3,806 |
| 2026-04-27 | DT02 | 4,230 |
| 2026-04-27 | Other | 2.721e+04 |
| 2026-05-04 | DT06 | 0 |
| 2026-05-04 | DT04 | 0 |
| 2026-05-04 | DT05 | 0 |
| 2026-05-04 | DT10 | 0 |
| 2026-05-04 | DT02 | 0 |
| 2026-05-04 | Other | 0 |
| 2026-05-11 | DT06 | 0 |
| 2026-05-11 | DT04 | 0 |
| 2026-05-11 | DT05 | 0 |
| 2026-05-11 | DT10 | 0 |
| 2026-05-11 | DT02 | 0 |
| 2026-05-11 | Other | 0 |
| 2026-05-18 | DT06 | 0 |
| 2026-05-18 | DT04 | 0 |
| 2026-05-18 | DT05 | 0 |
| 2026-05-18 | DT10 | 0 |
| 2026-05-18 | DT02 | 0 |
| 2026-05-18 | Other | 0 |
| 2026-05-25 | DT06 | 0 |
| 2026-05-25 | DT04 | 0 |
| 2026-05-25 | DT05 | 0 |
| 2026-05-25 | DT10 | 0 |
| 2026-05-25 | DT02 | 0 |
| 2026-05-25 | Other | 0 |
| 2026-06-01 | DT06 | 5,440 |
| 2026-06-01 | DT04 | 6,136 |
| 2026-06-01 | DT05 | 7,347 |
| 2026-06-01 | DT10 | 3,873 |
| 2026-06-01 | DT02 | 4,157 |
| 2026-06-01 | Other | 2.078e+04 |
| 2026-06-08 | DT06 | 0 |
| 2026-06-08 | DT04 | 0 |
| 2026-06-08 | DT05 | 0 |
| 2026-06-08 | DT10 | 0 |
| 2026-06-08 | DT02 | 0 |
| 2026-06-08 | Other | 0 |
| 2026-06-15 | DT06 | 0 |
| 2026-06-15 | DT04 | 0 |
| 2026-06-15 | DT05 | 0 |
| 2026-06-15 | DT10 | 0 |
| 2026-06-15 | DT02 | 0 |
| 2026-06-15 | Other | 0 |
| 2026-06-22 | DT06 | 0 |
| 2026-06-22 | DT04 | 0 |
| 2026-06-22 | DT05 | 0 |
| 2026-06-22 | DT10 | 0 |
| 2026-06-22 | DT02 | 0 |
| 2026-06-22 | Other | 0 |
| 2026-06-29 | DT06 | 7,881 |
| 2026-06-29 | DT04 | 6,851 |
| 2026-06-29 | DT05 | 1,454 |
| 2026-06-29 | DT10 | 1,762 |
| 2026-06-29 | DT02 | 1,279 |
| 2026-06-29 | Other | 1.875e+04 |
| 2026-07-06 | DT06 | 0 |
| 2026-07-06 | DT04 | 0 |
| 2026-07-06 | DT05 | 0 |
| 2026-07-06 | DT10 | 0 |
| 2026-07-06 | DT02 | 0 |
| 2026-07-06 | Other | 0 |
| 2026-07-13 | DT06 | 0 |
| 2026-07-13 | DT04 | 0 |
| 2026-07-13 | DT05 | 0 |
| 2026-07-13 | DT10 | 0 |
| 2026-07-13 | DT02 | 0 |
| 2026-07-13 | Other | 0 |
| 2026-07-20 | DT06 | 0 |
| 2026-07-20 | DT04 | 0 |
| 2026-07-20 | DT05 | 0 |
| 2026-07-20 | DT10 | 0 |
| 2026-07-20 | DT02 | 0 |
| 2026-07-20 | Other | 0 |
| 2026-07-27 | DT06 | 2,819 |
| 2026-07-27 | DT04 | 7,853 |
| 2026-07-27 | DT05 | 5,302 |
| 2026-07-27 | DT10 | 1,915 |
| 2026-07-27 | DT02 | 5,724 |
| 2026-07-27 | Other | 1.762e+04 |
| 2026-08-03 | DT06 | 0 |
| 2026-08-03 | DT04 | 0 |
| 2026-08-03 | DT05 | 0 |
| 2026-08-03 | DT10 | 0 |
| 2026-08-03 | DT02 | 0 |
| 2026-08-03 | Other | 0 |
| 2026-08-10 | DT06 | 0 |
| 2026-08-10 | DT04 | 0 |
| 2026-08-10 | DT05 | 0 |
| 2026-08-10 | DT10 | 0 |
| 2026-08-10 | DT02 | 0 |
| 2026-08-10 | Other | 0 |
| 2026-08-17 | DT06 | 0 |
| 2026-08-17 | DT04 | 0 |
| 2026-08-17 | DT05 | 0 |
| 2026-08-17 | DT10 | 0 |
| 2026-08-17 | DT02 | 0 |
| 2026-08-17 | Other | 0 |
| 2026-08-24 | DT06 | 0 |
| 2026-08-24 | DT04 | 0 |
| 2026-08-24 | DT05 | 0 |
| 2026-08-24 | DT10 | 0 |
| 2026-08-24 | DT02 | 0 |
| 2026-08-24 | Other | 0 |
| 2026-08-31 | DT06 | 4,125 |
| 2026-08-31 | DT04 | 8,231 |
| 2026-08-31 | DT05 | 8,813 |
| 2026-08-31 | DT10 | 7,608 |
| 2026-08-31 | DT02 | 7,592 |
| 2026-08-31 | Other | 2.857e+04 |

### Figure 18. Events per 10,000 people by feature [56]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0 | 8 | 9 |
| 8 | 16 | 0 |
| 16 | 24 | 0 |
| 24 | 32 | 0 |
| 32 | 40 | 1 |

### Figure 19. Hotels per 10,000 people by feature [57]

| bin_start | bin_end | count |
| --- | --- | --- |
| 1 | 11.2 | 7 |
| 11.2 | 21.4 | 1 |
| 21.4 | 31.6 | 1 |
| 31.6 | 41.8 | 0 |
| 41.8 | 52 | 1 |

### Figure 20. Hot spot analysis: Events [58]

| bin_start | bin_end | band | band_label | count |
| --- | --- | --- | --- | --- |
| -1 | -0.5 | not_significant | Not significant | 7 |
| -0.5 | 0 | not_significant | Not significant | 7 |
| 0 | 0.5 | not_significant | Not significant | 5 |
| 2.5 | 3 | hot_95 | Hot spot, 95% confidence | 1 |
| 3 | 3.5 | hot_99 | Hot spot, 99% confidence | 1 |

### Figure 21. Hot spot analysis: Hotels [59]

| bin_start | bin_end | band | band_label | count |
| --- | --- | --- | --- | --- |
| -1 | -0.5 | not_significant | Not significant | 14 |
| -0.5 | 0 | not_significant | Not significant | 8 |
| 0 | 0.5 | not_significant | Not significant | 6 |
| 0.5 | 1 | not_significant | Not significant | 4 |
| 1 | 1.5 | not_significant | Not significant | 1 |
| 3 | 3.5 | hot_99 | Hot spot, 99% confidence | 2 |

### Figure 22. Emerging hot spots: Events [60]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0.038 | 0.871 | 4 |
| 0.871 | 1.704 | 1 |
| 1.704 | 2.537 | 1 |
| 2.537 | 3.37 | 3 |
| 3.37 | 4.203 | 3 |

### Figure 23. Emerging hot spots: Visitor check-ins [61]

| bin_start | bin_end | count |
| --- | --- | --- |
| -1.056 | -0.5576 | 150 |
| -0.5576 | -0.05911 | 59 |
| -0.05911 | 0.4393 | 15 |
| 0.4393 | 0.9378 | 10 |
| 0.9378 | 1.436 | 3 |
| 1.436 | 1.935 | 3 |
| 1.935 | 2.433 | 3 |
| 2.433 | 2.932 | 2 |
| 2.932 | 3.43 | 2 |

### Figure 24. Calculate density: Events [62]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0 | 1.5 | 11 |
| 1.5 | 3 | 8 |
| 3 | 4.5 | 17 |
| 4.5 | 6 | 10 |
| 6 | 7.5 | 17 |
| 7.5 | 9 | 8 |
| 9 | 10.5 | 20 |
| 10.5 | 12 | 14 |

### Figure 25. Calculate density: Hotels [63]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0 | 1.556 | 16 |
| 1.556 | 3.111 | 26 |
| 3.111 | 4.667 | 13 |
| 4.667 | 6.222 | 23 |
| 6.222 | 7.778 | 12 |
| 7.778 | 9.333 | 22 |
| 9.333 | 10.89 | 11 |
| 10.89 | 12.44 | 23 |
| 12.44 | 14 | 14 |

### Figure 26. Transit stations per 10,000 people by feature [64]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0 | 1 | 1 |
| 1 | 2 | 3 |
| 2 | 3 | 2 |
| 3 | 4 | 2 |
| 4 | 5 | 2 |

### Figure 27. Hot spot analysis: Short-term rentals [65]

| bin_start | bin_end | band | band_label | count |
| --- | --- | --- | --- | --- |
| -1 | -0.5 | not_significant | Not significant | 50 |
| -0.5 | 0 | not_significant | Not significant | 22 |
| 0 | 0.5 | not_significant | Not significant | 24 |
| 0.5 | 1 | not_significant | Not significant | 10 |
| 1 | 1.5 | not_significant | Not significant | 1 |
| 1.5 | 2 | hot_90 | Hot spot, 90% confidence | 4 |
| 1.5 | 2 | hot_95 | Hot spot, 95% confidence | 3 |
| 2.5 | 3 | hot_95 | Hot spot, 95% confidence | 2 |
| 3 | 3.5 | hot_99 | Hot spot, 99% confidence | 3 |
| 3.5 | 4 | hot_99 | Hot spot, 99% confidence | 1 |

### Figure 28. Repeat and near repeat classification: Events [66]

| period | series | value |
| --- | --- | --- |
| 2026-06-01 | exhibition | 0 |
| 2026-06-01 | concert | 2 |
| 2026-06-01 | match | 1 |
| 2026-06-01 | market | 0 |
| 2026-06-01 | festival | 0 |
| 2026-06-08 | exhibition | 1 |
| 2026-06-08 | concert | 0 |
| 2026-06-08 | match | 1 |
| 2026-06-08 | market | 1 |
| 2026-06-08 | festival | 0 |
| 2026-06-15 | exhibition | 0 |
| 2026-06-15 | concert | 0 |
| 2026-06-15 | match | 1 |
| 2026-06-15 | market | 1 |
| 2026-06-15 | festival | 0 |
| 2026-06-22 | exhibition | 0 |
| 2026-06-22 | concert | 1 |
| 2026-06-22 | match | 0 |
| 2026-06-22 | market | 0 |
| 2026-06-22 | festival | 0 |
| 2026-06-29 | exhibition | 3 |
| 2026-06-29 | concert | 3 |
| 2026-06-29 | match | 0 |
| 2026-06-29 | market | 0 |
| 2026-06-29 | festival | 1 |
| 2026-07-06 | exhibition | 0 |
| 2026-07-06 | concert | 1 |
| 2026-07-06 | match | 0 |
| 2026-07-06 | market | 0 |
| 2026-07-06 | festival | 0 |
| 2026-07-13 | exhibition | 1 |
| 2026-07-13 | concert | 0 |
| 2026-07-13 | match | 1 |
| 2026-07-13 | market | 0 |
| 2026-07-13 | festival | 0 |
| 2026-07-20 | exhibition | 0 |
| 2026-07-20 | concert | 0 |
| 2026-07-20 | match | 0 |
| 2026-07-20 | market | 2 |
| 2026-07-20 | festival | 0 |
| 2026-07-27 | exhibition | 1 |
| 2026-07-27 | concert | 0 |
| 2026-07-27 | match | 0 |
| 2026-07-27 | market | 0 |
| 2026-07-27 | festival | 0 |
| 2026-08-03 | exhibition | 0 |
| 2026-08-03 | concert | 0 |
| 2026-08-03 | match | 1 |
| 2026-08-03 | market | 1 |
| 2026-08-03 | festival | 0 |
| 2026-08-10 | exhibition | 1 |
| 2026-08-10 | concert | 0 |
| 2026-08-10 | match | 2 |
| 2026-08-10 | market | 0 |
| 2026-08-10 | festival | 0 |
| 2026-08-17 | exhibition | 1 |
| 2026-08-17 | concert | 1 |
| 2026-08-17 | match | 0 |
| 2026-08-17 | market | 0 |
| 2026-08-17 | festival | 0 |
| 2026-08-24 | exhibition | 0 |
| 2026-08-24 | concert | 1 |
| 2026-08-24 | match | 1 |
| 2026-08-24 | market | 0 |
| 2026-08-24 | festival | 1 |
| 2026-08-31 | exhibition | 1 |
| 2026-08-31 | concert | 1 |
| 2026-08-31 | match | 0 |
| 2026-08-31 | market | 0 |
| 2026-08-31 | festival | 0 |
| 2026-09-07 | exhibition | 1 |
| 2026-09-07 | concert | 0 |
| 2026-09-07 | match | 1 |
| 2026-09-07 | market | 1 |
| 2026-09-07 | festival | 0 |
| 2026-09-14 | exhibition | 1 |
| 2026-09-14 | concert | 0 |
| 2026-09-14 | match | 1 |
| 2026-09-14 | market | 1 |
| 2026-09-14 | festival | 3 |
| 2026-09-21 | exhibition | 0 |
| 2026-09-21 | concert | 0 |
| 2026-09-21 | match | 0 |
| 2026-09-21 | market | 0 |
| 2026-09-21 | festival | 0 |
| 2026-09-28 | exhibition | 0 |
| 2026-09-28 | concert | 0 |
| 2026-09-28 | match | 0 |
| 2026-09-28 | market | 0 |
| 2026-09-28 | festival | 0 |
| 2026-10-05 | exhibition | 0 |
| 2026-10-05 | concert | 2 |
| 2026-10-05 | match | 0 |
| 2026-10-05 | market | 1 |
| 2026-10-05 | festival | 1 |
| 2026-10-12 | exhibition | 1 |
| 2026-10-12 | concert | 0 |
| 2026-10-12 | match | 1 |
| 2026-10-12 | market | 2 |
| 2026-10-12 | festival | 0 |
| 2026-10-19 | exhibition | 2 |
| 2026-10-19 | concert | 1 |
| 2026-10-19 | match | 0 |
| 2026-10-19 | market | 0 |
| 2026-10-19 | festival | 1 |
| 2026-10-26 | exhibition | 2 |
| 2026-10-26 | concert | 1 |
| 2026-10-26 | match | 2 |
| 2026-10-26 | market | 0 |
| 2026-10-26 | festival | 0 |

### Figure 29. Calculate density: Transit stations [67]

| bin_start | bin_end | count |
| --- | --- | --- |
| 0.001178 | 0.178 | 23 |
| 0.178 | 0.3547 | 8 |
| 0.3547 | 0.5315 | 7 |
| 0.5315 | 0.7083 | 8 |
| 0.7083 | 0.8851 | 6 |
| 0.8851 | 1.062 | 4 |
| 1.062 | 1.239 | 4 |

### Figure 30. Repeat and near repeat classification: Events over time [68]

| period | series | value |
| --- | --- | --- |
| 2026-06-01 | exhibition | 0 |
| 2026-06-01 | concert | 2 |
| 2026-06-01 | match | 1 |
| 2026-06-01 | market | 0 |
| 2026-06-01 | festival | 0 |
| 2026-06-08 | exhibition | 1 |
| 2026-06-08 | concert | 0 |
| 2026-06-08 | match | 1 |
| 2026-06-08 | market | 1 |
| 2026-06-08 | festival | 0 |
| 2026-06-15 | exhibition | 0 |
| 2026-06-15 | concert | 0 |
| 2026-06-15 | match | 1 |
| 2026-06-15 | market | 1 |
| 2026-06-15 | festival | 0 |
| 2026-06-22 | exhibition | 0 |
| 2026-06-22 | concert | 1 |
| 2026-06-22 | match | 0 |
| 2026-06-22 | market | 0 |
| 2026-06-22 | festival | 0 |
| 2026-06-29 | exhibition | 3 |
| 2026-06-29 | concert | 3 |
| 2026-06-29 | match | 0 |
| 2026-06-29 | market | 0 |
| 2026-06-29 | festival | 1 |
| 2026-07-06 | exhibition | 0 |
| 2026-07-06 | concert | 1 |
| 2026-07-06 | match | 0 |
| 2026-07-06 | market | 0 |
| 2026-07-06 | festival | 0 |
| 2026-07-13 | exhibition | 1 |
| 2026-07-13 | concert | 0 |
| 2026-07-13 | match | 1 |
| 2026-07-13 | market | 0 |
| 2026-07-13 | festival | 0 |
| 2026-07-20 | exhibition | 0 |
| 2026-07-20 | concert | 0 |
| 2026-07-20 | match | 0 |
| 2026-07-20 | market | 2 |
| 2026-07-20 | festival | 0 |
| 2026-07-27 | exhibition | 1 |
| 2026-07-27 | concert | 0 |
| 2026-07-27 | match | 0 |
| 2026-07-27 | market | 0 |
| 2026-07-27 | festival | 0 |
| 2026-08-03 | exhibition | 0 |
| 2026-08-03 | concert | 0 |
| 2026-08-03 | match | 1 |
| 2026-08-03 | market | 1 |
| 2026-08-03 | festival | 0 |
| 2026-08-10 | exhibition | 1 |
| 2026-08-10 | concert | 0 |
| 2026-08-10 | match | 2 |
| 2026-08-10 | market | 0 |
| 2026-08-10 | festival | 0 |
| 2026-08-17 | exhibition | 1 |
| 2026-08-17 | concert | 1 |
| 2026-08-17 | match | 0 |
| 2026-08-17 | market | 0 |
| 2026-08-17 | festival | 0 |
| 2026-08-24 | exhibition | 0 |
| 2026-08-24 | concert | 1 |
| 2026-08-24 | match | 1 |
| 2026-08-24 | market | 0 |
| 2026-08-24 | festival | 1 |
| 2026-08-31 | exhibition | 1 |
| 2026-08-31 | concert | 1 |
| 2026-08-31 | match | 0 |
| 2026-08-31 | market | 0 |
| 2026-08-31 | festival | 0 |
| 2026-09-07 | exhibition | 1 |
| 2026-09-07 | concert | 0 |
| 2026-09-07 | match | 1 |
| 2026-09-07 | market | 1 |
| 2026-09-07 | festival | 0 |
| 2026-09-14 | exhibition | 1 |
| 2026-09-14 | concert | 0 |
| 2026-09-14 | match | 1 |
| 2026-09-14 | market | 1 |
| 2026-09-14 | festival | 3 |
| 2026-09-21 | exhibition | 0 |
| 2026-09-21 | concert | 0 |
| 2026-09-21 | match | 0 |
| 2026-09-21 | market | 0 |
| 2026-09-21 | festival | 0 |
| 2026-09-28 | exhibition | 0 |
| 2026-09-28 | concert | 0 |
| 2026-09-28 | match | 0 |
| 2026-09-28 | market | 0 |
| 2026-09-28 | festival | 0 |
| 2026-10-05 | exhibition | 0 |
| 2026-10-05 | concert | 2 |
| 2026-10-05 | match | 0 |
| 2026-10-05 | market | 1 |
| 2026-10-05 | festival | 1 |
| 2026-10-12 | exhibition | 1 |
| 2026-10-12 | concert | 0 |
| 2026-10-12 | match | 1 |
| 2026-10-12 | market | 2 |
| 2026-10-12 | festival | 0 |
| 2026-10-19 | exhibition | 2 |
| 2026-10-19 | concert | 1 |
| 2026-10-19 | match | 0 |
| 2026-10-19 | market | 0 |
| 2026-10-19 | festival | 1 |
| 2026-10-26 | exhibition | 2 |
| 2026-10-26 | concert | 1 |
| 2026-10-26 | match | 2 |
| 2026-10-26 | market | 0 |
| 2026-10-26 | festival | 0 |


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