GeoBitLarge Spatial Model

Tourism and events

Venues, footfall by hour and hotels

Barcelona, Catalonia, Spainsynthetic data, real placeMaps, charts and report
The question

When and where are crowds heaviest over time around the venues, and where do hotels and visitors concentrate by district?

A city tourism office uploads venues, footfall counters with hourly counts, visitor check-ins, hotels, districts, transit stations, event schedules, short-term rentals and an event mobility plan. It wants to know when and where crowds are heaviest and where visitors and hotels concentrate.

41.3870, 2.1700data covers about 6 by 5 km
12datasets uploaded
12output layers
12maps made
18charts made
29findings
287 sStart run time

Every map and chart this run made is here. 12 map images, 18 charts and the cited report (PDF, Word, Markdown), as exported on 2026-10-22.

Findings (first few)

  • Evidenced: 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 12345.
  • Evidenced: By dataset: Footfall by hour 5,040; Visitor check-ins 1,787; Hotel occupancy by district and month 120; Events 60 1.
  • Evidenced: The trend is +185 events per month; the second half of the months has 6,937 against 60 in the first (+11461.7%) 1.
  • Evidenced: The busiest months were September 2026 (6,112), August 2026 (756), June 2026 (21) 1.
  • Evidenced: The quietest months were October 2025 (10), November 2025 (10), December 2025 (10) 1.

What Start ran

Describe mode, the local model only (no paid model); run status succeeded, 8 analyst agents.

gp_calculate_densitygp_hot_spotsgp_near_repeatgp_pipelinegp_space_time_hot_spots

Maps

Every map image this run exported, with legend, north arrow and scale bar. Select one to view it full size.

Events per 10,000 people by feature
Events per 10,000 people by featureFig. 2
Hotels per 10,000 people by feature
Hotels per 10,000 people by featureFig. 3
Hot spot analysis: Events
Hot spot analysis: EventsFig. 4
Hot spot analysis: Hotels
Hot spot analysis: HotelsFig. 5
Emerging hot spots: Events
Emerging hot spots: EventsFig. 6
Emerging hot spots: Visitor check-ins
Emerging hot spots: Visitor check-insFig. 7
Calculate density: Events
Calculate density: EventsFig. 8
Calculate density: Hotels
Calculate density: HotelsFig. 9
Transit stations per 10,000 people by feature
Transit stations per 10,000 people by featureFig. 10
Hot spot analysis: Short-term rentals
Hot spot analysis: Short-term rentalsFig. 11
Repeat and near repeat classification: Events
Repeat and near repeat classification: EventsFig. 12
Calculate density: Transit stations
Calculate density: Transit stationsFig. 13

Charts

Every chart image this run exported.

Footfall counters over time (installed_year)
Footfall counters over time (installed_year)Fig. 14
Footfall by hour over time (timestamp)
Footfall by hour over time (timestamp)Fig. 15
Events over time (date)
Events over time (date)Fig. 16
Hotel occupancy by district and month over time (month)
Hotel occupancy by district and month over time (month)Fig. 17
Events per 10,000 people by feature
Events per 10,000 people by featureFig. 18
Hotels per 10,000 people by feature
Hotels per 10,000 people by featureFig. 19
Hot spot analysis: Events
Hot spot analysis: EventsFig. 20
Hot spot analysis: Hotels
Hot spot analysis: HotelsFig. 21
Emerging hot spots: Events
Emerging hot spots: EventsFig. 22
Emerging hot spots: Visitor check-ins
Emerging hot spots: Visitor check-insFig. 23
Calculate density: Events
Calculate density: EventsFig. 24
Calculate density: Hotels
Calculate density: HotelsFig. 25
Transit stations per 10,000 people by feature
Transit stations per 10,000 people by featureFig. 26
Hot spot analysis: Short-term rentals
Hot spot analysis: Short-term rentalsFig. 27
Repeat and near repeat classification: Events
Repeat and near repeat classification: EventsFig. 28
Calculate density: Transit stations
Calculate density: Transit stationsFig. 29
Repeat and near repeat classification: Events over time
Repeat and near repeat classification: Events over timeFig. 30
Timeline: events per month by dataset
Timeline: events per month by datasetFig. 1

The cited report

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

DataKindRowsTime spanSummarySource
Venuesvector, Point4040 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 countersvector, Point3030 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 hourtable5,0402026-09-14 to 2026-09-20Table 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-insvector, Point1,7872026-08-01 to 2026-09-301,787 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
Hotelsvector, Point120120 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
Districtsvector, Polygon1010 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 stationsvector, Point2525 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
Eventsvector, Point602026-06-02 to 2026-10-3160 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 rentalsvector, Point359359 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 monthtable1202025-10-01 to 2026-09-01Table 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 routesvector, LineString77 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 plandocument1-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, Barcelonarecord77
Events per 10,000 people by featurelayer, Polygon1078
Hotels per 10,000 people by featurelayer, Polygon1079
Hot spot analysis: Eventslayer, Polygon2180
Hot spot analysis: Hotelslayer, Polygon3581
Emerging hot spots: Eventslayer, Polygon1282
Emerging hot spots: Visitor check-inslayer, Polygon24783
Calculate density: Eventslayer, Polygon10584
Calculate density: Hotelslayer, Polygon16085
Transit stations per 10,000 people by featurelayer, Polygon1086
Hot spot analysis: Short-term rentalslayer, Polygon12087
Repeat and near repeat classification: Eventslayer, Point6088
Calculate density: Transit stationslayer, Polygon6089

Timeline

Timeline: events per month by dataset
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 12345. 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 8910. 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.

ResultValue
result, count10
result, elapsed ms0
billing, cost0.0215
billing, list price0.0215
billing, upstream cost0
list price0.0215

Findings

  • Evidenced: Pipeline on Events, Districts: Pipeline of 2 steps (summarize_within > calculate_field): 1 layer saved 8910.
  • 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 101112. 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.

ResultValue
result, count10
result, elapsed ms0
billing, cost0.0227
billing, list price0.0227
billing, upstream cost0
list price0.0227

Findings

  • Evidenced: Pipeline on Hotels, Districts: Pipeline of 2 steps (summarize_within > calculate_field): 1 layer saved 101112.
  • 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

ResultValue
result, bins21
result, count21
result, elapsed ms2
result, cell size m1,000
result, incidents used60
result, distance band m1,000
billing, cost0.0512
billing, list price0.0512
billing, upstream cost0

Findings

  • Evidenced: Hot spots on Events: Hot spot analysis: 1 layer saved: Hot spot analysis: Events (21 features) 915.
  • 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) 1217. 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.

ResultValue
result, bins35
result, count35
result, elapsed ms3
result, cell size m1,000
result, incidents used120
result, distance band m1,000
billing, cost0.0524
billing, list price0.0524
billing, upstream cost0

Findings

  • Evidenced: Hot spots on Hotels: Hot spot analysis: 1 layer saved: Hot spot analysis: Hotels (35 features) 1217.
  • 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.

ResultValue
result, count12
result, elapsed ms2
result, resolution7
result, features used60
result, time neighbors0
billing, cost0.103
billing, list price0.103
billing, upstream cost0

Findings

  • Evidenced: Space time hot spots on Events: Emerging hot spots: 1 layer saved: Emerging hot spots: Events (12 features) 920.
  • 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.

ResultValue
result, count247
result, elapsed ms25
result, resolution9
result, features used1,787
result, time neighbors0
billing, cost0.1893
billing, list price0.1893
billing, upstream cost0

Findings

  • Evidenced: Space time hot spots on Visitor check-ins: Emerging hot spots: 1 layer saved: Emerging hot spots: Visitor check-ins (247 features) 2223.
  • 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) 926. 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.

ResultValue
result, count105
result, elapsed ms3
result, bandwidth m1,000
result, cell size m500
result, excluded null0
result, max density per km213.52
billing, cost0.0512
billing, list price0.0512
billing, upstream cost0

Findings

  • Evidenced: Calculate density on Events: Calculate density: 1 layer saved: Calculate density: Events (105 features) 926.
  • 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) 1228. 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.

ResultValue
result, count160
result, elapsed ms5
result, bandwidth m1,000
result, cell size m500
result, excluded null0
result, max density per km219.04
billing, cost0.0524
billing, list price0.0524
billing, upstream cost0

Findings

  • Evidenced: Calculate density on Hotels: Calculate density: 1 layer saved: Calculate density: Hotels (160 features) 1228.
  • 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 103031. 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.

ResultValue
result, count10
result, elapsed ms0
billing, cost0.0208
billing, list price0.0208
billing, upstream cost0
list price0.0208

Findings

  • Evidenced: Pipeline on Transit stations, Districts: Pipeline of 2 steps (summarize_within > calculate_field): 1 layer saved 103031.
  • 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) 3334. 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.

ResultValue
result, bins120
result, count120
result, elapsed ms6
result, cell size m500
result, incidents used359
result, distance band m500
billing, cost0.05718
billing, list price0.05718
billing, upstream cost0

Findings

  • Evidenced: Hot spots on Short-term rentals: Hot spot analysis: 1 layer saved: Hot spot analysis: Short-term rentals (120 features) 3334.
  • 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) 937. It reported result, days 15; result, count 60; result, chains 12; result, distance m 500; result, elapsed ms 4; result, pairs linked 27 37.

ResultValue
result, days15
result, count60
result, chains12
result, distance m500
result, elapsed ms4
result, pairs linked27
billing, cost0.0518
billing, list price0.0518
billing, upstream cost0

Findings

  • Evidenced: Near repeat on Events: Repeat and near repeat classification: 1 layer saved: Repeat and near repeat classification: Events (60 features) 937.
  • 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.

ResultValue
result, count60
result, elapsed ms2
result, bandwidth m2,000
result, cell size m1,000
result, excluded null0
result, max density per km21.239
billing, cost0.0505
billing, list price0.0505
billing, upstream cost0

Findings

  • Evidenced: Calculate density on Transit stations: Calculate density: 1 layer saved: Calculate density: Transit stations (60 features) 3139.
  • 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) 404142434445464748495051 and 17 chart(s) 5253545556575859606162636465666768.

Events per 10,000 people by feature
Figure 2. Events per 10,000 people by feature 40
Hotels per 10,000 people by feature
Figure 3. Hotels per 10,000 people by feature 41
Hot spot analysis: Events
Figure 4. Hot spot analysis: Events 42
Hot spot analysis: Hotels
Figure 5. Hot spot analysis: Hotels 43
Emerging hot spots: Events
Figure 6. Emerging hot spots: Events 44
Emerging hot spots: Visitor check-ins
Figure 7. Emerging hot spots: Visitor check-ins 45
Calculate density: Events
Figure 8. Calculate density: Events 46
Calculate density: Hotels
Figure 9. Calculate density: Hotels 47
Transit stations per 10,000 people by feature
Figure 10. Transit stations per 10,000 people by feature 48
Hot spot analysis: Short-term rentals
Figure 11. Hot spot analysis: Short-term rentals 49
Repeat and near repeat classification: Events
Figure 12. Repeat and near repeat classification: Events 50
Calculate density: Transit stations
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)
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)
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)
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)
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
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
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
Figure 20. Hot spot analysis: Events 58. Source: Hot spot analysis: Events (21 features). The data is in the appendix.
Hot spot analysis: Hotels
Figure 21. Hot spot analysis: Hotels 59. Source: Hot spot analysis: Hotels (35 features). The data is in the appendix.
Emerging hot spots: Events
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
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
Figure 24. Calculate density: Events 62. Source: Calculate density: Events (105 features). The data is in the appendix.
Calculate density: Hotels
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
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
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
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
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
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. 1Rendering: Chart: Timeline: events per month by dataset. 52 rows id c28dd5e4-8e5d-4f58-bf01-ecf5befa059e
  2. 2Dataset: Footfall by hour. table, 5040 rows id 14c3f63e-f77c-42c5-915f-d9c05adec323
  3. 3Dataset: Visitor check-ins. vector, 1787 rows id c5304978-960d-40ec-bdc4-acd2ea178039
  4. 4Dataset: Events. vector, 60 rows id 19eebb1c-a097-45a3-b65f-454a721735ff
  5. 5Dataset: Hotel occupancy by district and month. table, 120 rows id ddefeb0f-7855-4d3a-bdbc-830daaef4145
  6. 6Catalog: 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. 7Plan: 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. 8Tool result: gp_pipeline, step 0. Pipeline of 2 steps (summarize_within > calculate_field): 1 layer saved. id 6f508d4a-8ac6-43c5-a648-3a317b80b725:0
  9. 9Layer: Events. 60 features id 48b188aa-e96c-47fa-8a28-11e7d0d1265b
  10. 10Layer: Districts. 10 features id 2a6df822-dc6d-48ab-bb94-b4a34b363160
  11. 11Tool result: gp_pipeline, step 1. Pipeline of 2 steps (summarize_within > calculate_field): 1 layer saved. id 6f508d4a-8ac6-43c5-a648-3a317b80b725:1
  12. 12Layer: Hotels. 120 features id 3e8f4b82-1559-4b63-821d-9911e2fb57de
  13. 13Catalog: 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. 14Plan: 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. 15Tool 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. 16Plan: 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. 17Tool 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. 18Catalog: 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. 19Plan: 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. 20Tool 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. 21Plan: 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. 22Tool 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. 23Layer: Visitor check-ins. 1,787 features id 517cd234-4309-4e40-a7fe-bfcb513649a0
  24. 24Catalog: 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. 25Plan: 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. 26Tool result: gp_calculate_density, step 0. Calculate density: 1 layer saved: Calculate density: Events (105 features). id ffcce236-4ccf-4454-afd4-62fde6826f22:0
  27. 27Plan: 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. 28Tool result: gp_calculate_density, step 1. Calculate density: 1 layer saved: Calculate density: Hotels (160 features). id ffcce236-4ccf-4454-afd4-62fde6826f22:1
  29. 29Plan: 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. 30Tool result: gp_pipeline, step 0. Pipeline of 2 steps (summarize_within > calculate_field): 1 layer saved. id dd7063d1-b363-4d94-9eb7-b0637b02bd44:0
  31. 31Layer: Transit stations. 25 features id 05c69c10-6b81-4264-aa80-dccc82f1a9c9
  32. 32Plan: 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. 33Tool 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. 34Layer: Short-term rentals. 359 features id cfe7bd58-22ce-4b7e-9b85-2abc3992354a
  35. 35Catalog: 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. 36Plan: 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. 37Tool 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. 38Plan: 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. 39Tool 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. 40Rendering: Map: Events per 10,000 people by feature id eec57722-0ad2-4980-8930-376540984ea4
  41. 41Rendering: Map: Hotels per 10,000 people by feature id 63d6732e-910d-4395-a217-5bcea906df8f
  42. 42Rendering: Map: Hot spot analysis: Events id 54872c4e-d24c-440f-8d17-ea1b17fedf7a
  43. 43Rendering: Map: Hot spot analysis: Hotels id 0332ec77-f165-48dd-8f8b-f78eca70bd2e
  44. 44Rendering: Map: Emerging hot spots: Events id a797e3a3-c203-46a0-adee-6ae0c49df6f7
  45. 45Rendering: Map: Emerging hot spots: Visitor check-ins id c742998e-376d-4428-9cd8-ee43aff334d8
  46. 46Rendering: Map: Calculate density: Events id c4858508-5b9c-4244-9da4-22c00b167e7d
  47. 47Rendering: Map: Calculate density: Hotels id f95cc319-e8fc-4b4d-b94c-0f32f5d881ba
  48. 48Rendering: Map: Transit stations per 10,000 people by feature id 490f3ebb-c4cf-4df7-a6b0-8a2608c0ce37
  49. 49Rendering: Map: Hot spot analysis: Short-term rentals id 5f63f0eb-e874-46e7-9c16-5195d066b68e
  50. 50Rendering: Map: Repeat and near repeat classification: Events id 6ead692c-c1bf-41c9-b506-1b85a1e7c4e7
  51. 51Rendering: Map: Calculate density: Transit stations id 9c9b72cd-660b-4431-a878-d8ce46aa23b5
  52. 52Rendering: Chart: Footfall counters over time (installed_year). 1 rows id c57481bf-4a67-476a-8f46-50e95ee01d34
  53. 53Rendering: Chart: Footfall by hour over time (timestamp). 7 rows id 68fde6c5-f7cb-4519-beb5-fda2f50b9446
  54. 54Rendering: Chart: Events over time (date). 110 rows id 7d7c5e9a-3392-4fe8-abd9-3e23dce93372
  55. 55Rendering: Chart: Hotel occupancy by district and month over time (month). 294 rows id 16057d40-3ace-4329-a918-0640eb5a26c4
  56. 56Rendering: Chart: Events per 10,000 people by feature. 5 rows id 4b2b3fe6-a4cc-4a5a-8f99-df4cfc48d3db
  57. 57Rendering: Chart: Hotels per 10,000 people by feature. 5 rows id a4fb08df-9753-4dfe-ae78-a53901591524
  58. 58Rendering: Chart: Hot spot analysis: Events. 5 rows id df76ff7e-00ff-4fc6-b41d-5534968aa408
  59. 59Rendering: Chart: Hot spot analysis: Hotels. 6 rows id 49c30dd2-4afd-47ce-9cab-fd5b09afd3e2
  60. 60Rendering: Chart: Emerging hot spots: Events. 5 rows id b15107ab-80aa-4641-95b1-be5375aa81e8
  61. 61Rendering: Chart: Emerging hot spots: Visitor check-ins. 9 rows id c4087574-f496-4825-92c9-f13d8ca99c72
  62. 62Rendering: Chart: Calculate density: Events. 8 rows id cd478e87-79f0-4685-a84f-ef9250389ac5
  63. 63Rendering: Chart: Calculate density: Hotels. 9 rows id 01e6882d-fc1c-41fe-b9f0-d4c083f8ce50
  64. 64Rendering: Chart: Transit stations per 10,000 people by feature. 5 rows id 4bfbc9fc-df18-4c54-a1e1-dc5127040af0
  65. 65Rendering: Chart: Hot spot analysis: Short-term rentals. 10 rows id c2f2f8d1-b71f-40d0-96d6-7cd46f9331df
  66. 66Rendering: Chart: Repeat and near repeat classification: Events. 110 rows id ae906476-4bb5-48f7-b8ac-00ca31455f0b
  67. 67Rendering: Chart: Calculate density: Transit stations. 7 rows id d67b5707-4e19-4aa1-9943-011622237776
  68. 68Rendering: Chart: Repeat and near repeat classification: Events over time. 110 rows id 2885d2a0-a6c2-4e19-8f57-418823738ddf
  69. 69Dataset: Venues. vector, 40 rows id fc2cd39e-1054-4c59-b6b6-a2f324d6bb3e
  70. 70Dataset: Footfall counters. vector, 30 rows id e0df87f0-4ea0-4648-bcf4-e339d9dd7a09
  71. 71Dataset: Hotels. vector, 120 rows id 9b34f752-20f9-4be7-a9c5-ca3e89bf6438
  72. 72Dataset: Districts. vector, 10 rows id 3113aeda-04c2-4be2-9eb4-1141e74fcf1d
  73. 73Dataset: Transit stations. vector, 25 rows id a1a99e8d-74be-4d47-b8fc-3d2bc032a85e
  74. 74Dataset: Short-term rentals. vector, 359 rows id bc26f941-1554-4b84-858e-db36a81426a5
  75. 75Dataset: Walking routes. vector, 7 rows id 9c7a2aa2-432b-4ee7-8b1e-0b0bddcece5a
  76. 76Dataset: Event mobility plan. document, ? rows id 552eef78-c8b9-412e-9863-a10cdaee4c8c
  77. 77Record: Sample: Tourism and events, Barcelona id 7e086c07-eb20-44ad-b602-c5958e8ee13e
  78. 78Layer: Events per 10,000 people by feature. 10 features id 405dd996-09a6-48ed-ba23-86c7253d6c5c
  79. 79Layer: Hotels per 10,000 people by feature. 10 features id dafbe175-7ed5-414a-a851-63ce5848851d
  80. 80Layer: Hot spot analysis: Events. 21 features id a5378e74-77ee-4f8e-af6b-f25d1bd7f814
  81. 81Layer: Hot spot analysis: Hotels. 35 features id 5d3f8200-70e1-4127-b473-2986b030c3dc
  82. 82Layer: Emerging hot spots: Events. 12 features id 4ef1cdee-955e-4bd5-8945-dcb53b0bb3b3
  83. 83Layer: Emerging hot spots: Visitor check-ins. 247 features id 0550d911-8aec-447f-ad8b-d74332094159
  84. 84Layer: Calculate density: Events. 105 features id db4f4d27-3ab9-4f41-8da9-01e00e1e05fd
  85. 85Layer: Calculate density: Hotels. 160 features id 6a74aae2-e4d7-44bb-bae5-af7907cd724b
  86. 86Layer: Transit stations per 10,000 people by feature. 10 features id dc9ada02-031c-44ad-aa58-f2cbb1ad1c06
  87. 87Layer: Hot spot analysis: Short-term rentals. 120 features id 4e76223c-ab8d-4217-a68c-42960cd0306d
  88. 88Layer: Repeat and near repeat classification: Events. 60 features id 095f2dfc-b0b5-4811-be3f-338320ff4791
  89. 89Layer: Calculate density: Transit stations. 60 features id 68427dd3-936e-41d3-93aa-54b77e01fe32
Appendix: the data behind the chartsShow

Figure 1. Timeline: events per month by dataset 1

periodseriesvalue
2025-10-01Footfall by hour0
2025-10-01Visitor check-ins0
2025-10-01Hotel occupancy by district and month10
2025-10-01Events0
2025-11-01Footfall by hour0
2025-11-01Visitor check-ins0
2025-11-01Hotel occupancy by district and month10
2025-11-01Events0
2025-12-01Footfall by hour0
2025-12-01Visitor check-ins0
2025-12-01Hotel occupancy by district and month10
2025-12-01Events0
2026-01-01Footfall by hour0
2026-01-01Visitor check-ins0
2026-01-01Hotel occupancy by district and month10
2026-01-01Events0
2026-02-01Footfall by hour0
2026-02-01Visitor check-ins0
2026-02-01Hotel occupancy by district and month10
2026-02-01Events0
2026-03-01Footfall by hour0
2026-03-01Visitor check-ins0
2026-03-01Hotel occupancy by district and month10
2026-03-01Events0
2026-04-01Footfall by hour0
2026-04-01Visitor check-ins0
2026-04-01Hotel occupancy by district and month10
2026-04-01Events0
2026-05-01Footfall by hour0
2026-05-01Visitor check-ins0
2026-05-01Hotel occupancy by district and month10
2026-05-01Events0
2026-06-01Footfall by hour0
2026-06-01Visitor check-ins0
2026-06-01Hotel occupancy by district and month10
2026-06-01Events11
2026-07-01Footfall by hour0
2026-07-01Visitor check-ins0
2026-07-01Hotel occupancy by district and month10
2026-07-01Events11
2026-08-01Footfall by hour0
2026-08-01Visitor check-ins736
2026-08-01Hotel occupancy by district and month10
2026-08-01Events10
2026-09-01Footfall by hour5,040
2026-09-01Visitor check-ins1,051
2026-09-01Hotel occupancy by district and month10
2026-09-01Events11
2026-10-01Footfall by hour0
2026-10-01Visitor check-ins0
2026-10-01Hotel occupancy by district and month0
2026-10-01Events17

Figure 14. Footfall counters over time (installed_year) 52

bin_startbin_endcount
2,0252,02530

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

periodvalue
2026-09-14720
2026-09-15720
2026-09-16720
2026-09-17720
2026-09-18720
2026-09-19720
2026-09-20720

Figure 16. Events over time (date) 54

periodseriesvalue
2026-06-01exhibition0
2026-06-01concert2
2026-06-01match1
2026-06-01market0
2026-06-01festival0
2026-06-08exhibition1
2026-06-08concert0
2026-06-08match1
2026-06-08market1
2026-06-08festival0
2026-06-15exhibition0
2026-06-15concert0
2026-06-15match1
2026-06-15market1
2026-06-15festival0
2026-06-22exhibition0
2026-06-22concert1
2026-06-22match0
2026-06-22market0
2026-06-22festival0
2026-06-29exhibition3
2026-06-29concert3
2026-06-29match0
2026-06-29market0
2026-06-29festival1
2026-07-06exhibition0
2026-07-06concert1
2026-07-06match0
2026-07-06market0
2026-07-06festival0
2026-07-13exhibition1
2026-07-13concert0
2026-07-13match1
2026-07-13market0
2026-07-13festival0
2026-07-20exhibition0
2026-07-20concert0
2026-07-20match0
2026-07-20market2
2026-07-20festival0
2026-07-27exhibition1
2026-07-27concert0
2026-07-27match0
2026-07-27market0
2026-07-27festival0
2026-08-03exhibition0
2026-08-03concert0
2026-08-03match1
2026-08-03market1
2026-08-03festival0
2026-08-10exhibition1
2026-08-10concert0
2026-08-10match2
2026-08-10market0
2026-08-10festival0
2026-08-17exhibition1
2026-08-17concert1
2026-08-17match0
2026-08-17market0
2026-08-17festival0
2026-08-24exhibition0
2026-08-24concert1
2026-08-24match1
2026-08-24market0
2026-08-24festival1
2026-08-31exhibition1
2026-08-31concert1
2026-08-31match0
2026-08-31market0
2026-08-31festival0
2026-09-07exhibition1
2026-09-07concert0
2026-09-07match1
2026-09-07market1
2026-09-07festival0
2026-09-14exhibition1
2026-09-14concert0
2026-09-14match1
2026-09-14market1
2026-09-14festival3
2026-09-21exhibition0
2026-09-21concert0
2026-09-21match0
2026-09-21market0
2026-09-21festival0
2026-09-28exhibition0
2026-09-28concert0
2026-09-28match0
2026-09-28market0
2026-09-28festival0
2026-10-05exhibition0
2026-10-05concert2
2026-10-05match0
2026-10-05market1
2026-10-05festival1
2026-10-12exhibition1
2026-10-12concert0
2026-10-12match1
2026-10-12market2
2026-10-12festival0
2026-10-19exhibition2
2026-10-19concert1
2026-10-19match0
2026-10-19market0
2026-10-19festival1
2026-10-26exhibition2
2026-10-26concert1
2026-10-26match2
2026-10-26market0
2026-10-26festival0

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

periodseriesvalue
2025-09-29DT062,138
2025-09-29DT041,034
2025-09-29DT052,817
2025-09-29DT101,781
2025-09-29DT021,728
2025-09-29Other1.651e+04
2025-10-06DT060
2025-10-06DT040
2025-10-06DT050
2025-10-06DT100
2025-10-06DT020
2025-10-06Other0
2025-10-13DT060
2025-10-13DT040
2025-10-13DT050
2025-10-13DT100
2025-10-13DT020
2025-10-13Other0
2025-10-20DT060
2025-10-20DT040
2025-10-20DT050
2025-10-20DT100
2025-10-20DT020
2025-10-20Other0
2025-10-27DT067,226
2025-10-27DT048,472
2025-10-27DT054,464
2025-10-27DT106,345
2025-10-27DT022,589
2025-10-27Other1.248e+04
2025-11-03DT060
2025-11-03DT040
2025-11-03DT050
2025-11-03DT100
2025-11-03DT020
2025-11-03Other0
2025-11-10DT060
2025-11-10DT040
2025-11-10DT050
2025-11-10DT100
2025-11-10DT020
2025-11-10Other0
2025-11-17DT060
2025-11-17DT040
2025-11-17DT050
2025-11-17DT100
2025-11-17DT020
2025-11-17Other0
2025-11-24DT060
2025-11-24DT040
2025-11-24DT050
2025-11-24DT100
2025-11-24DT020
2025-11-24Other0
2025-12-01DT068,013
2025-12-01DT043,856
2025-12-01DT055,653
2025-12-01DT108,983
2025-12-01DT028,363
2025-12-01Other2.188e+04
2025-12-08DT060
2025-12-08DT040
2025-12-08DT050
2025-12-08DT100
2025-12-08DT020
2025-12-08Other0
2025-12-15DT060
2025-12-15DT040
2025-12-15DT050
2025-12-15DT100
2025-12-15DT020
2025-12-15Other0
2025-12-22DT060
2025-12-22DT040
2025-12-22DT050
2025-12-22DT100
2025-12-22DT020
2025-12-22Other0
2025-12-29DT068,732
2025-12-29DT043,728
2025-12-29DT052,054
2025-12-29DT106,927
2025-12-29DT027,321
2025-12-29Other2.694e+04
2026-01-05DT060
2026-01-05DT040
2026-01-05DT050
2026-01-05DT100
2026-01-05DT020
2026-01-05Other0
2026-01-12DT060
2026-01-12DT040
2026-01-12DT050
2026-01-12DT100
2026-01-12DT020
2026-01-12Other0
2026-01-19DT060
2026-01-19DT040
2026-01-19DT050
2026-01-19DT100
2026-01-19DT020
2026-01-19Other0
2026-01-26DT062,115
2026-01-26DT045,118
2026-01-26DT053,277
2026-01-26DT105,816
2026-01-26DT027,018
2026-01-26Other2.137e+04
2026-02-02DT060
2026-02-02DT040
2026-02-02DT050
2026-02-02DT100
2026-02-02DT020
2026-02-02Other0
2026-02-09DT060
2026-02-09DT040
2026-02-09DT050
2026-02-09DT100
2026-02-09DT020
2026-02-09Other0
2026-02-16DT060
2026-02-16DT040
2026-02-16DT050
2026-02-16DT100
2026-02-16DT020
2026-02-16Other0
2026-02-23DT063,579
2026-02-23DT042,962
2026-02-23DT057,288
2026-02-23DT103,713
2026-02-23DT022,970
2026-02-23Other3.058e+04
2026-03-02DT060
2026-03-02DT040
2026-03-02DT050
2026-03-02DT100
2026-03-02DT020
2026-03-02Other0
2026-03-09DT060
2026-03-09DT040
2026-03-09DT050
2026-03-09DT100
2026-03-09DT020
2026-03-09Other0
2026-03-16DT060
2026-03-16DT040
2026-03-16DT050
2026-03-16DT100
2026-03-16DT020
2026-03-16Other0
2026-03-23DT060
2026-03-23DT040
2026-03-23DT050
2026-03-23DT100
2026-03-23DT020
2026-03-23Other0
2026-03-30DT065,650
2026-03-30DT046,950
2026-03-30DT056,796
2026-03-30DT104,404
2026-03-30DT023,479
2026-03-30Other1.511e+04
2026-04-06DT060
2026-04-06DT040
2026-04-06DT050
2026-04-06DT100
2026-04-06DT020
2026-04-06Other0
2026-04-13DT060
2026-04-13DT040
2026-04-13DT050
2026-04-13DT100
2026-04-13DT020
2026-04-13Other0
2026-04-20DT060
2026-04-20DT040
2026-04-20DT050
2026-04-20DT100
2026-04-20DT020
2026-04-20Other0
2026-04-27DT067,955
2026-04-27DT042,998
2026-04-27DT052,479
2026-04-27DT103,806
2026-04-27DT024,230
2026-04-27Other2.721e+04
2026-05-04DT060
2026-05-04DT040
2026-05-04DT050
2026-05-04DT100
2026-05-04DT020
2026-05-04Other0
2026-05-11DT060
2026-05-11DT040
2026-05-11DT050
2026-05-11DT100
2026-05-11DT020
2026-05-11Other0
2026-05-18DT060
2026-05-18DT040
2026-05-18DT050
2026-05-18DT100
2026-05-18DT020
2026-05-18Other0
2026-05-25DT060
2026-05-25DT040
2026-05-25DT050
2026-05-25DT100
2026-05-25DT020
2026-05-25Other0
2026-06-01DT065,440
2026-06-01DT046,136
2026-06-01DT057,347
2026-06-01DT103,873
2026-06-01DT024,157
2026-06-01Other2.078e+04
2026-06-08DT060
2026-06-08DT040
2026-06-08DT050
2026-06-08DT100
2026-06-08DT020
2026-06-08Other0
2026-06-15DT060
2026-06-15DT040
2026-06-15DT050
2026-06-15DT100
2026-06-15DT020
2026-06-15Other0
2026-06-22DT060
2026-06-22DT040
2026-06-22DT050
2026-06-22DT100
2026-06-22DT020
2026-06-22Other0
2026-06-29DT067,881
2026-06-29DT046,851
2026-06-29DT051,454
2026-06-29DT101,762
2026-06-29DT021,279
2026-06-29Other1.875e+04
2026-07-06DT060
2026-07-06DT040
2026-07-06DT050
2026-07-06DT100
2026-07-06DT020
2026-07-06Other0
2026-07-13DT060
2026-07-13DT040
2026-07-13DT050
2026-07-13DT100
2026-07-13DT020
2026-07-13Other0
2026-07-20DT060
2026-07-20DT040
2026-07-20DT050
2026-07-20DT100
2026-07-20DT020
2026-07-20Other0
2026-07-27DT062,819
2026-07-27DT047,853
2026-07-27DT055,302
2026-07-27DT101,915
2026-07-27DT025,724
2026-07-27Other1.762e+04
2026-08-03DT060
2026-08-03DT040
2026-08-03DT050
2026-08-03DT100
2026-08-03DT020
2026-08-03Other0
2026-08-10DT060
2026-08-10DT040
2026-08-10DT050
2026-08-10DT100
2026-08-10DT020
2026-08-10Other0
2026-08-17DT060
2026-08-17DT040
2026-08-17DT050
2026-08-17DT100
2026-08-17DT020
2026-08-17Other0
2026-08-24DT060
2026-08-24DT040
2026-08-24DT050
2026-08-24DT100
2026-08-24DT020
2026-08-24Other0
2026-08-31DT064,125
2026-08-31DT048,231
2026-08-31DT058,813
2026-08-31DT107,608
2026-08-31DT027,592
2026-08-31Other2.857e+04

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

bin_startbin_endcount
089
8160
16240
24320
32401

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

bin_startbin_endcount
111.27
11.221.41
21.431.61
31.641.80
41.8521

Figure 20. Hot spot analysis: Events 58

bin_startbin_endbandband_labelcount
-1-0.5not_significantNot significant7
-0.50not_significantNot significant7
00.5not_significantNot significant5
2.53hot_95Hot spot, 95% confidence1
33.5hot_99Hot spot, 99% confidence1

Figure 21. Hot spot analysis: Hotels 59

bin_startbin_endbandband_labelcount
-1-0.5not_significantNot significant14
-0.50not_significantNot significant8
00.5not_significantNot significant6
0.51not_significantNot significant4
11.5not_significantNot significant1
33.5hot_99Hot spot, 99% confidence2

Figure 22. Emerging hot spots: Events 60

bin_startbin_endcount
0.0380.8714
0.8711.7041
1.7042.5371
2.5373.373
3.374.2033

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

bin_startbin_endcount
-1.056-0.5576150
-0.5576-0.0591159
-0.059110.439315
0.43930.937810
0.93781.4363
1.4361.9353
1.9352.4333
2.4332.9322
2.9323.432

Figure 24. Calculate density: Events 62

bin_startbin_endcount
01.511
1.538
34.517
4.5610
67.517
7.598
910.520
10.51214

Figure 25. Calculate density: Hotels 63

bin_startbin_endcount
01.55616
1.5563.11126
3.1114.66713
4.6676.22223
6.2227.77812
7.7789.33322
9.33310.8911
10.8912.4423
12.441414

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

bin_startbin_endcount
011
123
232
342
452

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

bin_startbin_endbandband_labelcount
-1-0.5not_significantNot significant50
-0.50not_significantNot significant22
00.5not_significantNot significant24
0.51not_significantNot significant10
11.5not_significantNot significant1
1.52hot_90Hot spot, 90% confidence4
1.52hot_95Hot spot, 95% confidence3
2.53hot_95Hot spot, 95% confidence2
33.5hot_99Hot spot, 99% confidence3
3.54hot_99Hot spot, 99% confidence1

Figure 28. Repeat and near repeat classification: Events 66

periodseriesvalue
2026-06-01exhibition0
2026-06-01concert2
2026-06-01match1
2026-06-01market0
2026-06-01festival0
2026-06-08exhibition1
2026-06-08concert0
2026-06-08match1
2026-06-08market1
2026-06-08festival0
2026-06-15exhibition0
2026-06-15concert0
2026-06-15match1
2026-06-15market1
2026-06-15festival0
2026-06-22exhibition0
2026-06-22concert1
2026-06-22match0
2026-06-22market0
2026-06-22festival0
2026-06-29exhibition3
2026-06-29concert3
2026-06-29match0
2026-06-29market0
2026-06-29festival1
2026-07-06exhibition0
2026-07-06concert1
2026-07-06match0
2026-07-06market0
2026-07-06festival0
2026-07-13exhibition1
2026-07-13concert0
2026-07-13match1
2026-07-13market0
2026-07-13festival0
2026-07-20exhibition0
2026-07-20concert0
2026-07-20match0
2026-07-20market2
2026-07-20festival0
2026-07-27exhibition1
2026-07-27concert0
2026-07-27match0
2026-07-27market0
2026-07-27festival0
2026-08-03exhibition0
2026-08-03concert0
2026-08-03match1
2026-08-03market1
2026-08-03festival0
2026-08-10exhibition1
2026-08-10concert0
2026-08-10match2
2026-08-10market0
2026-08-10festival0
2026-08-17exhibition1
2026-08-17concert1
2026-08-17match0
2026-08-17market0
2026-08-17festival0
2026-08-24exhibition0
2026-08-24concert1
2026-08-24match1
2026-08-24market0
2026-08-24festival1
2026-08-31exhibition1
2026-08-31concert1
2026-08-31match0
2026-08-31market0
2026-08-31festival0
2026-09-07exhibition1
2026-09-07concert0
2026-09-07match1
2026-09-07market1
2026-09-07festival0
2026-09-14exhibition1
2026-09-14concert0
2026-09-14match1
2026-09-14market1
2026-09-14festival3
2026-09-21exhibition0
2026-09-21concert0
2026-09-21match0
2026-09-21market0
2026-09-21festival0
2026-09-28exhibition0
2026-09-28concert0
2026-09-28match0
2026-09-28market0
2026-09-28festival0
2026-10-05exhibition0
2026-10-05concert2
2026-10-05match0
2026-10-05market1
2026-10-05festival1
2026-10-12exhibition1
2026-10-12concert0
2026-10-12match1
2026-10-12market2
2026-10-12festival0
2026-10-19exhibition2
2026-10-19concert1
2026-10-19match0
2026-10-19market0
2026-10-19festival1
2026-10-26exhibition2
2026-10-26concert1
2026-10-26match2
2026-10-26market0
2026-10-26festival0

Figure 29. Calculate density: Transit stations 67

bin_startbin_endcount
0.0011780.17823
0.1780.35478
0.35470.53157
0.53150.70838
0.70830.88516
0.88511.0624
1.0621.2394

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

periodseriesvalue
2026-06-01exhibition0
2026-06-01concert2
2026-06-01match1
2026-06-01market0
2026-06-01festival0
2026-06-08exhibition1
2026-06-08concert0
2026-06-08match1
2026-06-08market1
2026-06-08festival0
2026-06-15exhibition0
2026-06-15concert0
2026-06-15match1
2026-06-15market1
2026-06-15festival0
2026-06-22exhibition0
2026-06-22concert1
2026-06-22match0
2026-06-22market0
2026-06-22festival0
2026-06-29exhibition3
2026-06-29concert3
2026-06-29match0
2026-06-29market0
2026-06-29festival1
2026-07-06exhibition0
2026-07-06concert1
2026-07-06match0
2026-07-06market0
2026-07-06festival0
2026-07-13exhibition1
2026-07-13concert0
2026-07-13match1
2026-07-13market0
2026-07-13festival0
2026-07-20exhibition0
2026-07-20concert0
2026-07-20match0
2026-07-20market2
2026-07-20festival0
2026-07-27exhibition1
2026-07-27concert0
2026-07-27match0
2026-07-27market0
2026-07-27festival0
2026-08-03exhibition0
2026-08-03concert0
2026-08-03match1
2026-08-03market1
2026-08-03festival0
2026-08-10exhibition1
2026-08-10concert0
2026-08-10match2
2026-08-10market0
2026-08-10festival0
2026-08-17exhibition1
2026-08-17concert1
2026-08-17match0
2026-08-17market0
2026-08-17festival0
2026-08-24exhibition0
2026-08-24concert1
2026-08-24match1
2026-08-24market0
2026-08-24festival1
2026-08-31exhibition1
2026-08-31concert1
2026-08-31match0
2026-08-31market0
2026-08-31festival0
2026-09-07exhibition1
2026-09-07concert0
2026-09-07match1
2026-09-07market1
2026-09-07festival0
2026-09-14exhibition1
2026-09-14concert0
2026-09-14match1
2026-09-14market1
2026-09-14festival3
2026-09-21exhibition0
2026-09-21concert0
2026-09-21match0
2026-09-21market0
2026-09-21festival0
2026-09-28exhibition0
2026-09-28concert0
2026-09-28match0
2026-09-28market0
2026-09-28festival0
2026-10-05exhibition0
2026-10-05concert2
2026-10-05match0
2026-10-05market1
2026-10-05festival1
2026-10-12exhibition1
2026-10-12concert0
2026-10-12match1
2026-10-12market2
2026-10-12festival0
2026-10-19exhibition2
2026-10-19concert1
2026-10-19match0
2026-10-19market0
2026-10-19festival1
2026-10-26exhibition2
2026-10-26concert1
2026-10-26match2
2026-10-26market0
2026-10-26festival0

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

The data

The files this sample uploads: synthetic data generated at a real place. Download any of them and add them to a project of your own.

DatasetKindRowsTime spanFile
Venues
Venues and attractions.		vector, Point40venues.geojson
Footfall counters
Pedestrian footfall counters.		vector, Point30footfall_counters.geojson
Footfall by hour
Hourly pedestrian counts per counter over the festival week (joins to Footfall counters on counter_id). Dated by timestamp (hour).		table5,0402026-09-14
to 2026-09-20		footfall_hourly.csv
Visitor check-ins
Check-ins from the city visitor app (opt-in, jittered). Dated by checked_in_at (minute).		vector, Point1,7872026-08-01
to 2026-09-30		visitor_checkins.geojson
Hotels
Hotels with stars, rooms and average daily rate.		vector, Point120hotels.geojson
Districts
City districts with residents and tourist beds.		vector, Polygon10districts.geojson
Transit stations
Metro and rail stations.		vector, Point25transit_stations.geojson
Events
Scheduled events by day. Dated by date (day).		vector, Point602026-06-02
to 2026-10-31		events.geojson
Short-term rentals
Short-term rental listings.		vector, Point359short_term_rentals.geojson
Hotel occupancy by district and month
Monthly hotel occupancy per district (joins to Districts on area_code). Dated by month (month).		table1202025-10-01
to 2026-09-01		hotel_occupancy_monthly.csv
Walking routes
Signed walking routes between venues.		vector, LineString7walking_routes.geojson
Event mobility plan
A plan naming venues, counters and a position.		documentevent_mobility_plan.md, event_mobility_plan.pdf

What the data was built to show

  • Visitors concentrate at the waterfront near 41.37806, 2.17872 and in the old town.
  • The festival weekend of 2026-09-19 and 20 spikes check-ins and footfall.
  • Hotels and short-term rentals cluster in the old town districts.

Expected analysis families: hot_spots, space_time, density, aggregation.

How the product read each dataset

The profile written when the data was added, before Start; no model call is needed for it.

  • Venues: 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.
  • Footfall counters: 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.
  • Footfall by hour: 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).
  • Visitor check-ins: 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.
  • Hotels: 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.
  • Districts: 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.
  • Transit stations: 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.
  • Events: 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.
  • Short-term rentals: 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.
  • Hotel occupancy by district and month: 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).
  • Walking routes: 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.
  • Event mobility plan: 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).

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