IoTSyn Generated
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Synthetic Smart City IoT Dataset — Heatwave, 672 Rows
Smart City & Urban Sensing
Smart City
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672 rows · 64 columns
IoTSyn v4.4.0
License
Abstract
"Free CC0 synthetic dataset: 672 rows of UTCI thermal comfort, PM2.5 and NO2 air quality, occupancy and LAeq noise. Heatwave."
Description
Overview
Synthetic urban public-space IoT time series generated by the IoTSyn v4.4.0 reference engine (Smart City & Public Spaces Edition). A coupled physical–stochastic model produces meteorology (solar geometry, cloud/precipitation Markov chains, AR(1) air temperature with a nocturnal urban-heat-island term, and a Weibull wind field induced through a Gaussian copula), human presence (a state-modulated non-homogeneous Poisson arrival process with LogNormal dwell times and infinite-server occupancy), road traffic, species-resolved air quality from a well-mixed mass-balance box model (PM2.5 and NO2, with separate physical and sensor columns), environmental noise composed in the energy domain (LAeq), and UTCI thermal comfort. A three-way separation between physical truth, sensor observation, and decision output is preserved throughout, and a multi-hazard decision layer emits an ordinal urban risk vector (thermal, air quality, noise, crowding), operational action levels, risk episodes, and provenance-tagged behaviour-change strategy objects. All randomness derives from named substreams of a portable MT19937 generator; identical configuration and seed reproduce byte-identical output. Each dataset ships with machine-readable sidecars: normalized scenario, domain validation report, risk episodes, and the strategy file.
672 data points, 64 columns. Config: IoTSyn v4.4.0 engine; master_seed: 123456; Capacity: 500; Controls: preset=heatwave; EXTENDED (non-normative).
Column Schema
| Column | Description |
|---|---|
timestamp | string_iso8601 — interval start, explicit UTC offset |
step_duration_seconds | integer, s |
day_type | enum {weekday | weekend | holiday} |
cloud_state | enum {clear | partly_cloudy | overcast} |
cloud_fraction | float, dimensionless — range 0..1 |
precipitation_mm_h | float, mm/h — >=0; exactly 0 when dry |
air_temperature_c | float, degC |
vapour_pressure_pa | float, Pa |
relative_humidity_pct | float, % — 0..100 after roundoff clamp |
wind_speed_10m_m_s | float, m/s |
wind_speed_pedestrian_m_s | float, m/s |
mean_radiant_temperature_c | float, degC |
crowd_state | enum {quiet | normal | busy | event} |
crowd_state_mechanism | enum {stochastic | forced} |
offered_arrivals_count | integer, persons |
admitted_arrivals_count | integer, persons |
rejected_arrivals_count | integer, persons |
deferred_arrivals_count | integer, persons |
deferred_timeout_count | integer, persons |
queue_length_persons | integer, persons — 0 unless capacity_mode=defer_fifo |
occupancy_persons | integer, persons |
operational_capacity_ratio | float, dimensionless |
audience_general_public_persons | integer, persons |
audience_families_persons | integer, persons |
audience_elderly_persons | integer, persons |
audience_students_persons | integer, persons |
audience_commuters_persons | integer, persons |
audience_event_attendees_persons | integer, persons |
dominant_audience | enum, nullable {general_public | families | elderly | students | commuters | event_attendees} |
traffic_flow_vehicles_h | float, vehicles/h |
pm25_emission_ug_s | float, ug/s |
pm25_background_ug_m3 | float, ug/m3 |
pm25_physical_ug_m3 | float, ug/m3 |
pm25_sensor_raw_ug_m3 | float, ug/m3 |
pm25_sensor_calibrated_ug_m3 | float, ug/m3, nullable |
pm25_rolling_24h_ug_m3 | float, ug/m3, nullable — null until complete window |
pm25_rolling_complete | boolean |
no2_emission_ug_s | float, ug/s |
no2_background_ug_m3 | float, ug/m3 |
no2_physical_ug_m3 | float, ug/m3 |
no2_sensor_raw_ug_m3 | float, ug/m3 |
no2_sensor_calibrated_ug_m3 | float, ug/m3, nullable |
no2_rolling_24h_ug_m3 | float, ug/m3, nullable — null until complete window |
no2_rolling_complete | boolean |
LAeq_15min_dBA | float, dB(A), nullable — column name embeds the declared window; null until complete window unless warm-up supplies history |
noise_window_complete | boolean |
utci_c | float, degC |
utci_stress_category | enum {extreme cold stress | very strong cold stress | strong cold stress | moderate cold stress | slight cold stress | no thermal stress | moderate heat stress | strong heat stress | very strong heat stress | extreme heat stress} |
utci_input_clamped | boolean |
wbgt_outdoor_c | float, degC, nullable |
thermal_risk_level | enum, nullable {low | caution | high_risk | severe} |
air_quality_risk_level | enum, nullable {low | caution | high_risk | severe} |
noise_risk_level | enum, nullable {low | caution | high_risk | severe} |
crowding_risk_level | enum, nullable {low | caution | high_risk | severe} |
overall_action_level | enum, nullable {low | caution | high_risk | severe} |
dominant_issue | enum, nullable {thermal | air_quality | noise | crowding} |
co_dominant_issues | json_array_of_enum {thermal | air_quality | noise | crowding} |
elevated_issues | json_array_of_enum {thermal | air_quality | noise | crowding} |
elevated_hazard_count | integer |
compound_risk_flag | boolean |
risk_completeness_fraction | float, dimensionless |
overall_action_level_provisional | boolean |
risk_communication_template_id | string |
risk_communication_message | string |
Mathematical Models
- Meteorology:
AR(1) air temperature + sinusoidal diurnal cycle + nocturnal urban-heat-island (Oke); Magnus–WMO saturation vapour pressure; Weibull 10 m wind via an AR(1) Gaussian copula (Sklar), pedestrian wind through a log profile - Thermal comfort:
UTCI operational polynomial (Bröde et al. 2012) from air temperature, mean radiant temperature, 10 m wind and vapour pressure; clamp-and-flag domain policy; 10 scientific categories plus a coarser 4-level operational band - Air quality:
Single well-mixed box mass balance with ventilation exchange, deposition and chemical first-order loss and a traffic-driven source; exact exponential one-step update; mean-preserving log-normal sensor observation model; 24 h rolling means - Presence:
Discrete-time Markov-modulated NHPP arrivals with thermal/rain/wind deterrence; exact per-interval Poisson counts with sorted uniform offsets; LogNormal dwell; infinite-server occupancy reconstructed from events; largest-remainder audience allocation - Noise:
A-weighted equivalent continuous level (LAeq) over a declared window, summed in the energy (intensity) domain per ISO 1996 - Risk & strategy:
Conservative-max hazard vector with compound-risk indicators; compound priority score H·log10(1 + person-minutes / E_ref); ex-post, provenance-tagged behaviour-change strategies
Use Cases
- Thermal-comfort (UTCI) and heat-risk analytics
- Air-quality (PM2.5 / NO2) exposure and sensor calibration / drift research
- Occupancy and arrivals (NHPP) estimation benchmarks with exact ground truth
- Environmental-noise (LAeq) exposure analysis
- Multi-hazard urban risk aggregation and decision-support research
Reproducibility
Seed: 123456. Same seed + parameters = identical output. IoTSyn v3.1.
Machine-readable Sidecars
- Normalized scenario + controls (reproducibility): scenario.json
- Domain validation report (recomputed invariants & coverage): validation.json
- Elevated-risk episodes: episodes.json
- Behaviour-change strategy file (priority-ranked, provenance-tagged): strategy.json
Sample Data (18 rows · 64 columns)
| # | timestamp | step_duration_seconds | day_type | cloud_state | cloud_fraction | precipitation_mm_h | air_temperature_c | vapour_pressure_pa | relative_humidity_pct | wind_speed_10m_m_s | wind_speed_pedestrian_m_s | mean_radiant_temperature_c | crowd_state | crowd_state_mechanism | offered_arrivals_count | admitted_arrivals_count | rejected_arrivals_count | deferred_arrivals_count | deferred_timeout_count | queue_length_persons | occupancy_persons | operational_capacity_ratio | audience_general_public_persons | audience_families_persons | audience_elderly_persons | audience_students_persons | audience_commuters_persons | audience_event_attendees_persons | dominant_audience | traffic_flow_vehicles_h | pm25_emission_ug_s | pm25_background_ug_m3 | pm25_physical_ug_m3 | pm25_sensor_raw_ug_m3 | pm25_sensor_calibrated_ug_m3 | pm25_rolling_24h_ug_m3 | pm25_rolling_complete | no2_emission_ug_s | no2_background_ug_m3 | no2_physical_ug_m3 | no2_sensor_raw_ug_m3 | no2_sensor_calibrated_ug_m3 | no2_rolling_24h_ug_m3 | no2_rolling_complete | LAeq_15min_dBA | noise_window_complete | utci_c | utci_stress_category | utci_input_clamped | wbgt_outdoor_c | thermal_risk_level | air_quality_risk_level | noise_risk_level | crowding_risk_level | overall_action_level | dominant_issue | co_dominant_issues | elevated_issues | elevated_hazard_count | compound_risk_flag | risk_completeness_fraction | overall_action_level_provisional | risk_communication_template_id | risk_communication_message |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2026-07-14T00:00:00+01:00 | 900 | weekday | clear | 0.10 | 0 | 34.31 | 1,914.09 | 35.43 | 3.01 | 1.10 | 31.63 | normal | stochastic | 4 | 4 | 0 | 0 | 0 | 0 | 16 | 0.03 | 3 | 0 | 10 | 1 | 2 | 0 | elderly | 212.48 | 47.22 | 12 | 11.94 | 11.97 | 12.05 | true | 88.53 | 20 | 19.91 | 20.31 | 20.00 | true | 57.02 | true | 32.63 | strong heat stress | false | high_risk | low | caution | low | high_risk | thermal | ["thermal"] | ["thermal"] | 1 | false | 1 | false | action_high_risk | Overall action level high_risk; dominant issue thermal. | |||
| 2 | 2026-07-14T00:15:00+01:00 | 900 | weekday | clear | 0.19 | 0 | 34.85 | 1,915.23 | 34.42 | 1.64 | 0.60 | 32.41 | normal | stochastic | 1 | 1 | 0 | 0 | 0 | 0 | 8 | 0.02 | 1 | 1 | 3 | 1 | 2 | 0 | elderly | 175.52 | 39 | 12 | 11.90 | 11.73 | 12.02 | true | 73.13 | 20 | 19.83 | 17.73 | 19.99 | true | 55.36 | true | 34.07 | strong heat stress | false | high_risk | low | caution | low | high_risk | thermal | ["thermal"] | ["thermal"] | 1 | false | 1 | false | action_high_risk | Overall action level high_risk; dominant issue thermal. | |||
| 3 | 2026-07-14T00:30:00+01:00 | 900 | weekday | clear | 0.26 | 0 | 34.42 | 1,941.31 | 35.73 | 2.85 | 1.04 | 32.20 | quiet | stochastic | 1 | 1 | 0 | 0 | 0 | 0 | 7 | 0.01 | 1 | 1 | 2 | 1 | 2 | 0 | 175.30 | 38.95 | 12 | 11.94 | 12.82 | 12.03 | true | 73.04 | 20 | 19.90 | 21.25 | 20.01 | true | 56.23 | true | 33 | strong heat stress | false | high_risk | low | caution | low | high_risk | thermal | ["thermal"] | ["thermal"] | 1 | false | 1 | false | action_high_risk | Overall action level high_risk; dominant issue thermal. | ||||
| 4 | 2026-07-14T00:45:00+01:00 | 900 | weekday | clear | 0.15 | 0 | 34.03 | 1,823.31 | 34.28 | 4.79 | 1.76 | 31.48 | quiet | stochastic | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0.01 | 1 | 0 | 2 | 0 | 1 | 0 | elderly | 117.74 | 26.16 | 12 | 11.96 | 13.55 | 12.03 | true | 49.06 | 20 | 19.93 | 20.24 | 20.00 | true | 55.97 | true | 31.31 | moderate heat stress | false | caution | low | caution | low | caution | thermal | ["thermal","noise"] | [] | 0 | false | 1 | false | action_caution | Overall action level caution; dominant issue thermal. | |||
| 5 | 2026-07-14T01:00:00+01:00 | 900 | weekday | clear | 0.16 | 0 | 33.43 | 1,817.59 | 35.34 | 2.93 | 1.07 | 30.92 | quiet | stochastic | 6 | 6 | 0 | 0 | 0 | 0 | 8 | 0.02 | 2 | 0 | 4 | 0 | 2 | 0 | elderly | 94.15 | 20.92 | 12 | 11.93 | 9.78 | 12.01 | true | 39.23 | 20 | 19.89 | 20.58 | 19.99 | true | 55.41 | true | 31.57 | moderate heat stress | false | caution | low | caution | low | caution | thermal | ["thermal","noise"] | [] | 0 | false | 1 | false | action_caution | Overall action level caution; dominant issue thermal. | |||
| 6 | 2026-07-14T01:15:00+01:00 | 900 | weekday | clear | 0.42 | 0 | 33.32 | 1,714.01 | 33.53 | 1.20 | 0.44 | 31.58 | busy | stochastic | 9 | 9 | 0 | 0 | 0 | 0 | 15 | 0.03 | 4 | 1 | 8 | 0 | 1 | 1 | elderly | 90.80 | 20.18 | 12 | 11.86 | 12.98 | 12.02 | true | 37.83 | 20 | 19.76 | 20.33 | 20.00 | true | 57.08 | true | 32.45 | strong heat stress | false | high_risk | low | caution | low | high_risk | thermal | ["thermal"] | ["thermal"] | 1 | false | 1 | false | action_high_risk | Overall action level high_risk; dominant issue thermal. | |||
| 7 | 2026-07-14T01:30:00+01:00 | 900 | weekday | clear | 0.24 | 0 | 33.02 | 1,695.19 | 33.73 | 1.42 | 0.52 | 30.74 | busy | stochastic | 13 | 13 | 0 | 0 | 0 | 0 | 23 | 0.05 | 6 | 2 | 12 | 1 | 1 | 1 | elderly | 86.01 | 19.11 | 12 | 11.87 | 12.81 | 12.02 | true | 35.84 | 20 | 19.79 | 19.50 | 19.97 | true | 56.77 | true | 31.90 | moderate heat stress | false | caution | low | caution | low | caution | thermal | ["thermal","noise"] | [] | 0 | false | 1 | false | action_caution | Overall action level caution; dominant issue thermal. | |||
| 8 | 2026-07-14T01:45:00+01:00 | 900 | weekday | clear | 0.14 | 0 | 32.89 | 1,595.86 | 31.99 | 0.69 | 0.25 | 30.30 | busy | stochastic | 16 | 16 | 0 | 0 | 0 | 0 | 31 | 0.06 | 8 | 5 | 12 | 1 | 3 | 2 | elderly | 86.08 | 19.13 | 12 | 11.78 | 11.06 | 12.01 | true | 35.87 | 20 | 19.64 | 23.32 | 20.02 | true | 57.35 | true | 31.62 | moderate heat stress | false | caution | low | caution | low | caution | thermal | ["thermal","noise"] | [] | 0 | false | 1 | false | action_caution | Overall action level caution; dominant issue thermal. | |||
| 9 | 2026-07-14T02:00:00+01:00 | 900 | weekday | clear | 0.17 | 0 | 32.36 | 1,614.54 | 33.33 | 0.59 | 0.22 | 29.87 | normal | stochastic | 5 | 5 | 0 | 0 | 0 | 0 | 21 | 0.04 | 5 | 4 | 8 | 0 | 2 | 2 | elderly | 91.89 | 20.42 | 12 | 11.76 | 11.90 | 12.02 | true | 38.29 | 20 | 19.60 | 22.02 | 20.06 | true | 56.85 | true | 31.12 | moderate heat stress | false | caution | low | caution | low | caution | thermal | ["thermal","noise"] | [] | 0 | false | 1 | false | action_caution | Overall action level caution; dominant issue thermal. | |||
| 10 | 2026-07-14T02:15:00+01:00 | 900 | weekday | clear | 0.28 | 0 | 32.09 | 1,581.64 | 33.16 | 1.26 | 0.46 | 29.93 | normal | stochastic | 4 | 4 | 0 | 0 | 0 | 0 | 16 | 0.03 | 4 | 3 | 5 | 1 | 1 | 2 | elderly | 106.06 | 23.57 | 12 | 11.86 | 10.90 | 12.02 | true | 44.19 | 20 | 19.77 | 16.81 | 20.06 | true | 58.95 | true | 30.83 | moderate heat stress | false | caution | low | caution | low | caution | thermal | ["thermal","noise"] | [] | 0 | false | 1 | false | action_caution | Overall action level caution; dominant issue thermal. |
Showing first 10 of 18 sample rows
Data Visualization Interactive
Numeric Trends
Category Distribution
Machine-Readable Artifacts JSON
This dataset ships companion files alongside the CSV. Together they make the run independently reproducible and auditable without re-running the generator.
Scenario + controls
Normalized scenario configuration and control parameters — everything needed to reproduce the run
scenario.json
Validation report
Recomputed invariants, coverage checks and Kolmogorov–Smirnov results for this dataset
validation.json
Risk episodes
Table of elevated-risk episodes detected across the simulated period
episodes.json
Strategy file
Priority-ranked, provenance-tagged behaviour-change strategy objects
strategy.json
Cite This Dataset
IoTSyn Generated (2026). Synthetic Smart City IoT Dataset — Heatwave, 672 Rows. [Dataset]. IoTSyn Generated. https://iotsyn.com/view.php?uid=iotsyn_6a602c5d216968.06470074
Source: IoTSyn Generated (2026)
Indexed by IoTDataset.com on Jul 28, 2026
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Related Topics & Keywords
#smart city
#public spaces
#urban sensing
#utci
#thermal comfort
#air quality
#pm2.5
#no2
#occupancy
#laeq
#noise
#nhpp
#box model
#multi-hazard
#reproducible
#v4.4.0
#synthetic data
#physics-based
#cc0
#iotsyn
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