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Synthetic Smart City IoT Dataset — 1K Rows #b252

Smart City & Urban Sensing Smart City

Jul 28, 2026

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1,000 rows · 64 columns

IoTSyn v4.4.0

License

Abstract

"Free CC0 synthetic dataset: 1,000 rows of UTCI thermal comfort, PM2.5 and NO2 air quality, occupancy and LAeq noise. 1K Rows #b252."

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.

1,000 data points, 64 columns. Config: IoTSyn v4.4.0 engine; master_seed: 123456; Capacity: 1780.

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

Domain validation report (recomputed invariants & coverage): validation.json

Sample Data (18 rows · 64 columns)

#	timestamp	step_duration_seconds	day_type	cloud_state	cloud_fraction	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	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_rolling_complete	no2_emission_ug_s	no2_background_ug_m3	no2_physical_ug_m3	no2_sensor_raw_ug_m3	no2_rolling_complete	LAeq_15min_dBA	noise_window_complete	utci_c	utci_stress_category	utci_input_clamped	thermal_risk_level	noise_risk_level	crowding_risk_level	overall_action_level	dominant_issue	co_dominant_issues	elevated_issues	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	1800	weekday	clear	0.23	24.27	1,188.79	39.30	4.79	1.76	21.98	busy	stochastic	21	21	33	0.02	13	4	5	3	6	2	general_public	193.35	42.97	12	11.96	13.03	false	80.56	20	19.94	23.14	false	57.55	true	17.99	no thermal stress	false	low	caution	low	caution	noise	["noise"]	[]	false	0.75	true	action_caution	Overall action level caution; dominant issue noise.
2	2026-07-14T00:30:00+01:00	1800	weekday	clear	0.15	24.46	1,148.20	37.54	3.33	1.22	21.91	normal	stochastic	15	15	22	0.01	9	5	3	2	1	2	general_public	148.40	32.98	12	11.94	12.41	false	61.83	20	19.91	19.71	false	57.91	true	19.81	no thermal stress	false	low	caution	low	caution	noise	["noise"]	[]	false	0.75	true	action_caution	Overall action level caution; dominant issue noise.
3	2026-07-14T01:00:00+01:00	1800	weekday	clear	0.08	23.86	1,131.49	38.33	3.23	1.18	21.10	normal	stochastic	16	16	15	0.01	3	6	5	1	0	0	families	190.09	42.24	12	11.94	11.31	false	79.20	20	19.91	19.21	false	58.05	true	19.19	no thermal stress	false	low	caution	low	caution	noise	["noise"]	[]	false	0.75	true	action_caution	Overall action level caution; dominant issue noise.
4	2026-07-14T01:30:00+01:00	1800	weekday	clear	0.09	24.57	1,158.61	37.61	1.09	0.40	21.84	normal	stochastic	12	12	15	0.01	3	7	2	0	3	0	families	190.60	42.35	12	11.86	12.15	false	79.42	20	19.77	14.79	false	58.72	true	22.56	no thermal stress	false	low	caution	low	caution	noise	["noise"]	[]	false	0.75	true	action_caution	Overall action level caution; dominant issue noise.
5	2026-07-14T02:00:00+01:00	1800	weekday	clear	0.14	24.44	1,175.93	38.48	2.75	1.01	21.87	normal	stochastic	18	18	18	0.01	8	6	1	2	1	0	general_public	202.55	45.01	12	11.94	11.72	false	84.40	20	19.90	19.42	false	58.93	true	20.60	no thermal stress	false	low	caution	low	caution	noise	["noise"]	[]	false	0.75	true	action_caution	Overall action level caution; dominant issue noise.
6	2026-07-14T02:30:00+01:00	1800	weekday	clear	0.24	23.27	1,250.25	43.90	3.27	1.20	20.98	normal	stochastic	22	22	25	0.01	15	3	4	1	0	2	general_public	198.32	44.07	12	11.95	13.19	false	82.63	20	19.91	21.90	false	59.61	true	18.81	no thermal stress	false	low	caution	low	caution	noise	["noise"]	[]	false	0.75	true	action_caution	Overall action level caution; dominant issue noise.
7	2026-07-14T03:00:00+01:00	1800	weekday	clear	0.05	23.33	1,296.18	45.35	3.99	1.46	20.47	normal	stochastic	20	20	20	0.01	8	3	4	1	3	1	general_public	206.41	45.87	12	11.96	13.07	false	86	20	19.93	18.81	false	57.85	true	17.81	no thermal stress	false	low	caution	low	caution	noise	["noise"]	[]	false	0.75	true	action_caution	Overall action level caution; dominant issue noise.
8	2026-07-14T03:30:00+01:00	1800	weekday	clear	0.05	23.31	1,339.79	46.93	2.32	0.85	20.46	normal	stochastic	22	22	24	0.01	9	2	2	5	4	2	general_public	178.59	39.69	12	11.92	11.97	false	74.41	20	19.88	19.62	false	57.54	true	20.21	no thermal stress	false	low	caution	low	caution	noise	["noise"]	[]	false	0.75	true	action_caution	Overall action level caution; dominant issue noise.
9	2026-07-14T04:00:00+01:00	1800	weekday	clear	0.41	22.78	1,304.53	47.17	1.47	0.54	21.02	normal	stochastic	25	25	32	0.02	10	6	3	3	7	3	general_public	159.86	35.53	12	11.89	16.59	false	66.61	20	19.81	18.19	false	57.22	true	21.07	no thermal stress	false	low	caution	low	caution	noise	["noise"]	[]	false	0.75	true	action_caution	Overall action level caution; dominant issue noise.
10	2026-07-14T04:30:00+01:00	1800	weekday	partly_cloudy	0.61	23.06	1,342.19	47.71	2.18	0.80	21.90	event	stochastic	62	62	57	0.03	19	18	7	6	6	1	general_public	140.97	31.33	12	11.92	11.07	false	58.74	20	19.86	18.44	false	57.02	true	20.65	no thermal stress	false	low	caution	low	caution	noise	["noise"]	[]	false	0.75	true	action_caution	Overall action level caution; dominant issue noise.

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.

Validation report Recomputed invariants, coverage checks and Kolmogorov–Smirnov results for this dataset validation.json

Cite This Dataset

IoTSyn Generated (2026). Synthetic Smart City IoT Dataset — 1K Rows #b252. [Dataset]. IoTSyn Generated. https://iotsyn.com/view.php?uid=iotsyn_6a4e3d37d8a801.93843824

Source: IoTSyn Generated (2026)

Indexed by IoTDataset.com on Jul 28, 2026

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Synthetic Smart City IoT Dataset — 1K Rows #b252

Abstract

Description

Overview

Column Schema

Mathematical Models

Use Cases

Reproducibility

Machine-readable Sidecars

Sample Data (18 rows · 64 columns)

Data Visualization Interactive

Numeric Trends

Category Distribution

Machine-Readable Artifacts JSON

Cite This Dataset

Ready to Start Your Research?

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Share This Research

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