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Synthetic Smart City IoT Dataset — 1K Rows #0dcf

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 #0dcf."

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: 1360.

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	overcast	0.83	23.68	954.39	32.68	4.62	1.70	23.18	normal	stochastic	16	16	16	0.01	6	5	2	0	1	2	general_public	102.81	22.85	12	11.96	11.67	false	42.84	20	19.93	19.44	false	56.47	true	17.58	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	partly_cloudy	0.44	23.74	988.49	33.74	3.51	1.29	22.06	normal	stochastic	18	18	20	0.01	1	7	2	5	4	1	families	104.46	23.21	12	11.94	11.21	false	43.52	20	19.91	18.93	false	56.45	true	18.76	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	0.21	23.47	1,070.64	37.13	3.89	1.43	21.10	normal	stochastic	15	15	17	0.01	8	4	2	2	1	0	general_public	158.88	35.31	12	11.95	12.21	false	66.20	20	19.92	21.28	false	55.53	true	17.87	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	0.29	22.92	1,012.39	36.29	4.77	1.75	20.80	busy	stochastic	33	33	32	0.02	10	5	5	3	7	2	general_public	159.67	35.48	12	11.96	11.25	false	66.53	20	19.94	21.13	false	56.25	true	16.08	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	0.34	22.27	1,011.10	37.70	5.40	1.98	20.29	busy	stochastic	27	27	29	0.02	10	2	5	3	6	3	general_public	154.06	34.24	12	11.96	10.45	false	64.19	20	19.94	18.79	false	54.63	true	14.61	no thermal stress	false	low	low	low	low	thermal	["thermal","crowding","noise"]	[]	false	0.75	true	action_low	Overall action level low; dominant issue thermal.
6	2026-07-14T02:30:00+01:00	1800	weekday	clear	0.17	22.00	1,121.23	42.51	5.06	1.86	19.50	busy	stochastic	34	34	37	0.03	10	6	7	4	5	5	general_public	150.31	33.40	12	11.96	12.68	false	62.63	20	19.94	17.96	false	57.22	true	14.67	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	0.23	22.22	1,188.79	44.48	4.79	1.76	19.92	busy	stochastic	26	26	34	0.03	14	4	6	3	3	4	general_public	213.42	47.43	12	11.96	13.03	false	88.93	20	19.94	23.15	false	57.74	true	15.44	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	0.15	22.87	1,148.20	41.30	3.33	1.22	20.32	normal	stochastic	18	18	23	0.02	7	5	7	1	3	0		171.13	38.03	12	11.95	12.41	false	71.30	20	19.91	19.71	false	58.14	true	18.02	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	0.08	22.95	1,131.49	40.50	3.23	1.18	20.19	normal	stochastic	25	25	29	0.02	11	8	5	0	4	1	general_public	231.69	51.49	12	11.95	11.31	false	96.54	20	19.91	19.22	false	58.58	true	18.17	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	clear	0.09	24.22	1,158.61	38.42	1.09	0.40	21.48	normal	stochastic	26	26	26	0.02	11	6	5	1	2	1	general_public	248.19	55.15	12	11.87	12.16	false	103.41	20	19.79	14.81	false	59.35	true	22.23	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 #0dcf. [Dataset]. IoTSyn Generated. https://iotsyn.com/view.php?uid=iotsyn_6a4ec9e1ce0653.03429722

Source: IoTSyn Generated (2026)

Indexed by IoTDataset.com on Jul 28, 2026

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Synthetic Smart City IoT Dataset — 1K Rows #0dcf

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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