Smart City Sensor Event Streams

This dataset aggregates real-time event streams from diverse IoT sensors deployed across urban environments, capturing air quality, noise, light, and pedestrian flow metrics with precise geolocation and timestamping. It enables advanced urban monitoring, anomaly detection, and data-driven resource allocation for smart city initiatives. The dataset's granular, multi-modal structure supports both operational dashboards and in-depth analytics for city planners and researchers.

  • last updated 22 Jul 2025
  • by GoMask
The brief that made it

Real-time urban environment monitoring and visualization

Sample rows

preview · 8 of 200 rows · all 16 columns
event_idstringstatusstringbattery_levelfloatsensor_typestringsensor_idstringevent_timestampdatetimelocation_latitudefloatlocation_longitudefloatlocation_descriptionstringair_quality_pm25floatair_quality_pm10floatair_quality_no2floatnoise_level_dbfloatlight_level_luxfloatpedestrian_countintegerfirmware_versionstring
EVT00001normal82.5air_qualitySEN_AQ_0012021-03-15T09:17:00Z40.748817-73.9854285th Ave & W 34th St, NYC18.628.120.4blankblankblank2.3.1
EVT00002anomaly10.3noiseSEN_NI_0042022-08-01T17:54:00Z35.689487139.691706Shibuya Crossing, Tokyoblankblankblank91.7blankblank1.4.0
EVT00003normal90.2lightSEN_LI_0022023-05-10T11:35:00Z48.858372.294481Eiffel Tower Plaza, Parisblankblankblankblank15500blank3.2.0
EVT00004normal87.9pedestrian_flowSEN_PF_0032021-12-31T23:59:59Z51.507351-0.127758Piccadilly Circus, Londonblankblankblankblankblank1833.3.3
EVT00005anomaly91.5pedestrian_flowSEN_PF_0082022-03-01T16:20:00Z37.774929-122.419416Market St & 5th St, San Franciscoblankblankblankblankblank9823.0.1
EVT00006anomaly13.8air_qualitySEN_AQ_0052023-01-13T03:11:00Z28.61393977.209021Connaught Place, New Delhi112.2315.631.7blankblankblank2.8.2
EVT00007normal78.1noiseSEN_NI_0092023-03-15T12:00:00Z34.052234-118.243685Pershing Square, Los Angelesblankblankblank44.2blankblank2.6.7
EVT00008anomaly99.7lightSEN_LI_0102022-09-06T22:02:00Z-33.86882151.209296Circular Quay, Sydneyblankblankblankblank22800.5blank3.0.8

What the 200 rows show

from the 200-row sample

Offline status stands out: mean battery_level is 0.04, against 61.7 for the rest.

  • 71.2median battery_level
  • 17.3median air_quality_pm25
  • 33.0median air_quality_pm10
  • 18.8median air_quality_no2
  • 59.8median noise_level_db
  • 15,200median light_level_lux
Mean battery_level by status200 rows
0408079.8normal117 rows35.2anomaly53 rows0.04offline18 rows2.6error12 rows
battery_level200 rows, in bands of 10
020403732000126303836050100battery_level →

Median 71.2, from 0.0 to 100.0.

sensor_type200 rows · 4 values
  1. air_quality52
  2. noise52
  3. light48
  4. pedestrian_flow48
16 columns by typefrom the column list below
  • string 6
  • integer 1
  • float 8
  • datetime 1

Columns

16 columns in three groups
blueprint · 16 columns
columntypedescriptionexample
Text 6 columns
event_idstringUnique identifier for each sensor event recorduniqueEVT00001
sensor_idstringUnique identifier for the physical IoT sensor deviceSEN_AQ_001
sensor_typestringType of sensor reporting the event (e.g., air_quality, noise, light, pedestrian_flow)air_quality · noise · light · pedestrian_flowair_quality
location_descriptionstringHuman-readable description of the sensor location (e.g., 'Main St & 3rd Ave')optional5th Ave & W 34th St, NYC
statusstringStatus of the sensor event (e.g., normal, anomaly, offline, error)normal · anomaly · offline · error · optionalnormal
firmware_versionstringFirmware version of the sensor deviceoptional2.3.1
Numbers 9 columns
location_latitudefloatLatitude coordinate of the sensor location-90 to 9040.748817
location_longitudefloatLongitude coordinate of the sensor location-180 to 180-73.985428
air_quality_pm25floatParticulate matter 2.5 concentration (µg/m³) if sensor_type is air_quality0 or more · optional18.6
air_quality_pm10floatParticulate matter 10 concentration (µg/m³) if sensor_type is air_quality0 or more · optional28.1
air_quality_no2floatNitrogen dioxide concentration (ppb) if sensor_type is air_quality0 or more · optional20.4
noise_level_dbfloatNoise level in decibels (dB) if sensor_type is noise0 or more · optional91.7
light_level_luxfloatAmbient light level in lux if sensor_type is light0 or more · optional15500
pedestrian_countintegerNumber of pedestrians detected in the event interval if sensor_type is pedestrian_flow0 or more · optional183
battery_levelfloatBattery level of the sensor at the time of event (percentage, 0-100)0 to 100 · optional82.5
Dates and times 1 column
event_timestampdatetimeDate and time when the sensor event was recorded (UTC)2021-03-15T09:17:00Z

Use it for

  • median battery…71.2200 rowsmean battery level by…79.8norm…35.2anom…0.04offl…2.6error

    A technology dashboard

    Battery_level by status and a breakdown of sensor_type. Excel, Power BI or Tableau.

  • Why do the 18 offline rows have a mean battery_level of 0.04?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable events with sensor_id, sensor_type and event_timestamp to fill a screen in front of a buyer.

Not quite right?

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Same 16 columns, your size and your rules. See 20 rows before you pay.

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This dataset200 rows16 columns
Yours1,000 rows16 columnslocation_latitude: UK only

blueprint · smart-city-sensor-event-streams

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 16 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with
  • Sensor outages randomly simulated
  • Readings show time and spatial correlation
  • Anomalous spikes indicate events
  • Different sensor types follow unique sampling rates
  • Maintenance logs included
Rows
Open the blueprint in Data Factory

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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
smart-city-sensor-event-streams

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