® Sponsored by Environmental and Observational Data in Urban Planning 95th OGC Technical Committee Boulder, Colorado USA Katharina Schleidt 2 June 2015 Copyright © 2015 Open Geospatial Consortium

OGC ®Introduction 2 Why does Urban Planning need various types of Environmental and Observational data –What types of data are out there? –What are they good for? –Tacking data to the map What are Observations? How to provide Observations? CitySee

OGC ® Environmental Data 3 Air Quality (alerts) Water Quality (bathing water, drinking water) & Quantity Soil Quality (pollutants from landfills) Radiation Measurements Biodiversity Observations (tree cadastre, What’s on in my backyard)

OGC ® Urban Data 4 Other Sensors Assisted Living (have fallen down, tracking of patients with dementia, notification to take medications, tracking devices) Traffic (when is the tram coming, what‘s the fastest route) Other Observations: Illegal rubbish tipping (easy to report) Dangerous spots (don’t want to be there late at night!) Internet of Things (everything is connected) Wierd stuff: Tracking twitter posts (tracking forest fires)

OGC ® Environmental Data in Urban Planning 5 Sudplan FP7 Project Climate Change: how to plan our cities so they won't flood in 20 years based on changing weather patterns Windy Cities: depending on how the city is built, it's inevitable, or maybe not (also related to the hot spots, if we could channel the wind there, things would be better) Urban Hot Spots: how to find them, what to do about

OGC ® What is an observation? 6 To understand the data from an observation or measurement, we must know: What was measured (observedProperty) Where was it measured (featureOfInterest) How was it measured (procedure) When was it measured (phenomenonTime) Data quality information (resultQuality) And of course, we need the result of the observation.

OGC ® What is an observation? 7 To understand the data from an observation or measurement, we must know: What was measured (observedProperty) Where was it measured (featureOfInterest) How was it measured (procedure) When was it measured (phenomenonTime) Data quality information (resultQuality) And of course, we need the result of the observation. From ISO 19156:2011 Geographic information – Observations and measurements

OGC ® What is an observation? 8 From ISO 19156:2011 Geographic information – Observations and measurements To understand the data from an observation or measurement, we must know: What was measured (observedProperty) Where was it measured (featureOfInterest) How was it measured (procedure) When was it measured (phenomenonTime) Data quality information (resultQuality) And of course, we need the result of the observation.

OGC ® What is an observation? 9 From ISO 19156:2011 Geographic information – Observations and measurements To understand the data from an observation or measurement, we must know: What was measured (observedProperty) Where was it measured (featureOfInterest) How was it measured (procedure) When was it measured (phenomenonTime) Data quality information (resultQuality) And of course, we need the result of the observation.

OGC ® What is an observation? 10 From ISO 19156:2011 Geographic information – Observations and measurements To understand the data from an observation or measurement, we must know: What was measured (observedProperty) Where was it measured (featureOfInterest) How was it measured (procedure) When was it measured (phenomenonTime) Data quality information (resultQuality) And of course, we need the result of the observation.

OGC ® What is an observation? 11 From ISO 19156:2011 Geographic information – Observations and measurements To understand the data from an observation or measurement, we must know: What was measured (observedProperty) Where was it measured (featureOfInterest) How was it measured (procedure) When was it measured (phenomenonTime) Data quality information (resultQuality) And of course, we need the result of the observation.

OGC ® What is an observation? 12 From ISO 19156:2011 Geographic information – Observations and measurements To understand the data from an observation or measurement, we must know: What was measured (observedProperty) Where was it measured (featureOfInterest) How was it measured (procedure) When was it measured (phenomenonTime) Data quality information (resultQuality) And of course, we need the result of the observation.

OGC ® What is an observation? 13 From ISO 19156:2011 Geographic information – Observations and measurements To understand the data from an observation or measurement, we must know: What was measured (observedProperty) Where was it measured (featureOfInterest) How was it measured (procedure) When was it measured (phenomenonTime) Data quality information (resultQuality) And of course, we need the result of the observation.

OGC ® What is an observation? 14 From ISO 19156:2011 Geographic information – Observations and measurements To understand the data from an observation or measurement, we must know: What was measured (observedProperty) Where was it measured (featureOfInterest) How was it measured (procedure) When was it measured (phenomenonTime) Data quality information (resultQuality) And of course, we need the result of the observation.

OGC ® What is an observation? 15 From ISO 19156:2011 Geographic information – Observations and measurements To understand the data from an observation or measurement, we must know: What was measured (observedProperty) Where was it measured (featureOfInterest) How was it measured (procedure) When was it measured (phenomenonTime) Data quality information (resultQuality) And of course, we need the result of the observation. The contextual information usually already exists

OGC ® Comparison to GML Observation 16 What was measured (observedProperty) Where was it measured (featureOfInterest) How was it measured (procedure) When was it measured (phenomenonTime) The result of the observation. From ISO 19156:2011 Geographic information – Observations and measurements From ISO 19136:2007 Geographic information -- Geography Markup Language (GML)

OGC ® Providing Observational Data 17 In the environmental domain, the use of OGC Sensor Observation Service (SOS) is being increasingly adopted –Just as valid for other domains Advantage to WFS for observational or measurement data provision: –Capabilities provide information on what was measured where with what methodology over which timeperiod –getObservation request allows for temporal subsampling Common misconception that this can only be used for sensor data

OGC ® WOS instead of SOS? 18 It could make sense to define a more abstract Web Observation Service (WOS) logically nested above SOS Basic structure ideal for non-sensor data Semantic constraints relevant with sensors could be removed to simplify wider use European INSPIRE initiative reduced complexity of methodological information (INSPIRE Process type replacing SensorML) Also saves a lot of time explaining to people worried about not having a sensor!

OGC ®Conclusion 19 Observational data valuable for modern Urban Planning –Environmental Data –Sensor Data –Observational Data (humans as sensors) Basic concepts of Observations from ISO valid Sensor Observation Service may be a bit too complex –Define Web Observation Service Will help us bring Urban Planning beyond bricks & mortar to people & their needs

OGC ® Data is all over! Copyright © 2015 Open Geospatial Consortium When‘s the tram coming? What‘s that tree? Where‘s home? Where‘s the hospital? You left the Kettle on! Will this area flood? Is there danger from that spilled tanker? Is the air always so bad here?

® Sponsored by Katharina Schleidt 21 Umweltbundesamt OGC Meeting Boulder ■