Geospatial analysis offers large potential for better understanding, modelling and visualizing our natural and artificial ecosystems, using Internet of Things as a pervasive sensing infrastructure. This presentation performs a review of research work based on the IoT, in which geospatial analysis has been employed in environmental informatics. Six different geospatial analysis methods have been identified, presented together with 26 relevant IoT initiatives adopting some of these techniques. Analysis is performed in relation to the type of IoT devices used, their deployment status and data transmission standards, data types employed, and reliability of measurements. This paper scratches the surface of this combination of technologies and techniques, providing indications of how IoT, together with geospatial analysis, are currently being used in the domain of environmental research. Presented at EnviroInfo 2018 at Munich, Germany.

1. GEOSPATIAL ANALYSIS AND INTERNET OF
THINGS IN ENVIRONMENTAL INFORMATICS
DR. ANDREAS KAMILARIS ENVIROINFO 2018

2.  An important characteristic of the measurements performed by IoT things
(i.e. sensors, mobile phones, cameras etc.) is the geo-location where
each measurement was done.
 A rich collection of measurements in space and time is necessary in
order to model and understand physical or artificial ecosystems.
 Geospatial analysis is used in order to provide high-quality analytics and
insights.
 How are geospatial analysis and IoT used in environmental informatics?
INTRODUCTION
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3. 1. Which geospatial analytical methods do IoT-based projects and research
works use?
2. What kind of IoT device types have been used?
3. Are the IoT sensors used disposable or long-term ones?
4. Are the IoT sensors used static or mobile?
5. Which are the IoT data transmission standards used?
6. Which are the sources and types of data used?
7. Which is the reliability of measurements?
8. Which is the accuracy of measurements?
9. Is calibration needed? How are problems of faulty readings addressed?
RESEARCH QUESTIONS
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4.  Keyword-based search for related work
 Web scientific indexing services Web of Science and Google Scholar
 26 highly relevant papers
 By belonging to IoT, we refer to the “connection of the physical devices to
the Internet/Web”.
METHODOLOGY
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5. GEOSPATIAL ANALYTICAL METHODS
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Buffering Network Analysis
Route planning Density analysis Interpolation/kriging
Heatmaps

6. APPLICATION AREAS VS. GEOSPATIAL METHODS
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7.  RFID tags, barcodes and QR codes
 Mobile phones and their embedded sensors (i.e. cameras, microphones)
 GPS sensing
 Ultrasonic water sensors
 Pollution sensors
 Infrared (IR) cameras
 Temperature, humidity and pressure sensors
 Geo-cubes, meteorological and hydrological sensors
 Buoy, pressure and water column heights sensors
 3D accelerometer and gyroscope sensors
 Animal collar tags
IOT DEVICES AND SENSORS
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8.  Satellite-based imagery used in:
• biodiversity
• livestock agriculture and environmental impact
• forest fire risk assessment
• vulnerability assessment
• disaster relief in earthquakes
• for identifying groundwater potential zones
• in tsunami evacuation planning (LiDAR imagery).
 Meteorological stations (i.e. air, temperature and precipitation) used for
climatological modelling
 Radar data used for recording precipitation in flood forecasting
 Spectrophotometers for detection of heavy metals
IOT DEVICES AND SENSORS
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9.  Not many papers revealed information
 Provisioned where sensor replacement was expensive or difficult
Long-term sensor deployment:
 water monitoring systems
 air quality monitoring
 monitoring landslide displacements (low-power 10-Watt solar panels)
 Deep-ocean tsunami measuring (battery-powered with four-year lifetime),
 Weather monitoring stations
 Wildlife monitoring
DISPOSABLE VS. LONG-TERM IOT SENSORS
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10. Mobile:
 mobile phones and their sensing capabilities (crowdsensing applications)
 Animal collar tags
Static:
 Landslides
 Flooding
 Tsunami
 Forest fire
 Earthquakes
 Measuring air quality
 All papers related to surface analysis and geo-statistics
MOBILE VS. STATIC IOT DEVICES
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11.  Wi-Fi
 Telecommunication protocols (3G/4G, GPRS/UMTS, SMS/MM)
 Bluetooth, together with mobile phones
 IEEE802.11 wireless transmission standard (ultrasonic water sensors)
 Combination of GPRS/Wi-Fi (air quality sensory system)
 Low-power WAN (recording animal activity, video transmission)
 BLE/LoRa (wildlife animal monitoring)
 2.4GHz wireless protocol (geo-cube sensors – landslide displacements)
 Satellite communications (deep-ocean tsunami measuring)
 Papers dealing with surface analysis and geo-statistics: no wired/wireless
transmissions, recording sensory measurements at the spot.
IOT DATA TRANSMISSION STANDARDS
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12. Data sources:
 Mobile phone-based crowdsourcing
 Satellite-based imagery
 Data acquired from previous projects used for recording soil fertility data.
 Offline data (governmental data repositories, topographic sheets, surveys)
 Map layers prepared in previous research works: digitized vector maps, digital
thematic maps, digital soil maps and digital elevation models.
Data types:
 Text (i.e. measurements from IoT sensors, JSON, XML and CSV)
 Images (i.e. mobile phone and fixed cameras, satellites, spectrophotometers)
 Sound (i.e. mobile phone microphones)
 Video (i.e. mobile phone cameras and fixed cameras for animal monitoring).
DATA SOURCES AND TYPES
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13.  Most authors did not provide any relevant details
 Need for trust in participatory sensing
 In crowdsourcing, sometimes the data is incomplete
 Noise map application: source of noise (e.g. train, airplane) was missing.
 Lack of complete and accurate crowdsourcing-based information
complicated rescue and recovery efforts during the Haitian earthquake.
 The geo-cubes used to monitor landslide displacements experienced loss
of observations due to a lack of sealing and not proper solar panels used
as energy sources.
 The sensors used to assess irrigation water well suitability were forced to
become sealed after sensing because exposure to air affected the
measurements.
RELIABILITY OF MEASUREMENTS
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14.  Lack of accuracy a dominant issue in interpolation/kriging scenarios
 Issues in remote sensing, due to the resolution of satellite-based imagery
 Missing data created certain percentages of errors when
averaging/interpolating.
 Uncertainty in soil heavy metal pollution assessment.
 Need to meet data quality objectives (DQOs) was stressed during the design
of an air quality platform
 Dealing with missing data was an issue towards precise flood forecasting for
the river Meuse.
ACCURACY OF MEASUREMENTS
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15.  Calibration was necessary in pollution sensors: Authors had to send the
sensors back to the manufacturer for accurately calibrating them.
 Elaborate calibration of the high-quality tsunami measuring equipment
 Another general problem with calibration is that extreme values are more
difficult to predict than mean values.
 The problem with faulty readings is that it is sometimes hard to detect, if
ground-truth information is not available to compare with.
CALIBRATION AND FAULTY READINGS
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16. Open issues of the IoT world exist also in EnviroInfo applications:
• Data Sharing and Interoperability
• Device/service Discovery
• Scope of Analysis
• Security and Privacy
• Semantics and Services/data Description
DISCUSSION
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17. Future challenges include:
 Cost of IoT equipment
 Data storage needs
 Requirement of (sometimes real-time) event processing and
computational analysis of geospatial big data
 Use of databases that natively support spatial data types
 Absence of accuracy on generalizations
 Need of standardizations
 Geo-data interoperability
 Easily combine multiple heterogeneous datasets to develop 4D models
that would enable users to view conditions over time
DISCUSSION
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18.  A review of 26 research papers in which geospatial analysis has been
employed, based on IoT deployments in environmental informatics.
 Geospatial analysis offers large potential for better understanding,
modelling and visualizing natural and artificial ecosystems, using IoT as
sensing infrastructure.
 We only scratched the surface…
Large potential in future applications:
 Geospatial mapping based on participatory sensing
 Better understanding and assessment of the impact of climate change
 Environmental sustainability
CONCLUSION
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19. THANKS FOR YOUR ATTENTION!
DR. ANDREAS KAMILARIS EMAIL: A.KAMILARIS@UTWENTE.NL