How GIS Makes IoT Data Come Alive
John Koon posted on November 14, 2019 |
IoT and GIS will make cities smarter with useful data.
IoT and GIS will make cities smarter with useful data.

The pairing of a geographic information system (GIS), an established geospatial technology, and Internet of Things (IoT), an emerging technology, is like getting Tony Bennett together with a new rising star to do a Duets III album—seeming unlikely at first, but once they get together, sparks fly.

The IoT consists of sensors that collect different types of data, connectivity (networking) between the sensors, one or more means of data storage, algorithms to analyze that data to reach decisions, and a user interface. The IoT is already used in a range of applications, such as consumer electronics, aerospace and healthcare. IoT continues to grow, branching out into smart cities and grids, autonomous vehicles, retail, factory automation, logistics and supply chain, oil and gas exploration, and farming.

IoT devices can be installed on stationary or mobile assets, such as buildings or cars, to collect information about the operation of the machine or the surrounding environment. While the IoT is useful in collecting and analyzing data from each location or asset continuously, it cannot convey the big picture.

On the other hand, a GIS is designed to capture, store, manipulate, analyze, manage and present spatial or geographic data. Primarily used in urban planning, municipal management and asset management, a GIS can also be employed to study land use and the location of farms, towns and forests with maps and aerial photographs. In other words, GIS is very good at visualizing data and telling a story, but it does not inform on the fluidity of the situation or the granular details.

IoT and GIS Working Together

GIS and IoT are almost like the missing half each of them has been looking for. By combining GIS and IoT, stakeholders can get a complete and granular picture of individual assets or locations as well as of all assets as a whole. Smart cities, facilities management, transit development, disaster response and grid management are among the many applications that can benefit when GIS and IoT join forces. IoT and GIS working together can yield many benefits. Here are a few case studies to illustrate how cities and buildings can become smarter with GIS and IoT, and how disaster management can benefit as well.

Case Study: Smart Cities

To create smarter cities, we need more data to make smarter planning decisions. IoT sensors installed on cars, roads, highways and buildings can collect lots of real-time information for analytics. This way, city planners can be alerted to the occurrence and location of emergency events, such as fire, flooding or disease outbreaks at a city-wide scale. This data also can help government agencies monitor and improve air and noise pollution levels. In addition, city planners can develop a heat map of cycling routes for better planning. With the data, a city planner can, for example, plant more trees in cities with hot spots. Analytics can shed light on urban traffic and help optimize public transportation routes. Having up-to-date information on lane closures or road construction helps planners design and maintain roads.

Hundreds of thousands of water lines break every year in the U.S., costing close to $3 billion annually. Municipal governments can achieve significant savings by monitoring water lines and dealing with breaks as soon as possible.

For example, in Florida, the sprawling Miami-Dade County parks system was losing millions of dollars and more than 300 million gallons of water in its aging water infrastructure. With a solution that interfaces with sensors and intelligent meters, county employees can now remotely monitor water consumption and detect leaks via smart devices, laptops and office computers. As a result, the parks department estimates a 20 percent reduction in water use annually with a cost reduction of $860,000 per year.

Miami-Dade county in Florida used supervisory control and data acquisition (SCADA) sensors along with Microsoft’s Azure IoT platform to address the problems with its water infrastructure. (Image courtesy of Microsoft.)
Miami-Dade county in Florida used supervisory control and data acquisition (SCADA) sensors along with Microsoft’s Azure IoT platform to address the problems with its water infrastructure. (Image courtesy of Microsoft.)

Another example is the Canadian town of Olds, which was in a water supply crisis because it was losing close to 40 percent of its water supply to leaking pipes. The city deployed a system of strategically placed acoustic leakage detection sensors in service pipes. By analyzing sound pattern data from the pipes with an algorithm, the city was able to quickly detect new or existing leaks automatically. As a result, by reducing water loss, the city was able to save money as well as increase capacity.

Case Study: Smart Buildings

With IoT sensors installed on the exterior of buildings, managers can analyze the amount of sunlight received as well as the wind speed on the building’s faces. With IoT sensors installed inside buildings, managers can monitor the temperature and relative humidity, lighting, level of noise and traffic in the building. With this data, they can project the level of energy consumption during different times of the day and devise strategies to optimize energy use.

Using this strategy, planners can make old buildings more energy-efficient. For example, Tokyo is using such data to renovate old buildings with modern isolation, lighting, cooling and heating technologies to reduce energy consumption by as much as 20 percent. Furthermore, the municipal government is relying on thermal environmental data to create microclimates, such as breeze corridors between buildings, so residents can decrease energy use inside the buildings.

Lastly, IoT and GIS can be used to design evacuation systems that enable building residents to evacuate quickly.

Case Study: Natural Disasters

Municipalities can use GIS and IoT data to simulate and analyze the potential impact of natural disasters, such as flooding, on cities. For example, river line and coastal flood simulations can help city planners determine the preliminary flood risk assessments in urban floodplains and coastal areas so they can make appropriate design decisions.

In response to the flooding caused by Hurricane Floyd in 1999, the state of North Carolina established the North Carolina Floodplain Mapping Program to better identify, communicate and manage risks from flood hazards. The State Emergency Operations Center has since used this program during storms to monitor flooding conditions, assess potential impacts of flooding based on weather forecasts, and target the deployment of emergency response personnel and resources.

The North Carolina Flood Inundation Mapping Network (FIMAN) provides residents with real-time data on stream elevation, rainfall and weather conditions. (Image courtesy of FIMAN.)
The North Carolina Flood Inundation Mapping Network (FIMAN) provides residents with real-time data on stream elevation, rainfall and weather conditions. (Image courtesy of FIMAN.)

Future Actions

There are challenges to overcome in combining IoT and GIS. While combining IoT and GIS offers significant promise, there are also significant challenges, such as hardware ruggedization, data integration, data storage and data security.

Because the IoT sensors will be installed in the field to collect data, they need to be ruggedized to withstand extreme temperatures, significant temperature swings, and shock and vibration. Data integration will also be an issue since IoT datasets come in different formats, such as texts, images, sounds and videos. Data storage also presents several unique challenges. First, much of GIS data is aerial photographs, which are large files. Therefore, storage capacity has to keep pace with the amount of data acquired. Second, while storing geospatial data is feasible in the desktop environment, it is preferable, albeit challenging, to store data in the cloud.

Lastly, datasets accumulated in IoT devices are already quite attractive to hackers. Layering GIS data on top of those datasets creates an even more tempting target. Therefore, maintaining data security and privacy will be a critical piece of the puzzle.


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