What Is a Digital Twin in City Planning
by Marge
Visitor Question: I am on our city's planning commission. I don't understand exactly what a digital twin is, but I know that our planning staff is very excited about the prospect of having one. To me it seems a little bit like a big waste of information technology money that our city doesn't have, but it could be that I am just not understanding the possibilities. So please explain what this means, and why would it have utility in the quest for better city planning?
Editors Reply: We certainly understand why you are having trouble comprehending what the term digital twin means and what it might mean for your community development. But you are far ahead of some other readers, so let us set the groundwork first.
A digital twin of a city is simply a virtual model of it, a digital representation of a physical thing, in this case a whole city. The idea has been used in engineering and design for maybe a couple decades, but it’s certainly a recent concept in the planning and management of cities.
As a planning commissioner, you probably have a passing familiarity with GIS, geographic information systems. That is a computerized map-making capabilities, for those who are stumped by that acronym too. In GIS, maps are made in layers, with the ability to “see” or “turn off” each layer to accent particular characteristics of the map and to add characteristics as data becomes available. In the days before ubiquitous computing power, the overlays were done by hand with a transparent mylar material. This enabled planners and residents to gain new insights as they understood how their city’s base street map, for instance, interacted with some other characteristic, such as maybe zoning, air pollution intensity, a high concentration of children or the elderly, or anything else that could be mapped.
Think of the digital twin as also being composed of layers of information, or nowadays we would say data. At its most basic, cities have data about the exact location of their streets, they have topographic data about the terrain, and they have building permit data that gives them the location, width, height, depth, and shape of buildings. They know where parks, vacant lots, and cemeteries are located. Those might be among the most elementary types of data that the city stores in various ways.
Typically the city has a really good handle on its infrastructure too, even if it is owned by private utilities and other companies instead of by the city itself. Not only streets, but also electric lines, gas lines, broadband, cable TV lines, pipelines, transformers, and more, are currently depicted on maps that the city has in its possession.
The city also will have some data about the movement of vehicles within the city. They have at least sporadic traffic counts, but it’s even more likely now that the city may have some real-time traffic flow data either in primitive video form or flow data from sensors in the streets or at traffic signals. This data will be accumulated by time of day, time of the week, season of the year, and so forth.
The city also may have available to it some data about the mobility of people on foot if it has been able to tap into cell phone location data or GPS systems in vehicles, as some cities around the world have done.
Information on the quantity and quality of water flows is available sometimes, and certainly information on crime, police calls, and the consumption of electricity, natural gas, and heating oil is available. With some work, the city can obtain information on school attendance, voting behavior, solid waste generation, and spread of communicable diseases.
As IoT (Internet of Things) capabilities increase, the amount of data will multiply. IoT is the same kind of sensor-based capability that lets your neighbor sit in her office and still find out from her new refrigerator whether she needs milk or not. IoT devices already are present on utility and traffic light poles in many cities, and these will enable a constant flow of data on traffic, for example, instead of the traffic count numbers that a city might be able to gather by placing a tape connected to a counter box on your street every few years.
All of these items and more can be combined into one working model of your city, but this model will be digital and not made out of cardboard like those old cardboard models of downtown that you may have seen. This digital model can be considered the digital twin.
Obviously the digital twin will never be perfect because the data will still be poor in many instances. If your city measures the movement of people by obtaining collective GPS data from cell phones, the people who don’t have cell phones, don’t turn them on, or don’t allow their locations to be known will not be represented, for instances. Maps of all kinds may include mistakes, and the city may not record your real estate tax payment the same day you made it.
However, the advantage of the digital twin is the same advantage that models of various aspects of cities have always had, which is the ability to run scenarios to predict the likely result of a particular physical change, development, redevelopment, or policy intervention. Again, we have to caution you that scenario planning has always been a luxury of the best-funded planning departments, usually in the largest cities. It is the constant flow of new data into digital twins though that offers promise.
To assess whether your city can afford to spend money to create a digital twin, you would have to figure out what new data would need to be collected to run the scenarios the city wants to run. If funding for collection of new data is insufficient, the city would need to take into account whether the current methods of collecting and analyzing data across many different “layers” (aspects of the city) is working well. Does it allow prediction of the consequences of changes before expensive experiments are made? Can you predict what will happen if all one-way streets are converted to two-way streets using your existing methods? Are those current methods both effective and cost-efficient, or could the smart city digital twin (SCDT, it is already being called) improve the prediction capability and help avoid costly mistakes?
Based on experience with the leaps forward in planning-related technologies over the years, we have to suspect that the digital twin will need quite a bit of parenting in the form of staff time. The same could have been said about GIS too, but by now most planning departments would be extremely reluctant to go back to the old manual methods of drafting maps.
A key element in the city’s decision probably should be a consideration of how advanced the city’s efforts in collecting data from sensors or “smart” devices, vehicles, and cell phones has become. If there has been no investment in these so-called smart city innovations, it is probably best to make a plan for their deployment by the city and large private utilities and entities within the city. When there is robust new data collection, the time will be ripe for considering whether current GIS capabilities can be expanded in the direction of a digital twin of your city.
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