If you use Google or Outlook calendars, you heat map your own schedule.
When viewed from afar, those tiny meeting dots compile a map that showcases your daily, weekly, and monthly involvement. All you have to do is look at the densest areas to understand how to better manage your time, remove time-wasters, and increase productivity.
Personal calendars are very simple heat maps. Another example is Excel. The office’s least fun but most helpful application is capable of heat mapping your budget and letting you know unpleasant truths about your spending habits in stunningly beautiful ways.
Heat maps have been long used for statistical purposes. But with the big data inundating businesses and communities, we need to look at new ways how to make the surge of information work for us, instead of against us. Heat map analysis is one way to do that.
A Simple Way to Define Heat Maps
Heat mapping is a visual representation of two-dimensional data where values are presented by colors or other graphically distinctive qualities. There are also 3D heat maps (yes, you may have seen them in Excel), which represent the relationships between three variables, but these are less common.
A heat map provides a clear visual summary, one you wouldn’t be able to get by simply staring at a bland spreadsheet or endless rows of numbers. It is no wonder this tool has found many practical applications.
Although a heat map is a statistical tool capable of visually presenting complex data in simple ways, we see examples of heat mapping daily. In news weather forecasts, forecasters “map heat” literally by marking regions with lower and higher temperatures. Election results are reported with heat maps. Business intelligence, website analytics, and retail marketing software apply heat mapping to track and measure business metrics, KPIs, website performance, and consumer behavior. UX designers create heat maps to test diverse designs and gather insights about the user’s interaction with a product and identify well-performing website areas.
Heat Map Application in Engineering
Did you know that there is a way to lower your energy bill by running a personal energy savings calendar?
Probably yes, but this is where things get interesting industry-wise. In engineering design, heat maps turn into something more than just small-scale, red, green, and yellow colored graphs intended for personal help. Of course, for industry purposes, there is always some piece of software that measures and analyzes data collected either digitally or by hardware. Doing it manually is ineffective and inapplicable in large regions, such as a whole city or a country.
While collecting data from website clicks and enterprise performance tools is primarily the focus of marketing professionals, engineers are more interested in how to improve and deploy devices that collect field, “real-life” data.
Civil Engineering, Road Management, and Landscaping
Data collected in this way is invaluable for civil planning, landscaping, and road management. Construction engineers can use the predictive abilities of heat maps in retrocommissioning, to compare the energy consumption between two buildings and design energy-efficient housing and work facilities with minimal energy losses.
For instance, you can get tons of free data represented in heat maps about England and Wales’ important statistics, including accident blackspots and housing market prices.
Source: https://www.plumplot.co.uk/goodies.html (labeled: free to use with modification)
Road engineers can apply the lessons learned to design safer roads. Landscaping can get a greener dimension by analyzing heat maps of congested cities, which are growing in numbers as we are moving to smarter urban grids. New York City, for example, uses heat map analysis to track pollution.
There are different ways to collect field data. Smart sensors that detect and measure humidity, proximity, temperature, motion, gas, and other chemicals can deliver quantifiable data which can be visually represented with heat maps. Ideally, in the cities of the future, we would be seeing complete machine-to-machine (M2M) systems, which perfectly manage urban energy and transportation grids without human interference. But let’s not get ahead of ourselves and stick to what is possible now.
District heating, created by a centralized power location distributing energy through insulated pipes as a combined heat and power (CHP) system, is widely used in European cities. Notable examples include Sheffield, Stockholm, and Vienna.
Sheffield is already working to potentially benefit from heat mapping analysis by lowering carbon emissions even further. Its heat maps helped the city authorities locate heat sinks and heat sources, as well as use heat load calculations to implement the findings into a better performing district heating grid, as well as to integrate emerging heat sources.
Denmark is a few steps forward, already working on 4th generation district heating (4GDH), which includes smart gas, electricity, and thermal grids that could potentially be implemented into sustainable energy systems.
Exciting New Heatmap Devices
Although smart city grids do pose certain challenges, which we are not yet able to fully meet (think of the balance between the power used to supply countless smart sensors versus the potentially lower overall power usage), there have been many promising developments that speak in favor of using heat map analysis.
An experiment conducted by Google in 2007 opened the doors to using cameras for capturing street data for further analysis. Google put rooftop cameras on cars and let them collect drive-by street-level images. A similar process has been repeated by the MIT startup Essess. Essess uses thermal-imaging rooftop cameras which collect drive-by images from residential and business buildings. The purpose of the project is to find fixable energy leaks in windows, doors, walls, and foundations—and help residents tackle energy losses. Long-wave radiometric and near-infrared cameras are placed in a backpack-sized rig on car rooftops to capture heat signatures. To differentiate facades from the environment, Essess uses a LiDAR system which is capable of precisely capturing 3D images.
In view of the heat map analytics, MIT’s cameras are similar to the technology applied in-store, where heatmap cameras collect data such as footfall, consumer age, gender, and mood to personalize offerings. Apart from thermal imaging, retail heat map devices include alternate technologies, such as fish-eye lenses to capture precise panoramic images, face recognition, and WiFi trackers.
Heat map analysis is used in VR video analytics software to see what people really like when immersed in the limitless free field of virtual reality. Collecting data from the inside of the video, this tool analyzes behavioral patterns to be able to guide the user’s attention. But—hold your water—eye-tracking software is able to detect small movements, fixations, and gazes and represent them numerically via heat maps. Obviously, heat maps work well in macro and micro environments, as well as in the physical and the digital world.
The multiple ways of collecting data from the environment can be overwhelming. Consequently, heat mapping will grow in importance, simply because it is an effective, user-friendly way to find patterns and detect changes in huge data loads. Whether you’re designing the sensors that will be inevitably used to inform these heat maps or digging into how best to display the data your prototypes are gathering, heat maps are an invaluable tool that all engineers should keep their eye on.