{"id":9341,"date":"2017-08-23T09:25:03","date_gmt":"2017-08-23T16:25:03","guid":{"rendered":"https:\/\/www.esri.com\/about\/newsroom\/?post_type=wherenext&p=9341"},"modified":"2023-12-04T12:10:06","modified_gmt":"2023-12-04T20:10:06","slug":"new-forecast-human-weather","status":"publish","type":"wherenext","link":"https:\/\/www.esri.com\/about\/newsroom\/publications\/wherenext\/new-forecast-human-weather","title":{"rendered":"A New Forecast: Human Weather"},"author":721,"featured_media":9321,"parent":0,"menu_order":0,"template":"","format":"standard","meta":{"_acf_changed":false,"sync_status":"","episode_type":"","audio_file":"","castos_file_data":"","podmotor_file_id":"","cover_image":"","cover_image_id":"","duration":"","filesize":"","filesize_raw":"","date_recorded":"","explicit":"","block":"","itunes_episode_number":"","itunes_title":"","itunes_season_number":"","itunes_episode_type":"","_links_to":"","_links_to_target":""},"categories":[],"tags":[],"department":[476822],"wherenext-category":[],"industry":[],"class_list":["post-9341","wherenext","type-wherenext","status-publish","format-standard","has-post-thumbnail","hentry","department-data-and-ai"],"acf":{"short_description":"The weather above us isn\u2019t the only forecast businesses need to tune in to these days.","pdf":{"host_remotely":false,"file":"","file_url":""},"flexible_content":[{"acf_fc_layout":"content","content":"While we have long been accustomed to tracking the dynamic data that determines the weather, we now find ourselves at the dawn of a new era of forecasting. Call it human weather.<\/em> In my work with some of the largest and most innovative companies in the Bay Area and beyond, I\u2019ve seen the tremendous promise that human weather holds.\r\n\r\n\u201cI move; therefore I am,\u201d observed Haruki Murakami, the renowned Japanese writer. Organizations intuitively understand the value they can gain from knowing more about the movements of established and potential customers, but most are simply unaware of how much insight into these movements is now available.\r\n\r\nToday, Internet of Things (IoT)-enabled devices have created legions of human data sources, whose signals can be understood in much the same way that we understand traditional weather data. Just as businesses benefit from traditional weather information\u2014by routing supplies around natural disasters, positioning field crews to mend damaged infrastructure, and even predicting how a heat wave might affect sales\u2014a broad range of business activities can benefit from data on human weather."},{"acf_fc_layout":"quote","image":"","text":"Companies are gaining competitive advantage from the data exhaust generated by mobile applications that use location awareness. ","author_name":"","author_profession_organization":""},{"acf_fc_layout":"content","content":"The term human weather<\/em> was coined by Myles Sutherland, founder of consulting firm One Degree North and former head of Esri\u2019s startup partner program. It refers to a real-time (or near real-time), high-volume data stream about human activity. It is principally about location\u2014how and where people move\u2014 but also encompasses behavioral components. Imagine being able to track the patterns and impact of human activity like a meteorologist tracks weather fronts. The technology to do this exists, and pioneering companies are beginning to use it to observe, document, and predict consumer behavior, even as they take steps to ensure the privacy of individuals.\r\n\r\n \r\n\r\nEarly Weather Watchers\u00a0 <\/strong>\r\n\r\nIf backyard and mountaintop weather stations are the sources of data for our seven-day forecasts, where do we find the data to understand human weather? The three principal sources of insight are mobile devices, financial transactions, and vehicle telematics.\r\n\r\nFor example, companies are learning much from the location exhaust generated by mobile applications that use location awareness. Social media, ridesharing, navigation, and dating apps know the location of users that have opted in to this kind of sharing. Third-party providers aggregate and anonymize that information, then serve it out as geographic, or spatial, data so companies can do things like call up storefront locations and see what retail traffic looks like at their own locations and those of competitors, comparing today\u2019s traffic to the previous week\u2019s or last month\u2019s.\r\n\r\nKnowing the ebb and flow of traffic can tell a company that an upstart competitor is beginning to gain traction with passersby, or that its own out-of-home advertising isn\u2019t drawing as much traffic as hoped. Airports are even beginning to adjust the cost of retail space based on the flow of foot traffic through the terminals. High-traffic areas can command premium rents.\r\n\r\nIn this way, the data generated through our smart devices\u2014that is, the location exhaust of our mobile applications\u2014becomes a valuable resource for business insight."},{"acf_fc_layout":"image","image":9301,"image_position":"left","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"On the transaction side, credit card providers and financial data aggregators are also using human weather techniques. Consider a person who uses multiple credit cards. While the data from the card transactions is anonymized, the associations between the accounts are still visible, so a marketer can use that data to see the full customer journey\u2014the latitude-longitude of each transaction across a period of time. Understanding purchases across product categories, services, and experiences can help companies tailor messages to the right buyers and increase marketing ROI.\r\n\r\nIn the realm of vehicle telematics and traffic modeling, community-based traffic and navigation applications like Waze and INRIX are a prime source of insight into human weather. They collect data on people maneuvering from point A to point B, and the most prominent apps have millions of users, providing insight into human movement on a massive scale.\r\n\r\nCities, for instance, are sharing information\u2014roadwork plans on a major bridge, or street shutdowns due to an upcoming marathon\u2014with these application providers. The applications\u2019 algorithms account for these events and effectively route people around them. The application providers love that data because it enriches and improves their applications; in return, the cities learn in real time about conditions in their area\u2014cars on the shoulder of the road, road debris, and more. Dispatchers at the cities\u2019 departments of transportation can visualize that insight on maps served by geographic information system (GIS)<\/a> technology, and understand spatial patterns and movement within the municipality, deploying responders accordingly."},{"acf_fc_layout":"sidebar","layout":"standard","image_reference":null,"image_reference_figure":"","spotlight_image":null,"section_title":"","spotlight_name":"","position":"Right","content":"

Big Data and Human Weather<\/h2>\r\nAs advanced analytics teams look for insight in human weather data, one of the most significant challenges they\u2019re facing is volume. With billions of daily observations, the scale of human weather is much larger than most datasets.\r\n\r\nTo cope, some companies are inverting a tried-and-true analytical technique: They\u2019re bringing computing power to big data, instead of incurring the expense of moving data to the processing capacity. Advanced GIS technology and other IT tools are helping to facilitate this shift.","snippet":""},{"acf_fc_layout":"content","content":"Companies and municipalities are experimenting with human weather initiatives with increasing frequency. In these early days, it\u2019s helpful to think of these experiments on a maturity curve. On the left side of the curve, organizations are comparing human weather data to their own data, looking for correlations. On the right side of the curve, companies are using this data for predictive intelligence, anticipating with far greater accuracy things like how many people will visit their store next weekend, how sales will rise during a holiday weekend, and which competitors are beginning to gain customers.\r\n\r\nThe concept is a powerful one, and we can expect it to be embraced across a broad range of commercial and government sectors. Three have emerged as early adopters:\r\n
    \r\n \t
  • Retail site selection and market planning\r\n<\/strong>Retail companies have been among the first in the commercial sector to adopt human weather techniques. They use the data to make better real estate decisions and to market products and services with a more personalized touch.<\/li>\r\n \t
  • Civic development\r\n<\/strong>Human weather is being used to build smart cities by making better transportation decisions (where to locate transit stops or route new transit lines) and recreation choices (where to build parks and facilities based on human movement), and by conducting better event planning and real-time management.<\/li>\r\n \t
  • Public safety\r\n<\/strong>Police departments and other agencies are interested in human movement on the macro and micro levels (i.e., activity across an entire city or on a specific block). They\u2019re also parsing transactional data to assess risk and allocate public safety resources based on where people are, where they go, and how they move through municipalities.<\/li>\r\n<\/ul>\r\n\u00a0<\/strong>\r\n\r\nGaining Optimal Insight from Human Weather<\/strong>\r\n\r\nThe Internet of Things is the connective fiber in an increasingly connected commercial and industrial ecosystem, offering real-time insight throughout the economy. Consider the billions of sensors embedded in airplanes, manufacturing equipment, roadways, office buildings, and everywhere in between, and the rich stream of information they provide. In much the same way, the IoT is the sensory system behind human weather, in that technology has moved to the center of our lives, essentially creating human sensors.\r\n\r\nThe cell phones, fitness trackers, and other IoT-connected devices we carry around produce a profound variety and volume of data about our behavior\u2014how we bank, work, shop, work out, and more\u2014all tied to a specific place and time.\r\n\r\nAt a recent conference on the development of smart communities, Stephen Goldsmith, professor at Harvard\u2019s John F. Kennedy School of Government and former mayor of Indianapolis, made the following observation: \u201cData is everywhere and can be collected from everywhere, including from individuals walking around with cell phones. With this massive amount of data, we need context; and GIS is an organizing context for data.\u201d\r\n\r\nA geographic information system leverages maps to help organizations understand the spatial associations within this massive collection of data. That might mean plotting out the longitude and latitude of where people are when they request a mobile ad, or swipe a credit card, or move between locations. Companies can add layers to that information, just as one might place an acetate layer over a map, so that more detailed insight can be gained as data sets are combined. This technique enables new kinds of observations, ranging from seeing things like traffic hot spots or cold spots, to anomalies that simple charts don\u2019t uncover.\r\n\r\nIn the retail sector, for instance, a company looking to chart human weather might draw a boundary around its store, then define a rule that if a device dwells there for a predefined period, some action results. Marketers could send a push notification about a time-sensitive offer, or a back-end analytic system could register the visit and add a benefit to the customer\u2019s loyalty program account."},{"acf_fc_layout":"quote","image":"","text":"Just as weather data is being embedded in business logic, I believe we will see human weather data enabled in products and services to provide a standard way of making decisions.","author_name":"","author_profession_organization":""},{"acf_fc_layout":"content","content":"In addition to taking actions based on individual movements, the retailer might roll up the data to create what amounts to a local forecast. The patterns of human weather visible in the data can aid in predicting store traffic and sales, allowing the retailer to adjust staff and stock the store accordingly.\r\n\r\nA bank can analyze another form of human weather\u2014origin\/destination data\u2014to determine the right location for a new branch. Origin\/destination data shows the home zip code connected to purchases in various sections of the city, revealing where people travel to eat, drop off dry-cleaning, and more.\r\n\r\nWith modern GIS technology, bank planners can see the demographic and psychographic characteristics of those zip codes, cross-reference those with a targeted customer profile, and choose a branch location that serves the highest concentration of those ideal customers.\r\n\r\nThese are two cases among many. As the benefits of understanding human weather grow, companies in many industries will find ways to augment data, workflows, and products or services with human weather information.\r\n\r\n \r\n\r\nBroadening the Outlook<\/strong>\r\n\r\nThis information simply wasn\u2019t available in years past; but now that we\u2019re all carrying around little computers in our pockets, our weather patterns are becoming clear. What are the implications?\r\n\r\nIn the same way that executives have questioned the value of their traditional databases, they now must assess the worth of human weather data. Some will worry that another data stream will add to the noise within the company. But forward-thinking executives are already reaping benefits from the above scenarios and other uses of human weather, As a recent Forbes<\/em> article noted, \u201cthe McKinsey Global Institute indicate[s] that data-driven organizations are 23 times more likely to acquire customers, six times as likely to retain those customers, and 19 times as likely to be profitable as a result.\u201d\r\n\r\nI believe we will see organizations begin to build these dynamic datasets into their products and services to provide a standard way of making decisions. At first, they may not push these decisions to an individual using an application. Instead, the application itself will make decisions based on human weather conditions.\r\n\r\nOne example might be a ride-sharing app that automatically routes drivers to the vicinity of a concert venue because it receives an input indicating that a large number of mobile devices are in use at the event. Similarly, a manufacturer\u2019s supply chain management system might automatically re-allocate products to a local distribution center based on a spike in purchases at nearby stores.\r\n\r\nThis is how human weather will begin to permeate the business world.\r\n\r\nTo make better business decisions, executives must leverage not just the data they manage in-house but also additional sources of insight into human weather, including from the sensors around them\u2014cell phone movements, point-of-sale transactions, even computer-vision within cameras."}],"references":null},"yoast_head":"\nIoT-Connected Sensors Create a New Kind of Forecast: Human Weather<\/title>\n<meta name=\"description\" content=\"Connected devices have turned billions of people into a valuable stream of movement and behavior data, and companies are waking up to the business value of that insight.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" 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He works with tech companies to explore the use of location data in their products and apps. His passion is the intersection of the physical world and the digital world, including mobile applications, location services, and mobile payments. A pioneer in mobile social networks, Jim founded location-based Jambo Networks before joining Esri. He earned a master\u2019s degree in GIS and remote sensing from Cambridge University and holds a bachelor's degree in history and economics from Southern Methodist University.","url":"https:\/\/www.esri.com\/about\/newsroom\/author\/jyoung"}]}},"_links":{"self":[{"href":"https:\/\/www.esri.com\/about\/newsroom\/wp-json\/wp\/v2\/wherenext\/9341","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.esri.com\/about\/newsroom\/wp-json\/wp\/v2\/wherenext"}],"about":[{"href":"https:\/\/www.esri.com\/about\/newsroom\/wp-json\/wp\/v2\/types\/wherenext"}],"author":[{"embeddable":true,"href":"https:\/\/www.esri.com\/about\/newsroom\/wp-json\/wp\/v2\/users\/721"}],"version-history":[{"count":0,"href":"https:\/\/www.esri.com\/about\/newsroom\/wp-json\/wp\/v2\/wherenext\/9341\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.esri.com\/about\/newsroom\/wp-json\/wp\/v2\/media\/9321"}],"wp:attachment":[{"href":"https:\/\/www.esri.com\/about\/newsroom\/wp-json\/wp\/v2\/media?parent=9341"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.esri.com\/about\/newsroom\/wp-json\/wp\/v2\/categories?post=9341"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.esri.com\/about\/newsroom\/wp-json\/wp\/v2\/tags?post=9341"},{"taxonomy":"department","embeddable":true,"href":"https:\/\/www.esri.com\/about\/newsroom\/wp-json\/wp\/v2\/department?post=9341"},{"taxonomy":"wherenext-category","embeddable":true,"href":"https:\/\/www.esri.com\/about\/newsroom\/wp-json\/wp\/v2\/wherenext-category?post=9341"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/www.esri.com\/about\/newsroom\/wp-json\/wp\/v2\/industry?post=9341"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}