Smart Cities rely on external data: proprietary, public, and often open-source. In addition to general data categories like the weather and the census, smart cities use data from a complex network of sensors and devices that provide highly localized information on movement, utilities, and more. CCTV and acoustic sensors, for example, identify criminal behavior. Meanwhile, underground sensors placed beneath parking lots provide information on parking availability. IoT device-equipped cars communicate with each other about their location, acceleration, and more to reduce the chance of accidents. The examples go on.
Smart cities need machine learning programs to function: firstly, because no human can analyze the sheer amount of data (even de-centralized)—especially not in time to resolve emerging problems. Secondly, these programs identify patterns—of criminal activity, of electricity usage, etc.—which city planners can use to improve that aspect of life.
Data Alliance provides crowd-sourced block-chain IoT data for governments, municipalities, and others. In less than a year since its inception, it became a Gold Member of the Open Connectivity Foundation.
Commscope offers a wide range of sensors (fiber, cellular, WiFi, converged network management, etc.) to run a smart city. They also offer the services of consultants, local distributors, and other professionals who can help set up and manage the smart city ecosystem.
Siemens Smart Infrastructure focuses on providing industrial services to smart cities. In particular, they provide electricity and major smart power grid system services.
PipeCandy eCommerce Leads & Insights for Fulfillment tracks company data and company shipping details: shipping volume, which companies they use to ship products, whether they ship internationally or not, and so on. You can find company leads easily with this dataset with filter capabilities.
Wikiroutes Transit Data provides public transport information—routes, stop points, and more—via crowd-sourcing. The data is constantly updated and can be easily converted and integrated into your own software system.
Wikiroute’s Transit Data is used by individuals, private companies, and government agencies of all types and sizes.
TrackStar’s Predictive Credit Technology uses fifteen years of financial dispute data to create predictive models of future borrowing potential. With this data and AI technology, your bank or other lending company can mitigate the risk of fraud, improve existing customer relations, and reduce your operating costs.