LOcality analytiCs for urbAn Living ("LOCAL")

点击翻译按钮获取中文摘要

"In 2016 \>1.2million UK residential property transactions took place, averaging £225,956\. For most people, property is the most expensive purchase they ever make, and as a long-term investment, requires extensive research to ensure the right choice is made.Top among factors to research is ""location, location, location,"" with properties boasting desirable location attributes, such as proximity to green space commanding a premium. While real estate vendors have made some effort to incorporate basic location data into their service offerings, e.g. school performance reports, the range (usually no more than 3-4 variables), depth (typically static, and single dimension) and quality (often out of date, and only partial) are limited, and the user experience remains fragmented and clunky (e.g. requiring separate views for each variable or even in some instances, simply suggesting which 3rd party sites users may wish to explore). To determine a property's local environment and factors conditioning future life quality, e.g. air quality, noise pollution, connectivity, etc, the onus remains on the prospective buyer to do their own research. Lacking expertise in data analytics, or even familiarity with what data is available and where, this often results in long, frustrating, expensive, and sometimes unsuccessful searches. By failing to seize an opportunity to provide their customers with additional value, real estates agents are missing out on a means of extending their service offering, boosting customer engagement and differentiating themselves, while end users are unable to fully factor the quality of a local environment into their property-purchasing decision-making. Without knowing a location's true quality of living, including local public service provisions, the value ascribed to these services is not adequately captured in property prices and people remain unaware of services on their doorstep.This 6 month LOCAL feasibility study will assess the potential to exploit the deluge of new geo-referenced and time-coded data being generated from various sources to create meaningful, multi-dimension analytics on the urban environment that can be sold on a data-as-a-service basis to facilitate a vastly enhanced home-buying user experience.Led by UK start-up cartographiX, experts in geolocation analytics and machine learning, project activities include supplier engagement/appraisal, systems architecture research, and end user proposition validation and refinement, culminating in a refined business plan."