Foot Traffic analytics can identify the characteristics of transit stops and routes, helping to determine and improve the overall coverage of any transit system.
When it comes to public transport, foot traffic analytics can be of great help to authorities, who are faced with various challenges such as road congestion and the diversity of modes of transport, among others. This poses difficulties for public transport managers and operators when it comes to planning.
The secret to site selection in such a competitive market is based on the ability to analyze the right data and be able to understand and interpret the site selection strategies of nearby locations to stay ahead of their expansion plans and gain a competitive advantage.
Where? This is the fundamental question that guides any site selection decision. Tools such as location intelligence and footfall analytics enable the aggregation, analysis and visualization of spatial data and bring significant advantages to the site selection process.
Leveraging current and historical data on location movements allows urban planners to understand current challenges and build smart, flexible and efficient cities.
As more cities begin to implement smart city planning based on data science, location intelligence insights help shape policies that will benefit neighborhoods and the people who live in them.
By incorporating location intelligence into urban planning, it becomes possible to develop infrastructure adapted to the needs of citizens, enhancing living conditions in any given city. In addition, spatial data helps to optimize costs and prioritize government administration projects.
What does location intelligence provide to urban planning?
Foot traffic analytics and point-of-interest analytics help large chains supplying contractor-grade building materials and home improvement products measure footfall and understand consumer behavior patterns in any given zone of interest or point of sale.
Case Study
Foot traffic analytics: Builders Warehouse City Cape Town, South Africa
Footfall analytics and POI characterization through Big Data are being used by retail industry leaders to make smarter business decisions, by maximizing revenue and optimizing costs.
Case Study: Footfall analysis of the largest Starbucks Coffe in Bangalore India
At PREDIK Data-Driven we conducted a detailed study of Starbucks Coffe, the largest physical store in Bangalore, India. Through a footfall analysis and POI characterization of the area and people who visited that establishment, we estimated the foot traffic, identified the most and least crowded places, classified the users who visit, live or work in the area, the observations per day, the most and least visited places by the establishment’s customers as well as their relative wealth index characteristics.
By applying foot traffic analytics and location intelligence techniques it becomes possible to identify urban mobility patterns for the benefit of urban planning, transportation science, and geography.
Foot traffic analytics serve as a key factor in assessing the functionality and rationality of a city’s road network or a company’s distribution center, in terms of spatial structure and connectivity.
Mobility analysis and geomarketing techniques have become key factors in the real estate investment process.
In the process of searching and selecting areas for the acquisition of a property for real estate development, investors focus on finding those with the highest expected return on investment. This process, which until now was done using traditional financial and feasibility studies, has now become incredibly simple with mobility analytics and location intelligence based on Big Data.
The Nicaraguan government is planning the construction of roads and bridges nationwide.
CentralAmericaData's "Commercial Feasibility Studies" provides an up-to-date list of public and private construction projects that have submitted Environmental Impact Assessments (EIA) to the respective institutions in each country.
Retailers are already implementing Big Data tools such as location intelligence and foot traffic analytics to understand consumer mobility patterns, measure foot traffic at each store, understand the performance of their outlets, and estimate competitor turnover.
In order to research and evaluate real estate investment opportunities for commercial, industrial or hospitality use, it is important to consider all social and economic factors in a given area in order to make an informed investment.
Researching and evaluating real estate investment opportunities is not a piece of cake. Whether it is the valuation of a retail or hospitality investment space, it is crucial to take into account all the socio-economic factors of the area in question to ensure a high return.
During 2020, the main buyer of ceramic products was Guatemala with $83.2 million, in this year the purchases mainly came from China with a value of $75.4 million.
Figures from the Trade Intelligence Unit of CentralAmericaData: [GRAFICA caption="Click to interact with the graphic"]
Analytics based on Big Data allows mall operators to maximize revenues and visits by better selecting tenants, optimizing mall design, determining rents, establishing signage and advertising campaigns, etc.
New technological tools allow mall operators to measure the number of consumers spending in and out of stores, the time they spend in and out of stores, know their relative wealth index and understand visitor behavior patterns, helping to determine the best mix of stores, site infrastructure, rent price range and implement more efficient signage and advertising.
Location intelligence and POI characterization through Big Data are increasingly being used to make business decisions in the retail, real estate, logistics, and port sectors, among others.
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