POI analytics in Luxury Shopping Malls
Thursday, October 14, 2021
The POI characterization through Big Data has become more frequent as it allows the implementation of strategies and solutions within the business sectors.
ty of solutions based on location intelligence and zone characterization rely on efficient POI
analysis, which benefits any type of business.
Mapping platforms and social networking applications host their own geographic datasets, resulting in a rich source of data with which to obtain all the necessary characteristics and insights from any location where a more detailed picture of a POI's market, inhabitants, or potential spending is required.
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Thanks to these analyses, businesses can learn about coverage, visitor density
, market classification
, brand positioning
and the spatial relationship between POIs in different cities, states, or countries.
In PREDIK Data-Driven
we conducted a detailed study where we characterized a commercial area and its environment, called Plaza Fiesta
in the municipality of San Pedro Garza Garcia
, in the city of Monterrey, Nuevo Leon, Mexico
, during February, 2021.
Through an analysis and characterization of the point of interest
based on the records that denote mobility within the environment of a POI, we estimate the specific locations where the highest amount of pedestrian traffic is found, classify pedestrians as residents, workers or visitors, count the observations per day, collect socio-demographic aspects and categorize the most visited places in the city, in order to obtain and know the distribution of visits that are generated in various areas of interest.
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The objective of this case study is to identify the behavioral patterns of the people who visit this plaza, the number of visits, their socioeconomic status, and to categorize and identify in which other areas of the city of Monterrey
the visits are distributed. This analysis aims to answer the following questions:What areas and locations have the highest concentration and pedestrian traffic.
Generating a heat map that covers a radius outside and inside the POI analyzed, we identified that around Plaza Fiesta, the areas with the most pedestrian traffic
are in the Del Valle neighborhood, Colinas de San Agustin, Valle Oriente and Zona Loma Larga Poniente.
More specific locations such as Plaza Fiesta, the English schools of Monterrey, Global School of Monterrey, and part of the Club Campestre are the locations that denote a more prominent concentration within the POI analyzed.
Something to consider in our study is that a series of calculations were made, where it was possible to omit vehicles from the analysis, which helped to generate more detailed and focused information on the consumers and market in the area.What is the estimated evolution of visits to Plaza Fiesta?With these analyses we were able to make a comparison of the evolution of visits identified during hours, months, days and years of a POI, generating a detail of the average number of visits that helps businesses to have an idea of the market potential and customers in the area, identifying seasons of high and low mobility.The distribution by hour, day, month and year is an important segmentation when characterizing and analyzing a POI, since it allows: estimating the times and schedules of certain premises or locations and knowing when they are more and less crowded, estimating the average time that certain records are spent in or near specific areas, finding the times of day that are more and less favorable for implementing a product or service and even an expansion model.
The share of observations by day for February shows that the three busiest days of the week within the point of interest are Saturday with 19%, Friday with 16%, and Sunday with 13%. All of this helps businesses get an overview of how affluent the market is and the mobility of the area by day of the week, month or year, allowing them to implement strategies to reach targets more quickly and efficiently.
How do you classify the people who visit Plaza Fiesta and its surroundings and what are their socio-demographic characteristics?
With a data layer of the Global Wealth Index, collected by Facebook, the socioeconomic levels of the inhabitants were estimated. In parallel, applying geospatial data analysis techniques, we located corporate buildings, residential areas, schools, etc.; The combination of these analyses allows us to classify the inhabitants as students, workers, residents or "floating or instant population", i.e. people who only transit through that area to reach another location.This allowed us to identify that 49% of the recorded observations are visitors, 39% are residents and 12% are workers, all based on calculations of time spent and frequency of records in the area.Based on the facebook socioeconomic level classification, we identified that 50% of the observations are of a medium income level, 26% upper middle, 14% lower middle, 4% low and 3% high, the remaining 2% are of records that could not be related to the relative wealth Index of Facebook.How are the places within the City of Monterrey categorized and how are the visits distributed?
The points of interest
identified in the area were also categorized in order to understand the behavior patterns of pedestrians and thus identify potential markets and places that generate more interest.
Making a general analysis of the city of Monterrey, we can observe that Alameda Mariano Escobedo is the place where the highest distribution of visits was registered, followed by our analyzed point of interest Plaza Fiesta and Club Campestre
, agreeing with the analysis, we can see that both characterized areas such as parks, shopping malls and schools are the locations where the highest number of records are found.
Don't miss another POI characterization analysis that we conducted on Zona Rosa, in Mexico City. Read the article here
Thanks to the frequency of updates, coverage, ease of use and consistency of the data, these analyses help determine where to build new infrastructure (site selection). At PREDIK
Data-Driven we help our clients to understand consumer behavior patterns, study competition and understand user needs by geography.
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