2nd Main Course

This data might buy you a better understanding of what business is more related to what other business in a commercial zone or central business district with lots of POIs, based on real data. What other data do you have that tells you how many visitors engaged in multi-purpose shopping like filling up the tank, going to breakfast or lunch at a locally-owned business, and then do some household and personal shopping in one big long trip.

Looking at this information over several POIs over a long period of time could provide local business owners with clues about how to better take advantage of multi-purpose local buying trips to nearby big brands that draw in people who don’t even live in the area, stretching the idea of what constitutes the local market area.

Looking at this information may also help business advocates or local & state decision makers make better decisions about commercial zoning or funding transportation infrastructure based on actual purchasing behavior.

Mobile Device Visits to a Single Full-Service Restaurant Showing the Impact of COVID-19

The graph below is data from the Hawaii Tourism Authority on monthly visitor expenditures and expenditures per capita on total visitor arrivals from January 1999 to July 2021. You can see the impact of the COVID-19 public health emergency response. COVID-19 created by far the most significant decrease in total visitor arrivals to the State of Hawaii over the last twenty years.

Home Location of Mobile Device Visitors Pre and Post-COVID-19

The graph below is data on number of visitors to a single POI, Ken’s House of Pancakes in Hilo, from each census block group based on the visitor’s home location. You can see the impact of the COVID-19 public health emergency response. COVID-19 created by far the most significant decrease in total visitor arrivals to the State of Hawaii over the last twenty years.

Daytime Location of Mobile Device Visitors Pre and Post-COVID-19

The graph below is data on the number of visitors to a single POI, Ken’s House of Pancakes, from each census block group based on primary daytime location between 9 am – 5 pm. You can see the impact of the COVID-19 public health emergency response. COVID-19 created by far the most significant decrease in total visitor arrivals to the State of Hawaii over the last twenty years.

Popularity by Day Pre and Post-COVID-19

The graph below is data on the number of visits in total on each day of the week (in local time) over the course of each day. This is a mapping of the day of the week to the total number of visits seen on each day of the week during the course of the month.

Popularity by Hour Pre and Post-COVID-19

The graph below is data on the number of visits in each hour over the course of the date range, in local time. This is an array of visits seen in each hour of the day over the course of the month. Local time is used. If a visitor stays for multiple hours, an item in the array will be incremented for each hour during which the visitor stayed. This means that if you sum the numbers in the popularity_by_hour array the sum will likely be greater than the amount shown in the raw_visit_counts column (since the raw_visit_counts counts a multiple-hour visit as one visit).

Bucketed Dwell Times Pre and Post-COVID-19

The graph below is data on the distribution of visit dwell times based on pre-specified buckets. Key is the range of dwell time in minutes and value is number of visits that were within that range. This is a dictionary of different time spans and the number of visits that were of each duration. The time spans are in minutes.

Map of Related Same Day & Month Brands

The map below is data about other brands that the visitors to this POI visited on the same day as the visit to this POI. Limited to top 20. Other brands that the visitors to this POI visited in the same month as the visit to this POI. Limited to top 20. These are the brands that the visitors to this POI also visited, on the same day that they visited the POI. The number mapped to each brand is an indicator of how highly correlated a POI is to a certain brand. The value is a simple percent of POI visitors that visited the other brand on the same day.

Bar Chart of Related Same Day & Month Brands

The graph below is data about other brands that the visitors to this POI visited on the same day as the visit to this POI. Limited to top 20. Other brands that the visitors to this POI visited in the same month as the visit to this POI. Limited to top 20. These are the brands that the visitors to this POI also visited, on the same day that they visited the POI. The number mapped to each brand is an indicator of how highly correlated a POI is to a certain brand. The value is a simple percent of POI visitors that visited the other brand on the same day.

TECHNICAL NOTES

The key to the map using PostgreSQL with the PostGIS extension is to first parse the JSON columns, do a spatial join by brand name to find all the brand locations within Hawaiʻi County, and calculate straight-line distances. Doing a select distinct on the results ordered by distance will identify the most likely brand location. There are no assurances the brand location selected was the one that was actually visited. In some cases, like gas stations, there are often several brand locations all around the POI. Final maps and bar charts were done in R using the leaflet and ggplot packages.

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