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For instance, if the red bar on the figure above represents 40, and this location is anticipated to be at its weekly busiest on a Sunday at midday, then currently the location is 40% as busy as during a Sunday at twelve noon. Now, obviously, the existing value can be bigger than 100 too, which suggests that the place is busier than the expected weekly busiest time.

It must be evident that we can utilize these information to get an estimate of how people are distancing nowadays. We began gathering information for this function from a select number of locations in Pittsburgh on March 13th and we discovered some fascinating patterns. Individuals, in basic, were following suggestions (the order for safeguarding in location was revealed on March 19th and imposed on March 23rd in Pennsylvania).

Following, are some representative time-series examples of places that experienced a reduction in traffic. Check For Updates was bars during March 14th that were busier than regular, with Pittsburghers celebrating St. Patrick's day as it can be seen by the following time-series: Now the only type of service that did not see any considerable decline throughout the very first week of data collection in Pittsburgh was grocery stores.

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For instance, the following figure reveals typical daily changes from a hectic supermarket: These results appear to show that people stockpiled and distanced themselves even from grocery shops. Among the 30 groceries that we have actually been keeping an eye on currently, the last 10 days there has been an average reduction of 30% in the crowdedness levels in these organizations.

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Part of this decline can be policies put by different grocery stores on how numerous people can be within their properties at any offered time. This certainly will have an effect on the volumes reported by Google and other companies. So it is always good to remember these things when trying to understand the information and make conclusions.

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Now I was a bit doubtful at first since people rarely check-in to places they go, but digging a bit deeper in the information, these are not based upon check-ins however rather on passive picking up of user areas (i. e., comparable to what Google does). I was particularly interested in property locations (that we can not get info about from the Google API) and how foot traffic has actually altered there.