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Anton Antonov

Apple mobility trends data visualization

Anton Antonov, Accendo Data LLC

Posted 5 months ago

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MODERATOR NOTE: coronavirus resources & updates: https://wolfr.am/coronavirus

Apple mobility trends data visualization

Version 0.9

Anton Antonov
MathematicaForPrediction at WordPress
SystemModeling at GitHub
April 2020

Introduction

I this notebook we ingest and visualize the mobility trends data provided by Apple, [APPL1].

We take the following steps:

Download the data

Import the data and summarise it

Transform the data into long form

Partition the data into subsets that correspond to combinations of geographical regions and transportation types

Make contingency matrices and corresponding heat-map plots

Make nearest neighbors graphs over the contingency matrices and plot communities

Plot the corresponding time series

Data description

From Apple’s page https://www.apple.com/covid19/mobility

About This Data
The CSV file and charts on this site show a relative volume of directions requests per country/region or city compared to a baseline volume on January 13th, 2020.
We define our day as midnight-to-midnight, Pacific time. Cities represent usage in greater metropolitan areas and are stably defined during this period. In many countries/regions and cities, relative volume has increased since January 13th, consistent with normal, seasonal usage of Apple Maps. Day of week effects are important to normalize as you use this data.
Data that is sent from users’ devices to the Maps service is associated with random, rotating identifiers so Apple doesn’t have a profile of your movements and searches. Apple Maps has no demographic information about our users, so we can’t make any statements about the representativeness of our usage against the overall population.

Observations

The observations listed in this subsection are also placed under the relevant statistics in the following sections and indicated with “Observation”.

The directions requests volumes reference date for normalization is 2020-01-13 : all the values in that column are

100

◼

From the community clusters of the nearest neighbor graphs (derived from the time series of the normalized driving directions requests volume) we see that countries and cities are clustered in expected ways. For example, in the community graph plot corresponding to “{city, driving}” the cities Oslo, Copenhagen, Helsinki, Stockholm, and Zurich are placed in the same cluster. In the graphs corresponding to “{city, transit}” and “{city, walking}” the Japanese cities Tokyo, Osaka, Nagoya, and Fukuoka are clustered together.

◼

In the time series plots the Sundays are indicated with orange dashed lines. We can see that from Monday to Thursday people are more familiar with their trips than say on Fridays and Saturdays. We can also see that on Sundays people (on average) are more familiar with their trips or simply travel less.

◼

Load packages

Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/DataReshape.m"]Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/Misc/HeatmapPlot.m"]

In[]:=

Data ingestion

Apple mobile data was provided in this WWW page: https://www.apple.com/covid19/mobility , [APPL1]. (The data has to be download from that web page -- there is an “agreement to terms”, etc.)

dsAppleMobility=ResourceFunction["ImportCSVToDataset"]["~/Downloads/applemobilitytrends-2020-04-14.csv"]

In[]:=

geo_type	region	transportation_type	2020-01-13	2020-01-14	2020-01-15	2020-01-16	2020-01-17	2020-01-18	2020-01-19
country/region	Albania	driving	100	95.3	101.43	97.2	103.55	112.67	104.83
country/region	Albania	walking	100	100.68	98.93	98.46	100.85	100.13	82.13
country/region	Argentina	driving	100	97.07	102.45	111.21	118.45	124.01	95.44
country/region	Argentina	walking	100	95.11	101.37	112.67	116.72	114.14	84.54
country/region	Australia	driving	100	102.98	104.21	108.63	109.08	89.0	99.35
country/region	Australia	transit	100	101.78	100.64	99.58	98.34	86.97	99.87
country/region	Australia	walking	100	101.31	101.82	104.52	113.73	100.24	98.57
country/region	Austria	driving	100	101.14	104.24	112.21	117.23	117.22	105.17
country/region	Austria	walking	100	101.55	105.59	112.24	123.36	131.05	89.93
country/region	Belgium	driving	100	101.19	107.49	107.67	117.38	119.32	102.68
country/region	Belgium	transit	100	98.67	105.01	105.87	113.55	110.73	100.53
country/region	Belgium	walking	100	101.46	110.44	118.86	139.1	174.99	111.72
country/region	Brazil	driving	100	99.71	100.9	101.88	113.69	114.38	91.06
country/region	Brazil	transit	100	102.45	104.28	100.2	97.06	81.01	69.05
country/region	Brazil	walking	100	106.3	104.75	99.05	104.13	101.18	69.84
country/region	Bulgaria	driving	100	102.56	104.73	104.35	114.79	118.01	105.42
country/region	Bulgaria	walking	100	101.9	99.61	100.06	117.97	128.66	99.64
country/region	Cambodia	driving	100	100.75	99.33	96.0	98.78	103.64	99.16
country/region	Cambodia	walking	100	98.06	99.7	98.0	106.81	106.84	106.98
country/region	Canada	driving	100	102.91	99.74	105.17	122.34	102.5	84.46
rows 1–20 of 395 columns 1–10 of 96

Data not in notebook; Store now »

Out[]=

TypeSystem`Atom

::shdw

:Symbol Atom appears in multiple contexts {TypeSystem`,System`}; definitions in context TypeSystem` may shadow or be shadowed by other definitions.

Observation: The directions requests volumes reference date for normalization is 2020-01-13 : all the values in that column are

100

Data dimensions:

Dimensions[dsAppleMobility]

In[]:=

{395,96}

Out[]=

Data summary:

Magnify[ResourceFunction["RecordsSummary"][dsAppleMobility],0.6]

In[]:=



1 geo_type

city	242
country/region	153

2 region

Amsterdam	3
Atlanta	3
Auckland	3
Australia	3
Baltimore	3
Barcelona	3
(Other)	377

3 transportation_type

driving	152
walking	152
transit	91

4 2020-01-13

1st Qu	100
3rd Qu	100
Max	100
Mean	100
Median	100
Min	100

5 2020-01-14

Min	90.14
1st Qu	99.84
Median	101.78
Mean	102.051
3rd Qu	104.048
Max	115.04

6 2020-01-15

Min	73.72
1st Qu	101.183
Median	104.19
Mean	104.576
3rd Qu	107.035
Max	138.

7 2020-01-16

Min	74.53
1st Qu	102.653
Median	106.87
Mean	107.315
3rd Qu	110.273
Max	139.9

8 2020-01-17

Min	83.96
1st Qu	108.055
Median	117.78
Mean	118.305
3rd Qu	127.27
Max	173.98

9 2020-01-18

Min	69.28
1st Qu	100.275
Median	114.84
Mean	116.855
3rd Qu	130.068
Max	184.3

10 2020-01-19

Min	43.79
1st Qu	86.6725
Mean	96.516
Median	97.88
3rd Qu	105.963
Max	157.28

11 2020-01-20

Min	72.9
1st Qu	97.34
Median	101.21
Mean	102.031
3rd Qu	104.83
Max	179.09

12 2020-01-21

Min	69.79
1st Qu	99.055
Median	102.93
Mean	104.141
3rd Qu	107.643
Max	159.86

13 2020-01-22

Min	78.25
1st Qu	99.89
Median	104.32
Mean	105.273
3rd Qu	109.03
Max	150.62

14 2020-01-23

Min	64.37
1st Qu	102.443
Median	106.03
Mean	106.672
3rd Qu	110.943
Max	153.1

15 2020-01-24

Min	56.81
1st Qu	107.673
Mean	117.839
Median	118.01
3rd Qu	127.233
Max	175.98

16 2020-01-25

Min	40.94
1st Qu	101.405
Median	114.97
Mean	117.811
3rd Qu	133.14
Max	236.75

17 2020-01-26

Min	33.49
1st Qu	84.2875
Mean	94.8029
Median	95.59
3rd Qu	103.64
Max	170.46

18 2020-01-27

Min	24.46
1st Qu	95.895
Median	100.23
Mean	101.023
3rd Qu	105.368
Max	173.54

19 2020-01-28

Min	20.32
1st Qu	97.3875
Median	102.75
Mean	102.803
3rd Qu	107.315
Max	162.96

20 2020-01-29

Min	19.26
1st Qu	98.15
Median	103.88
Mean	104.246
3rd Qu	109.193
Max	149.42

21 2020-01-30

Min	19.48
1st Qu	101.353
Median	106.3
Mean	106.786
3rd Qu	112.25
Max	146.72

22 2020-01-31

Min	17.24
1st Qu	109.42
Median	118.52
Mean	119.36
3rd Qu	129.17
Max	189.28

23 2020-02-01

Min	16.11
1st Qu	103.575
Median	116.34
Mean	119.308
3rd Qu	133.818
Max	223.35

24 2020-02-02

Min	15.44
1st Qu	85.855
Mean	96.3973
Median	98.24
3rd Qu	107.36
Max	139.13

25 2020-02-03

Min	13.37
1st Qu	95.7775
Mean	100.508
Median	101.16
3rd Qu	107.058
Max	165.91

26 2020-02-04

Min	11.07
1st Qu	97.905
Mean	102.857
Median	104.02
3rd Qu	108.72
Max	158.73

27 2020-02-05

Min	10.79
1st Qu	98.3225
Mean	103.907
Median	104.03
3rd Qu	110.438
Max	148.43

28 2020-02-06

Min	10.25
1st Qu	100.827
Median	106.84
Mean	106.932
3rd Qu	113.012
Max	166.88

29 2020-02-07

Min	8.79
1st Qu	109.248
Median	119.21
Mean	120.006
3rd Qu	130.29
Max	196.82

30 2020-02-08

Min	7.51
1st Qu	103.318
Median	118.22
Mean	120.873
3rd Qu	138.017
Max	220.8

31 2020-02-09

Min	9.62
1st Qu	85.1775
Mean	96.3173
Median	96.86
3rd Qu	107.528
Max	146.32

32 2020-02-10

Min	8.87
1st Qu	97.7025
Median	103.28
Mean	103.413
3rd Qu	109.88
Max	142.61

33 2020-02-11

Min	9.18
1st Qu	101.36
Mean	105.656
Median	105.87
3rd Qu	112.055
Max	142.01

34 2020-02-12

Min	9.14
1st Qu	102.94
Mean	108.203
Median	109.02
3rd Qu	114.753
Max	143.63

35 2020-02-13

Min	10.39
1st Qu