Impact of COVID-19 Pandemic on Home Range in a Suburban City in the Osaka Metropolitan Area

sustainability

Article Impact of COVID-19 Pandemic on Home Range in a Suburban City in the Osaka Metropolitan Area

Haruka Kato * , Atsushi Takizawa and Daisuke Matsushita

Department of Housing and Environmental Design, Graduate School of Human Life Science, Osaka City University, Osaka 5588585, Japan; [email protected] (A.T.); [email protected] (D.M.) * Correspondence: [email protected]; Tel.: +81-6-6605-2823

Abstract: This study aims to clarify the impact of the COVID-19 pandemic on home range. The home range is the area that individuals traverse in conducting their daily activities, such as working and shopping. In Japan, the central government declared the first state of emergency in April 2020. This study analyzed the panel data for mobile phone GPS location history from April 2019 to April 2020 in Ibaraki City, Osaka Metropolitan area. The study applied the minimum convex polygon method to analyze the data. The results show that the home range decreased significantly between April 2019 and April 2020. Specifically, the home range in 2020 decreased to approximately 50% of that in 2019 because of COVID-19 infection control measures, preventing people from traveling far from their homes and only allowing them to step outside for the bare minimum of daily activities and necessities. The results suggest that the emergency reduced people’s home ranges to the neighborhood scale. Therefore, it is necessary to consider designing new walkable neighborhood environments after the COVID-19 pandemic era.

Citation: Kato, H.; Takizawa, A.; Keywords: walkability; COVID-19 pandemic; home range; GPS location history data; minimum Matsushita, D. Impact of COVID-19 convex polygon; Osaka Metropolitan area Pandemic on Home Range in a Suburban City in the Osaka Metropolitan Area. Sustainability 2021, 13, 8974. https://doi.org/ 1. Introduction 10.3390/su13168974 1.1. Background

Academic Editors: Simona Tondelli A significant social problem in Japan is population decline, with a rapidly increasing and Elisa Conticelli number of older adults. In 2019, the Japanese aged population (65 years old and over) was 35.89 million, constituting 28.4 percent of the total population [1]. For an aging society, Received: 9 July 2021 the home range is an essential indicator in planning a “community-based integrated care Accepted: 9 August 2021 system” to support the daily activities of older adults [2]. The home range is defined as Published: 11 August 2021 the area that individuals traverse in conducting their daily activities, such as working and shopping [3]. In other words, the home range is one of the essential considerations for Publisher’s Note: MDPI stays neutral policymakers and urban planners in Japan. Therefore, the government has introduced with regard to jurisdictional claims in enhanced policies that allow citizens to conduct their daily activities on a neighborhood published maps and institutional affil- scale. For example, in the field of urban planning, many local governments developed the iations. “Location Optimization Plan” [4]. One of the aims of planning is to improve walkability by designing walkable neighborhoods. Walkability is defined as a property of a residential environment that promotes walking or cycling with safety, comfort, and the attractions of daily life [5]. Designing Copyright: © 2021 by the authors. walkable neighborhoods could contribute to reducing the burden on the environment [6]. Licensee MDPI, Basel, Switzerland. Additionally, the design could contribute to promoting the health of older adults [7,8]. This article is an open access article Chen et al. [9] found that older adults living in high walkability areas tend to be healthier, distributed under the terms and with higher physical activity levels. The contribution could help to keep healthcare costs conditions of the Creative Commons under control and maintain sustainable financial balances in local governments. Attribution (CC BY) license (https:// The Coronavirus Disease 2019 (COVID-19) pandemic might accelerate the change as creativecommons.org/licenses/by/ the “new normal” lifestyle. For example, the mayor of Paris, Anne Hidalgo, proposed the 4.0/).

Sustainability 2021, 13, 8974. https://doi.org/10.3390/su13168974 https://www.mdpi.com/journal/sustainability Sustainability 2021, 13, x FOR PEER REVIEW 2 of 11

Sustainability 2021, 13, 8974 The Coronavirus Disease 2019 (COVID-19) pandemic might accelerate the change2 of as 11 the “new normal” lifestyle. For example, the mayor of Paris, Anne Hidalgo, proposed the realization of a 15-min city by 2024, in a move toward the design of walkable neighborhoods after the COVID-19 pandemic era, where people can live without using cars [10]. realizationThere has been of a 15-min an increase city by in 2024,research in a on move the towardprogression the design toward of the walkable realization neighborhoods of the 15- aftermin city the COVID-19[11]. In Japan, pandemic the first era,recorded where ca peoplese of COVID-19 can live withoutoccurred using in January cars [ 102020,]. There and hasthe beenJapanese an increase central government in research ondeclared the progression the first state toward of emergency the realization on 7 April of the 2020 15-min [12]. cityIn Osaka [11]. In Metropolitan Japan, the first area, recorded the state case of emer of COVID-19gency lasted occurred until 21 in May January 2020. 2020, Since and then, the Japanesethe number central of infected government people declared has continued the first to state fluctuate of emergency in Osaka on [13]. 7 April During 2020 the [12 first]. In Osakastate of Metropolitan emergency in area, Ibaraki the state City of in emergency Osaka Metropolitan lasted until area, 21 May Kato 2020. [14] Sincereported then, that the numberdense human of infected space-times people haswere continued formed in to fluctuatethe parks in as Osaka well as [13 in]. the During stations. the first Similarly, state of emergencyKato et al. in[15] Ibaraki reported City an in increasing Osaka Metropolitan number of area, bicycle Kato trips. [14] Those reported studies that densesuggest human that space-timesthe home range were might formed be indecreased the parks to as the well neighborhood as in the stations. scale due Similarly, to the Katostate of et al.emer- [15] reportedgency. Therefore, an increasing this study number focuses of bicycle on ch trips.anges Those in the studies home range suggest during that thethe homeCOVID-19 range mightpandemic. be decreased to the neighborhood scale due to the state of emergency. Therefore, this study focuses on changes in the home range during the COVID-19 pandemic. 1.2. Purpose 1.2. Purpose This study aims to clarify the impact of the COVID-19 pandemic on home ranges in This study aims to clarify the impact of the COVID-19 pandemic on home ranges suburban Ibaraki City, Osaka Metropolitan area. The study primarily employed the min- in suburban Ibaraki City, Osaka Metropolitan area. The study primarily employed the imum convex polygon (MCP) method using mobile phone GPS location history (LH) data. minimum convex polygon (MCP) method using mobile phone GPS location history (LH) The analysis was conducted on panel data from April 2019 to April 2020 using the case data. The analysis was conducted on panel data from April 2019 to April 2020 using the study of Ibaraki City. The period and area of the LH data are the same as that of the studies case study of Ibaraki City. The period and area of the LH data are the same as that of the of Kato [14] and Kato et al. [15]. Using the LH data, this study clarified the home range by studies of Kato [14] and Kato et al. [15]. Using the LH data, this study clarified the home utilizing MCP. range by utilizing MCP. Ibaraki City is a suburban city in the Osaka Metropolitan suburban area located mid- Ibaraki City is a suburban city in the Osaka Metropolitan suburban area located way between Osaka City and Kyoto City, as shown in Figure 1. Ibaraki City frequently midway between Osaka City and Kyoto City, as shown in Figure1. Ibaraki City frequently collaborates with neighboring municipalities in the metropolitan suburban area. Resi- collaborates with neighboring municipalities in the metropolitan suburban area. Residents dents of this city frequently commute about 30 min to neighboring cities, such as Osaka of this city frequently commute about 30 min to neighboring cities, such as Osaka City or City or Kyoto City, because of the city’s extensive train network. The population of Ibaraki Kyoto City, because of the city’s extensive train network. The population of Ibaraki City is City is approximately 280,000, and its area is 10 km east-west and 17 km north-south. approximately 280,000, and its area is 10 km east-west and 17 km north-south. Similar to Similar to other cities, it experienced an increase in COVID-19 infections during the state other cities, it experienced an increase in COVID-19 infections during the state of emergency. of emergency. However, during April 2020, only zero to three Ibaraki citizens were in- However, during April 2020, only zero to three Ibaraki citizens were infected with COVID- fected with COVID-19 [13]. This number of cases was small compared to other cities in 19 [13]. This number of cases was small compared to other cities in Osaka Prefecture. Osaka Prefecture.

FigureFigure 1.1.Location Location ofof IbarakiIbaraki City in the Osaka Metropolitan Area.

1.3.1.3. NoveltyNovelty TheThe impactimpact ofof thethe COVID-19COVID-19 pandemic on the home range is is still still being being studied. studied. For For example,example, thethe COVID-19COVID-19 pandemicpandemic has led to the widespread adoption adoption of of telecommuting telecommuting amongamong adultsadults and remote remote learning learning among among children children [16]. [16 In]. order In order to understand to understand daily daily ac- activitiestivities more more accurately, accurately, mobile mobile phone phone GPS GPS data data is used. is used. Lee et Lee al. et[17] al. clarified [17] clarified that equi- that equitabletable policy policy decisions decisions could could be made be made based based on the on LH the data LH databecause because the LH the data LH datathus thusob- obtainedtained can can provide provide more more accurate accurate population population data than data census than census data in dataemergencies. in emergencies. Oliver Oliveret al. [18] et al. pointed [18] pointed out the out effectiveness the effectiveness of using of using mobile mobile phone phone data datafor analysis for analysis in the in theCOVID-19 COVID-19 pandemic. pandemic. Using Using GPS GPS data, data, the spatial the spatial distribution distribution of COVID-19 of COVID-19 infected infected peo- peopleple has has been been verified verified in China in China [19]. [ 19In]. the In Un theited United States, States, it was it confirmed was confirmed that there that therewas was an association between mobility patterns and COVID-19 transmission by using mobile phone location data [20]. Using mobile phone data, Chang et al. [21] simulated the spread of the infection and pointed out the necessity of controlling the movements of a few super spreaders. In addition to mobility-related information, mobile phone data can also allow Sustainability 2021, 13, 8974 3 of 11

researchers to determine which citizens stayed at home [22]. These studies suggest the effectiveness of mobile phone GPS LH data during the COVID-19 pandemic. However, the impact of the state of emergency varied from country and region. In particular, the Japanese state of emergency was called a “soft lockdown” [23]. This is because the Japanese government restricted the activities of companies and other organizations but not of individuals [24]. Therefore, most citizens could go out, at least minimally, even under the state of emergency declaration. In fact, Anzai et al. [25] reported that widespread travel projects could spread the infection. In Tokyo, Yabe et al. [26] found that human mobility decreased by more than 50% and social contact by more than 70% between January and April 2020. By contrast, unexpectedly high population densities had been reported in suburban cities [27]. Therefore, the population may have decreased in urban centers but increased in suburban cities. Kato [14] obtained a similar result using the case of Ibaraki City. Therefore, this present study’s novel contribution is to quantitatively clarify changes in home ranges using LH data in the suburban city. The study’s results will allow us to consider designing a new neighborhood environment after the COVID-19 pandemic era.

2. Materials and Methods 2.1. Mobile Phone GPS Location History Data The present study used LH data comprising GPS location data obtained at regular intervals from mobile phones with users’ consent. The time interval for data collection was approximately every 15 min, depending on the mobile phone type. In Ibaraki City, there are approximately 1,600,000 LH data logs per day for approximately 12,000 individuals. The LH data can be considered as that of approximately 4.2% of its citizens. In Japan, the Agoop Corporation collects daily mobile phone location data and provides anonymized data for research purposes [28]. Governments and municipalities have used GPS data provided by Agoop Corporation for the control of the COVID-19 infection [29]. In addition, Kato [14] and Kato et al. [15] studied human mobility using the LH data provided by Agoop Corporation. Therefore, the LH data was validated as high-quality data used in policymaking and research to control the pandemic. This study complies with the “Guidelines for the Use of Device Location Data,” a common regulation for location data in Japan [30]. For example, the guideline prohibits using GPS data for any purpose that involves identifying individual users. The data were collected by obtaining consent from the mobile phone users, all of whom installed speciﬁc applications based on the type of data to be collected, the purpose of use, the provision to third parties, and the privacy policy. One of the applications is “WalkCoin,” which uses pedometers to help users earn e-coins [31]. Therefore, the LH data provided by Agoop have been acquired mainly from ordinary citizens. Additionally, users can stop sending the GPS location data by changing their phones’ basic settings. The data were obtained through contracts between Agoop Corporation and the Graduate School of Life Sciences at Osaka City University, where the authors work. The LH data mainly comprises the following variables: daily ID, user ID, year, month, day, day of week, hour, minutes, latitude, longitude, operating system (Android/iOS), country, GPS accuracy, speed, mesh ID, estimated transportation methods, and gender. User IDs are anonymized 96-digit alphanumeric codes, including information that protects personal privacy such as names, ages, and addresses. The user ID is a permanent ID assigned to each device and enables panel data analysis. Among these variables, this study used user ID, year/month/day, hour/minutes, and latitude/longitude. The study extracted data on people living in Ibaraki City. Therefore, data relating to people who only passed through Ibaraki City—for example, people going from Osaka to Tokyo by train or car—were excluded. The study extracted user IDs with LH data that started after 03:00 AM and were obtained from users located in Ibaraki City. This means that the time the mobile phone was turned on and GPS data was collected was after 3:00 AM. The time of 03:00 AM was selected because it had the lowest amount of LH data on traveling by walking, cycling, trains, or cars, as observed by analyzing the SustainabilitySustainability 2021 2021, ,13 13, ,x x FOR FOR PEER PEER REVIEW REVIEW 44 of of 11 11

TheThe studystudy extractedextracted datadata onon peoplepeople livingliving inin IbarakiIbaraki City.City. Therefore,Therefore, datadata relatingrelating toto peoplepeople whowho onlyonly passedpassed throughthrough IbarakiIbaraki CityCity—for—for example,example, peoplepeople goinggoing fromfrom OsakaOsaka toto TokyoTokyo byby traintrain oror car—werecar—were excluded.excluded. TheThe ststudyudy extractedextracted useruser IDsIDs withwith LHLH datadata thatthat Sustainability 2021, 13, 8974 startedstarted afterafter 03:0003:00 AMAM andand werewere obtainedobtained fromfrom usersusers locatedlocated inin IbarakiIbaraki City.City. ThisThis meansmeans4 of 11 thatthat thethe timetime thethe mobilemobile phonephone waswas turnedturned onon andand GPSGPS datadata waswas collectedcollected waswas afterafter 3:003:00 AM.AM. TheThe timetime ofof 03:0003:00 AMAM waswas selectedselected becausebecause itit hadhad thethe lowestlowest amountamount ofof LHLH datadata onon travelingtraveling by by walking, walking, cycling, cycling, trains, trains, or or cars, cars, as as observed observed by by analyzing analyzing the the estimated estimated travel travel estimated travel methods at each hour, as shown in Figure2. This study analyzed the home methodsmethods atat eacheach hour,hour, asas shownshown inin FigureFigure 2.2. ThisThis studystudy analyzedanalyzed thethe homehome rangerange fromfrom range from 3:00 to 27:00. Moreover, it assumed that the users were asleep and in the same 3:003:00 toto 27:00.27:00. Moreover,Moreover, itit assumedassumed thatthat thethe usersusers werewere asleepasleep andand inin thethe samesame locationlocation location when the smartphone was turned off after 3:00 and before 27:00. whenwhen thethe smartphonesmartphone waswas turnedturned offoff afterafter 3:003:00 andand beforebefore 27:00.27:00.

FigureFigure 2.2. EstimatedEstimated travel travel methods methods atat eacheach timetime inin oneone one day.day. day.

TheThe LHLH datadata provided provided by by AgoopAgoop werewere were notnot not obob obtainedtainedtained fromfrom from allall all mobilemobile mobile phonesphones phones inin in Japan.Japan. Japan. AsAsAs mentionedmentionedmentioned above, above, the the dataset datasetdataset in in this this study study representsrepresents approximatelyapproximately approximately 4.2%4.2% 4.2% ofof of IbarakiIbaraki Ibaraki City’sCity’s population.population. An An equation equation toto estimateestimate thethe the populationpopulation population byby by analyzinganalyzing analyzing thethe the relationshiprelationship relationship betweenbetween actualactual traffic trafﬁctraffic surveys surveyssurveys and and LH LH datadata waswas proposedproposed [32].[32]. [32]. However,However, However, itit it isis is possiblepossible possible toto to analyzeanalyze the the home home range rangerange distribution distribution withoutwithout without usingusing using LHLH LH datadata data fromfrom from thethe the entireentire entire population.population. population. Furthermore,Furthermore, the the penetration penetration rate rate of of mobile mobile phones phones phones in in in Japan Japan Japan is is is high high high across across across all all all generations. generations. generations. Therefore,Therefore, this this study’sstudy’s study’s utilizedutilized utilized datadata data werewere were coconsiderednsidered considered adequateadequate adequate forfor givinggiving for giving anan accurateaccurate an accurate pic-pic- picturetureture ofof the ofthethe changeschanges changes inin thethe in the homehome home range.range. range.

2.2.2.2. PanelPanel Data Data Analysis AnalysisAnalysis for for the the COVID-19COVID-19 PandemicPandemic Pandemic ThisThis studystudy performed performed panel panel datadata analysisanalysis analysis dudu duringringring thethe the firstfirst ﬁrst statestate state ofof of emergency.emergency. emergency. ItIt It com-com- com- paredpared thethe panelpanel data data for forfor one one month month from from when when when Japan Japan Japan implemented implemented implemented the the the state state state of of of emergency emergency emergency ininin AprilApril 20202020 with withwith the thethe panel panel datadata forfor AprilApril April 20192019 2019... ManyMany Many citiescities cities worldwideworldwide worldwide werewere were alsoalso also inin in lock-lock- lock- downdown inin April April 2020. 2020. On On 7 7 April April 2020, 2020, 2020, when when when the the the emergency emergency emergency was was was declared, declared, declared, Osaka Osaka Osaka Prefecture Prefecture Prefecture requestedrequested peoplepeople to to stay stay at atat home homehome and and cancel cancel anyany events, events, asas as shownshown shown inin in FigureFigure Figure 33 [[33].[33].33]. OnOn On 1414 14 AprilAprilApril 2020,2020, aa weekweek later, later, the the government government requrequ requestedestedested restrictionsrestrictions onon on thethe the unnecessaryunnecessary unnecessary useuse use ofof of facilitiesfacilities such such as as community community centers, centers, public public libraries,libraries, and and gymnasiumsgymnasiums gymnasiums [34].[34]. [34]. ComplianceCompliance Compliance withwithwith thesethese requestsrequests might might lead lead toto thethe graduagradua gradualll narrowingnarrowing narrowing downdown down ofof of residents’residents’ residents’ homehome home ranges.ranges. ranges. Therefore,Therefore, itit isis validvalid toto analyzeanalyze the thethe periods periods of of April April 20192019 andand AprilApril April 2020.2020. 2020.

Figure 3. The number of COVID-19 infections in Osaka Prefecture from February to May 2020. FigureFigure 3.3. TheThe numbernumber of COVID-19 infections in Osak Osakaa Prefecture Prefecture from from February February to to May May 2020. 2020.

2.3. Minimum Convex Polygon This study analyzed home ranges using the MCP method. The MCP method is an established radio–telemetric analysis method for estimating animals’ home ranges. The radio–telemetry analysis involves tracking individual animals using GPS. This analysis has contributed signiﬁcantly to animal ecological research development since the 1960s. The kernel density method is another well-known radio–telemetric home range estimation Sustainability 2021, 13, x FOR PEER REVIEW 5 of 11

2.3. Minimum Convex Polygon This study analyzed home ranges using the MCP method. The MCP method is an Sustainability 2021, 13, 8974 established radio–telemetric analysis method for estimating animals’ home ranges. 5The of 11 radio–telemetry analysis involves tracking individual animals using GPS. This analysis has contributed significantly to animal ecological research development since the 1960s. The kernel density method is another well-known radio–telemetric home range estima- methodtion method [35, 36[35,36].]. This This method method describes describes the the utilization utilization probabilityprobability of points points through through a a probabilityprobability densitydensity function.function. However,However, the the advantage advantage of of MCP MCP is is its its simplicity simplicity and and intuitive intui- use.tive use. MCP MCP analyzes analyzes zones zones that that connect connect the outermostthe outermost observation observation points points [37]. [37]. It generally It gen- calculateserally calculates the home the home range range area. However,area. However, it was it used was inused this in study this study to calculate to calculate the home the rangehome lengthrange length (HR-length), (HR-length), which iswhich the distance is the distance to the farthest to the farthest point moved point frommoved the from home. Thisthe home. is because This localis because governments local governments used distance used when distance considering when considering infection prevention infection measuresprevention during measures the COVID-19during the pandemic.COVID-19 pandemic. Speciﬁcally,Specifically, thethe HR-lengthHR-length was was calculated calculated in in three three phases. phases. First, First, a personala personal space-time space- pathtime path between between 3:00 3:00 and and 27:00 27:00 was was drawn. drawn. Then, Then, this this study study identiﬁedidentified the starting starting home home pointpoint after 3:00, 3:00, and and the the farthest farthest point point move movedd from from the home the home between between 3:00 and 3:00 27:00. and 27:00.Sec- Secondly,ondly, the thehome home range range distance distance for each for each mob mobileile phone phone user userwas calculated was calculated from the from lati- the latitudetude and and longitude longitude difference difference between between the the starting starting home home point point and and the the farthest farthest point point movedmoved from the home. Thirdly, Thirdly, based on on the the space-time space-time paths paths of of multiple multiple mobile mobile phone phone users,users, their HR-length HR-length were were calculated. calculated. Then, Then, thethe averages averages of their of their HR-length HR-length for each for day each daywere were calculated. calculated. Figure Figure 4 presents4 presents the schema the schematictic diagram diagram of the equation of the equation used to analyze used to analyzethe MCP. the MCP.

FigureFigure 4.4. Analysis Process to analyze the the minimum minimum convex convex polygon. polygon.

This study performed a a series series of of analyses analyses using using HR-length HR-length data. data. First, First, the the change change in in thethe averageaverage HR-lengthHR-length was was analyzed analyzed for for April April 2019 2019 and and April April 2020. 2020. Next, Next, the averagethe average value ofvalue the of HR-length the HR-length for each for weekeach week was calculated.was calculated. Finally, Finally, municipalities municipalities with with the the farthest far- pointsthest points moved moved from from homes homes were were analyzed. analyzed.

3. Results 3.1. Change of Daily Home Range Length The analysis results of the HR-length of each day, calculated by applying the MCP method, are shown in Figure5. Figure5 shows the analysis of the average and 95% intervals of the HR-length in a time series. In Figure5, “d” means weekday and “e” means weekend. It shows that the HR-length decreased signiﬁcantly between April 2019 and Sustainability 2021, 13, x FOR PEER REVIEW 6 of 11

3. Results 3.1. Change of Daily Home Range Length Sustainability 2021, 13, 8974 The analysis results of the HR-length of each day, calculated by applying the MCP6 of 11 method, are shown in Figure 5. Figure 5 shows the analysis of the average and 95% intervals of the HR-length in a time series. In Figure 5, “d” means weekday and “e” means weekend. It shows that the HR-length decreased significantly between April 2019 and April 2020 because of the COVID-19 pandemic. Next, the changes in HR-length in April April 2020 because of the COVID-19 pandemic. Next, the changes in HR-length in April 2019 and and April April 2020 2020 were were examined examined separately. separately. In In 2019, 2019, the the HR-length HR-length tended tended to to be be higher higher on weekdays than on weekends and holidays. However, at the end of April, Japan has a on weekdays than on weekends and holidays. However, at the end of April, Japan has a long holiday called “the Golden Week.” In 2019, this was a 10-day holiday, when many long holiday called “the Golden Week.” In 2019, this was a 10-day holiday, when many people went on trips or returned to their hometowns. Therefore, the HR-length increased people went on trips or returned to their hometowns. Therefore, the HR-length increased signiﬁcantly from 26 April 2019, when the holiday started. significantly from 26 April 2019, when the holiday started.

Figure 5. ChangeChange of of daily daily HR-length HR-length in in April April 2019 2019 and and April April 2020. 2020.

InIn 2020, 2020, the the HR-length HR-length at at the the beginning beginning of of April April was was approximately approximately two-thirds two-thirds of of what it was was in in April April 2019. 2019. Since Since mid-March, mid-March, Osaka Osaka Prefecture Prefecture had had requested requested that that residents residents refrain from leavingleaving thethe prefecture.prefecture. Therefore, Therefore, as as of of 1 April1 April 2020, 2020, the the HR-length HR-length had had already al- readydecreased. decreased. Subsequently, Subsequently, the length the length at the at end the of end April of April 2020 fell2020 to fell approximately to approximately half of halfthat of in that April in 2019.April After2019. 7After April 7 whenApril wh theen state the ofstate emergency of emergency was declared, was declared, the length the lengthdecreased decreased further, further, reducing reducing daily, particularly daily, particularly between between 7 and 12 April.7 and 12 The April. government The gov- did ernmentnot impose did any not penaltiesimpose any on penalties those who on broke those thewho rules; broke therefore, the rules; many therefore, people many gradually peo- plechanged gradually their changed home ranges their evenhome after ranges the even state after of emergency the state of was emergency declared. was However, declared. from However,the third to from fourth the weekthird ofto Aprilfourth 2020, week the of HR-lengthApril 2020, didthe notHR-length change did signiﬁcantly not change because sig- nificantlypeople’s behaviorbecause people’s had changed behavior sufﬁciently had changed and the sufficiently government and hadthe askedgovernment residents had to askedrefrain residents from traveling to refrain during from the traveling Golden during Week. the Golden Week.

3.2. Change Change of of Weekly Weekly Home Home Range Range Length Length Next,Next, the the weekly weekly averages averages of ofthe the home home range range length length were were analyzed, analyzed, and the and results the re- aresults shown are shown in Figure in Figure 6. Significant6. Significant differences differences in the average in the average values for values each for week each are week in- dicatedare indicated by the student by the t student-test. Figuret-test. 6 shows Figure the6 analysisshows theof the analysis average of and the 95% average intervals. and In95% Figure intervals. 6, blue Inis each Figure week6, blue of April is each 2019 weekand red of is April each 2019week andof April red 2020. is each There week was of aApril significant 2020. increase There was in the a significant average value increase of the in HR-length the average for each value week of the (abbreviated HR-length as for HReach − lengthweek) (abbreviated from the third as toHR fourth^− length week) from of April the third2019 ( toHR fourth − length week2019w3 of= 16.88 April km; 2019 HR − length 2019w4 = 19.25 km), which verifies that the Golden week increased the (HR^− length = 16.88 km; HR^− length = 19.25 km), which verifies that the HR − length. However,2019w3 there was a significant2019w4 decrease in HR − length in the first week ofGolden April 2020 week compared increased with the theHR fourth^− length week. However, of April 2019 there in was order a significantto analyze the decrease impact in ofHR the^− COVID-19length in the pandemic. first week The of AprilHR − 2020 length compared in 2020 was with found the fourth to have week decreased of April to 2019 ap- in proximatelyorder to analyze half thethat impact of 2019 of (HR the − COVID-19 length2020w1 pandemic. = 9.86 km). The AfterHR that,^− length the HRin 2020− length was con- found tinued to decrease significantly over the second (HR − length2020w2 = 7.86 km) and third to have decreased to approximately half that of 2019 (HR^− length2020w1 = 9.86 km). After

that, the HR^− length continued to decrease signiﬁcantly over the second (HR^− length2020w2

= 7.86 km) and third (HR^− length2020w3 = 6.99 km) weeks of 2020. This result validates the daily change of HR-length in Figure5, which shows a signiﬁcant decrease in HR^− length with the declaration of a state of emergency. SustainabilitySustainability 2021 2021, ,13 13, ,x x FOR FOR PEER PEER REVIEW REVIEW 77 of of 11 11

Sustainability 2021, 13, 8974 (HR(HR − − length length2020w32020w3 = = 6.99 6.99 km) km) weeks weeks of of 2020. 2020. This This result result validates validates the the daily daily change change of of7 ofHR- HR- 11 lengthlength in in Figure Figure 5, 5, which which shows shows a a significant significant decrease decrease in in HRHR − − length length withwith the the declaration declaration ofof a a state state of of emergency. emergency.

Figure 6. Change of weekly HR − length for April 2019 and April 2020. FigureFigure 6. 6.Change Change of of weekly weeklyHR HR^− −length length for for April April 2019 2019 and and April April 2020. 2020.

3.3.3.3.3.3. MunicipalitiesMunicipalities Municipalities ofof of thethe the FarthestFarthest Farthest PointsPoints Points Subsequently,Subsequently,Subsequently, thethe the municipalitiesmunicipalities municipalities comprisingcomprising comprising thethe the farthestfarthest farthest pointpoint point moved moved from from the the resi- resi- dents’dents’dents’ homeshomes homes werewere were analyzed,analyzed, analyzed, andand and thethe the resultsresults results arear aree shownshown shown inin in FigureFigure Figure7 .7. 7. These These These results results results reveal reveal reveal thatthatthat the the distribution distribution of ofof the thethe farthest farthest points points points di di diddd not not not change change change significantly significantly significantly from from from the the the first first first to to the tothe thefourthfourth fourth weekweek week ofof ofAprilApril April 2019.2019. 2019. ForFor For example,example, example, thethe the ratioratio ratio ofof of Ibaraki Ibaraki City CityCity as asas thethe farthest farthestfarthest point pointpoint movedmovedmoved fromfrom from residents’residents’ residents’ homes homes homes went went went from from from approximately appr approximatelyoximately 45% 45% 45% to to 48%.to 48%. 48%. However, However, However, the the the ratio ratio ratio of Ibarakiofof Ibaraki Ibaraki City City City as theas as the the farthest farthest farthest point point point moved moved moved from fr fromom residents’ residents’ residents’ homes homes homes was was was significantly significantly significantly different differ- differ- betweenentent between between the the fourththe fourth fourth week week week of Aprilof of April April 2019 2019 2019 and and and the the the first first first week week week of of of April April April 2020. 2020. 2020. InIn In particular,particular, particular, thisthisthis changedchanged changed significantlysignificantly significantly betweenbetween between thethe the firstfirst first andand and secondsecond second weeksweeks weeks ofof of AprilApril April 2020.2020. 2020. InIn In thethe the firstfirst first weekweekweek ofof of AprilApril April 2020,2020, 2020, beforebefore before thethe the statestate state ofof of emergencyemer emergencygency waswas was declared,declared, declared, thethe the ratioratio ratio ofof of IbarakiIbaraki Ibaraki CityCity City asasas the the farthest farthestfarthest point point was was was approximately approximately approximately 60%. 60%. 60%. However, However, However, in in the inthe thesecond second second week week week of of April April of April 2020, 2020, 2020,afterafter the afterthe state state the of stateof emergency emergency of emergency was was declared, declared, was declared, this this ratio ratio this increased increased ratio increased to to approximately approximately to approximately 65%. 65%. Figure Figure 65%. 7 7 Figureshowsshows7 that thatshows the the ratio thatratio theof of Takatsuki, ratioTakatsuki, of Takatsuki, Suita, Suita, Se Se Suita,ttsu,ttsu, and Settsu,and Minoh Minoh and Cities, MinohCities, which Cities,which neighbor whichneighbor neighbor Ibaraki Ibaraki Ibaraki City, as the farthest point did not change significantly between April 2019 and April City,City, as as the the farthest farthest point point did did not not change change significantly significantly between between April April 2019 2019 and and April April 2020. 2020. 2020. However, travel to urban centers such as the north and central wards in Osaka City However,However, travel travel to to urban urban centers centers such such as as th thee north north and and central central ward wardss in in Osaka Osaka City City de- de- decreased significantly. creasedcreased significantly. significantly.

Figure 7. Changes in the farthest point moved from residents’ homes for each week of April 2019 and April 2020. FigureFigure 7. 7. Changes Changes in in the the farthest farthest point point moved moved from from resident residents’s’ homes homes for for each each week week of of April April 2019 2019 and and April April 2020. 2020. 4. Discussion This study clariﬁed that the HR-length decreased to approximately half during the COVID-19 pandemic in the suburban city of Ibaraki City in the Osaka Metropolitan area. Speciﬁcally, the HR-length between April 2019 and April 2020 decreased rapidly, with a Sustainability 2021, 13, 8974 8 of 11

significant difference observed. The HR^− length in 2020 was found to have decreased to approximately 50% that of 2019. Travel to the urban center of Osaka City decreased significantly because of the state of emergency, which prevented people from traveling far from their homes and only allowed them to go out for the bare minimum of daily activities. The results suggest that the COVID-19 pandemic reduced people’s home ranges on a neighborhood scale. Specifically, in the suburban city, people did not go to urban centers but rather lived in their own and neighboring cities. The results are surprising in a suburban city where many residents live in wide home ranges. The results are logically matched with two previous studies in Ibaraki City in April 2020, which is the same period and area of this research. First, Kato [14] clarified that dense human space-times were formed in the parks as well as in the stations. This means that they did not go from the stations to the urban centers but rather visited the neighborhood parks. In Ibaraki City, the information distributed by the government frequently warned of increased population density in its parks [38]. Second, Kato et al. [15] reported an increased number of bicycle trips, which suggests that people moved around the neighborhood. As in Houston and parts of Seattle, Doubleday et al. [39] found that the number of people who walked and rode bicycles increased with the government’s stay home order. In Ibaraki City, the police department reported that the number of injuries in traffic accidents related to cycling increased [40]. A limitation of this study is that it focused only on the variable of distance. Although this study confirmed that people’s travel distances to the farthest location were reduced, the activities near their homes might have increased slightly. MCP, which is the study method, could analyze the space-time area of zones that connect the outermost observation points. Kato [14] reported that dense human space-times were unexpectedly formed in the parks. The risk of infection with COVID-19 increased as they contacted others. However, in New York, Oishi et al. [41] found that the higher the walkability of districts, the lower the number of COVID-19 infections and deaths. Therefore, the place and duration of stay should be clarified in future studies. Furthermore, by including the data of infected individuals, causal relationships between the number of infected people and home ranges could be examined.

5. Conclusions The study’s results show that the HR-length decreased to approximately half during the state of emergency, which suggests that we should consider the design of new neighborhood environments after the COVID-19 pandemic era. Paris [10] proposed 15 min cities, and Portland [42] proposed 20 min neighborhoods. Japan, with its aging society, also needs to redesign its cities strategically. Such a design is expected to contribute to Goal 11 of the Sustainable Development Goals (SDGs): “Make cities and human settlements inclusive, safe, resilient and sustainable.” The COVID-19 pandemic has indeed accelerated the need to enrich neighborhood environments. As Japan’s population continues to decline, neighborhood stores and hospitals have tended to cluster around urban centers and neighborhood environments are beginning to lack necessary urban facilities. However, to support the lives of the increasing number of older adults, walkable neighborhoods are essential in planning a “community-based integrated care system” [2]. In the short term, during the COVID-19 pandemic, placemaking projects were practical for enriched neighborhood environments. It has been noted that “with community-based participation at its center, an effective placemaking process capitalizes on a local community’s assets, inspiration, and potential, and it results in the creation of quality public spaces that contribute to people’s health, happiness, and well- being” [43]. During the COVID-19 pandemic, well managed outdoor placemaking projects were effective in preventing COVID-19 infection [44]. Short-term placemaking projects are more effective in collaboration with local governments and citizens. In the long term (i.e., after the COVID-19 pandemic), in order to maintain the citizens’ daily activities, there is a need for urban planning that diversiﬁes the land use of the neighborhood environment. Sustainability 2021, 13, 8974 9 of 11

The central government needs to take the initiative in changing long-term urban planning as part of a national strategy. It is essential to have urban facilities such as stores and hospitals within walking distance. Though cities with growing populations have simpliﬁed land uses, shrinking cities need to diversify land uses for walkable neighborhoods [4]. Worldwide, the number of infected people has continued to ﬂuctuate, and the number of new variants of SARS-CoV-2 has been increasing. The impacts of the COVID-19 pandemic are predicted to be long-lasting, and we will continue to live in coexistence with the virus for some years. In April 2021, the Japanese government declared a third state of emergency in Osaka Prefecture. A limitation of this research is that we only compared April 2019 and April 2020. Another limitation of this research is that we only analyzed Ibaraki City. The residents’ home ranges might constantly be changing, depending on the infection situation. After the COVID-19 pandemic era, future studies should research the design of walkable neighborhoods, increasing the research periods and areas after the state of emergency.

Author Contributions: Conceptualization, H.K.; methodology, H.K.; software, H.K.; validation, H.K., A.T. and D.M.; formal analysis, H.K.; investigation, H.K.; resources, H.K.; data curation, H.K.; writing—original draft preparation, H.K.; writing—review and editing, H.K., A.T. and D.M.; visualization, H.K.; supervision, H.K.; project administration, H.K.; funding acquisition, H.K. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the JSPS KAKENHI (grant number 21K14318). Institutional Review Board Statement: The protocol of this study was approved by the Agoop Corporation, which provides anonymized LH data. This study complies with the “Guidelines for the Use of Device Location Data,” a common regulation for location data in Japan. Informed Consent Statement: Informed consent was obtained from all subjects, all of whom installed particular applications based on the type of data to be collected, the purpose of use, the provision to third parties, and the privacy policy. Additionally, the subjects can stop sending the GPS location data at any time by changing their mobile phones’ settings. Data Availability Statement: The data of this study cannot be shared publicly due to mobile phone users’ confidentiality. GPS location history panel data are available to researchers and governments through agreements with the Agoop Corporation. By establishing contacts with the corporation, vari- ous stakeholders can access and utilize LH data for research purposes according to the “Guidelines for the Use of Device Location Data” to protect the privacy of users’ GPS location history. Acknowledgments: The authors are deeply grateful for the support of the Urban Development Department of the Ibaraki City Government (Local Government in the Osaka Metropolitan area). Conflicts of Interest: The authors declare no conflict of interest.

References 1. Statistics Bureau of Japan. Statistical Handbook of Japan 2020. Available online: https://www.stat.go.jp/english/data/ handbook/c0117.html (accessed on 5 August 2021). 2. Japanese MHLW (Ministry of Health, Labour, and Welfare). Establishing “the Community-Based Integrated Care System.”. Available online: https://www.mhlw.go.jp/english/policy/care-welfare/care-welfare-elderly/dl/establish_e.pdf (accessed on 30 June 2021). 3. Burt, W.H. Territoriality and home range concepts as applied to mammals. J. Mammal. 1943, 24, 346–352. [CrossRef] 4. Kato, H. How Does the Location of Urban Facilities Affect the Forecasted Population Change in the Osaka Metropolitan Fringe Area? Sustainability 2021, 13, 110. [CrossRef] 5. Kato, H.; Kanki, K. Development of walkability indicator for visualising smart shrinking—A case study of sprawl areas in North Osaka Metropolitan Region. Int. Rev. Spat. Plan. Sustain. Dev. 2020, 8, 39–58. [CrossRef] 6. Kato, H. Effect of Walkability on Urban Sustainability in the Osaka Metropolitan Fringe Area. Sustainability 2020, 12, 9248. [CrossRef] 7. Cerin, E.; Saelens, B.E.; Sallis, J.F.; Frank, L.D. Neighborhood environment walkability scale: Validity and development of a short form. Med. Sci. Sports Exerc. 2006, 38, 1682–1691. [CrossRef] 8. Inoue, S.; Murase, N.; Shimomitsu, T.; Ohya, Y.; Odagiri, Y.; Takamiya, T.; Ishii, K.; Katsumura, T.; Sallis, J.F. Association of physical activity and neighborhood environment among Japanese adults. Prev. Med. 2009, 48, 321–325. [CrossRef] Sustainability 2021, 13, 8974 10 of 11

9. Chen, B.-I.; Hsueh, M.-C.; Rutherford, R.; Park, J.-H.; Liao, Y. The associations between neighborhood walkability attributes and objectively measured physical activity in older adults. PLoS ONE 2019, 14, e0222268. [CrossRef] 10. World Economic Forum. Paris Is Planning to Become a ‘15-minute City’. Available online: https://www.weforum.org/videos/ paris-is-planning-to-become-a-15-minute-city-897c12513b (accessed on 2 April 2021). 11. Balletto, G.; Ladu, M.; Milesi, A.; Borruso, G. A Methodological Approach on Disused Public Properties in the 15-Minute City Perspective. Sustainability 2021, 13, 593. [CrossRef] 12. Prime Minister of Japan and His Cabinet. [COVID-19] Declaration of a state of emergency in response to the novel coronavirus disease. Kantei. 7 April 2020. Available online: https://japan.kantei.go.jp/ongoingtopics/_00018.html (accessed on 12 April 2021). 13. Osaka Prefecture. COVID-19 Task Force website. Latest Updates on COVID-19 in Osaka. Available online: https://covid19- osaka.info/en (accessed on 13 June 2021). 14. Kato, H. Development of a Spatio-temporal Analysis Method to Support the Prevention of COVID-19 Infection: Space-Time Kernel Density Estimation Using GPS Location History Data. In Urban Informatics and Future Cities; Geertman, S., Pettit, C., Goodspeed, R., Staffans, A., Eds.; Springer Nature: Cham, Switzerland, 2021; pp. 51–67. ISBN 978-3-030-76058-8. [CrossRef] 15. Kato, H.; Matsushita, D. Changes in Walkable Streets during the COVID-19 Pandemic in a Suburban City in the Osaka Metropolitan Area. Sustainability 2021, 13, 7442. [CrossRef] 16. Muto, K.; Yamamoto, I.; Nagasu, M.; Tanaka, M.; Wada, K. Japanese citizens’ behavioral changes and preparedness against COVID-19: An online survey during the early phase of the pandemic. PLoS ONE 2020, 15, e0234292. [CrossRef] 17. Lee, Y.H.; Lee, J.S.; Baek, S.C.; Hong, W.H. Spatial Equity with Census Population Data vs. Floating Population Data: The Distribution of Earthquake Evacuation Shelters in Daegu, South Korea. Sustainability 2020, 12, 8046. [CrossRef] 18. Oliver, N.; Lepri, B.; Sterly, H.; Lambiotte, R.; Deletaille, S.; De Nadai, M.; Letouzé, E.; Salah, A.A.; Benjamins, R.; Cattuto, C. Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle. Sci. Adv. 2020, 6, eabc0764. [CrossRef] 19. Jia, J.S.S.; Lu, X.; Yuan, Y.; Xu, G.; Jia, J.M.; Christakis, N.A. Population flow drives spatio-temporal distribution of COVID-19 in China. Nature 2020, 582, 389–394. [CrossRef] 20. Badr, H.S.; Du, H.R.; Marshall, M.; Dong, E.S.; Squire, M.M.; Gardner, L.M. Association between mobility patterns and COVID-19 transmission in the USA: A mathematical modelling study. Lancet Infect. Dis. 2020, 20, 1247–1254. [CrossRef] 21. Chang, S.; Pierson, E.; Koh, P.W.; Gerardin, J.; Redbird, B.; Grusky, D.; Leskovec, J. Mobility network models of COVID-19 explain inequities and inform reopening. Nature 2021, 589, 82-U54. [CrossRef][PubMed] 22. Lee, M.; Zhao, J.; Sun, Q.; Pan, Y.; Zhou, W.; Xiong, C.; Zhang, L. Human mobility trends during the early stage of the COVID-19 pandemic in the United States. PLoS ONE 2020, 15, e0241468. [CrossRef] 23. The Japan Times. The Coronavirus and Japan’s Constitution. 14 April 2020. Available online: https://www.japantimes.co.jp/ opinion/2020/04/14/commentary/japan-commentary/coronavirus-japans-constitution/ (accessed on 9 June 2021). 24. Japanese Law Translation. Outline of the Act Partially Amending the Act on Special Measures against Novel Influenza, etc., and Other Relevant Acts. Available online: http://www.japaneselawtranslation.go.jp/common/data/outline/210621192151_905R2 07.pdf (accessed on 1 July 2021). 25. Anzai, A.; Nishiura, H. “Go To Travel” Campaign and Travel-Associated Coronavirus Disease 2019 Cases: A Descriptive Analysis, July–August 2020. J. Clin. Med. 2021, 10, 398. [CrossRef][PubMed] 26. Yabe, T.; Tsubouchi, K.; Fujiwara, N.; Wada, T.; Sekimoto, Y.; Ukkusuri, S.V. Non-compulsory measures sufficiently reduced human mobility in Tokyo during the COVID-19 epidemic. Sci. Rep. 2020, 10, 18053. [CrossRef][PubMed] 27. Mizuno Laboratory. COVID-19 Special Site: Visualization of the Percentage of People Who Refrain from Going Out. National Institute of Informatics. Available online: http://research.nii.ac.jp/~{}mizuno/ (accessed on 9 April 2021). 28. Agoop Corporation. Homepage. Available online: https://www.agoop.co.jp/en/ (accessed on 12 April 2021). 29. Ministry of Health, Labour and Welfare (MHLW). Press: Ministry of Health, Labour and Welfare and Agoop Sign Agreement on Provision of Information Conducive to COVID-19 Cluster Response. Available online: https://www.mhlw.go.jp/stf/newpage_ 11116.html (accessed on 9 June 2021). (In Japanese) 30. LBMA Japan. Guidelines for the Use of Device Location Data. Available online: https://www.lbmajapan.com/guideline (accessed on 12 April 2021). (In Japanese). 31. Agoop. Application “WalkCoin.”. Available online: https://www.agoop.co.jp/appslib/walkcoin/ (accessed on 4 July 2021). (In Japanese). 32. Ichinose, R.; Maruyama, Y.; Nagata, S. Estimation of the number of floating population based on location data collected from smartphones. J. Jpn. Soc. Civ. Eng. 2018, 74, I_210–I_219. [CrossRef] 33. Osaka Prefecture Government. Outline of Emergency Measures of Osaka Prefecture. Available online: http://www.pref.osaka.lg. jp/attach/38687/00000000/0407_(EN)Emergency%20Measures%20of%20Osaka%20Prefecture.pdf (accessed on 12 April 2021). 34. Osaka Prefecture Government. Request for Facility Use Restrictions. Available online: http://www.pref.osaka.lg.jp/attach/3868 7/00000000/Revised(EN)Facility%20Use%20Restrictions%20.pdf (accessed on 9 April 2021). 35. Worton, B.J. Kernel Methods for Estimating the Utilization Distribution in Home-Range Studies. Ecology 1989, 70, 164–168. [CrossRef] Sustainability 2021, 13, 8974 11 of 11

36. Worton, B.J. Using Monte Carlo Simulation to Evaluate Kernel-Based Home Range Estimators. J. Wildl. Manag. 1995, 59, 794–800. [CrossRef] 37. Anderson, D.J. The Home Range—A New Nonparametric-Estimation Technique. Ecology 1982, 63, 103–112. [CrossRef] 38. Ibaraki City Government. Requests for Use of the Park in Conjunction with Measures to Prevent the Spread of COVID-19. 1 June 2020. Available online: https://www.city.ibaraki.osaka.jp/saigai/shingatacoronavirusinformation/oshirase/kouen/47738.html (accessed on 25 September 2020). (In Japanese). 39. Doubleday, A.; Choe, Y.; Isaksen, T.B.; Miles, S.; Errett, N.A. How did outdoor biking and walking change during COVID-19?: A case study of three US cities. PLoS ONE 2021, 16, e0245514. [CrossRef] 40. Osaka Prefectural Police. Location of Traffic Accidents in the Southern Part of Ibaraki City [Map of All Traffic Accidents]. Available online: https://www.police.pref.osaka.lg.jp/kotsu/jiko/3/1/10143.html (accessed on 5 April 2021). (In Japanese). 41. Oishi, S.; Cha, Y.J.; Schimmack, U. The Social Ecology of COVID-19 Cases and Deaths in New York City: The Role of Walkability, Wealth, and Race. Soc. Psychol. Personal. Sci. 2021, 10, 1948550620979259. [CrossRef] 42. Portland Plan. 20-minute Neighborhoods. Available online: https://www.portlandonline.com/portlandplan/index.cfm?a=2880 98&c=52256 (accessed on 5 April 2021). 43. Project for Public Spaces. PLACEMAKING—What If We Built Our Cities around Places? Available online: https://www.pps. org/wp-content/uploads/2016/10/Oct-2016-placemaking-booklet.pdf (accessed on 4 July 2021). 44. Project for Public Spaces. You Asked, We Answered: How Can Public Space Managers Help Fight COVID-19? Available online: https://www.pps.org/article/you-asked-we-answered-how-can-public-space-managers-help-fight-covid-19 (accessed on 4 July 2021).