Spatiotemporal Analysis using Google Earth Engine: An Evaluation of Covid-19 Emergency Response Mobility Policies in Java Island
Indonesia
M. Iqbal
1, I.W. Prabaswara
1, V.A Nurlita
2, D.R. Hizbaron
11
Department of Environmental Geography, Faculty of Geography, Universitas Gadjah Mada, Gedung D, 2
ndfloor, Sekip Utara, Bulaksumur, Yogyakarta 55281, [email protected]
2
Department of Science and Information Geography, Faculty of Geography, Universitas Gadjah Mada, Gedung D, 2
ndfloor, Sekip Utara, Bulaksumur, Yogyakarta 55281, [email protected]
Corresponding author’s:
[email protected]
ABSTRACT
The term of Mudik (in Bahasa) is often interpreted a s the return of migra nts from foreign areas to their hometowns, or la rgely known as massive mobility among regions that especially carried out annually during the Eid a l-Fitr holiday in Indonesia. However, due to the COVID-19 pandemic occurred in 2020 and 2021, restrictions were placed on mudik during the Eid holida y. This study wa s conducted to see the extent of the effectiveness of the mudik restrictions carried out by the Indonesian government. This study was conducted by reviewing the levels of NO2 a nd CO in the months before and during the Eid a l-Fitr holida y through spatiotemporal processing of images retrieved from Google Ea rth Engine. The data used is Sentinel-5p images to map air pollution levels from NO2 values a nd CO values in Ja nuary-June 2019-2021. The study area includes two districts in DKI Ja karta Province and two districts in Central Ja va Province. The statistical tests a re useful to see the trend of data that is obtained from the zonal a nalysis process. The statistical tests were ca rried out using the Mann- Kenda ll Test method to detect trends and the results were equipped with Sen's Slope a nalysis to measure the magnitude of the cha nges that occurred. According to the trend of NO2 a nd CO va lues obtained, the va lues in 2019 a re higher than in 2021, a nd the values in 2021 are higher than in 2020. Thus, the policy of mudik restrictions in 2020 is a ssumed more effectively than in 2021. The trend in the levels of NO2 a nd CO in the air is more significa nt a month before the Eid al-Fitr holida y than in the Eid a l-Fitr holida y. It can illustra te that the production of NO2 a nd CO from motor vehicles continues to increa se before there a re restrictions.
Keywords: mudik, mobility, Google Earth Engine, COVID-19, Indonesia
1. INTRODUCTION
Homecoming or Mudik (in Bahasa) has been a tradition among Indonesian, especially during holidays like Eid Al-Fitr holida y. It's a famous holiday as most people will come from their working city to their childhood city or villa ges.
Few other people may choose to spent Eid Al-Fitr holida y with their fa mily on a va cation. During the period of homecoming, the mobility likely to increa se, a ccumulated groups of family coming over, social ga thering increasing, and such event predictably mounting the potential spreading of COVID-19 virus. When the COVID-19 pandemic hit, the government decided to prohibit all forms of mobiliza tion that risk increasing the spread of the COVID-19 virus, including homecoming. However, this prohibition does not necessarily prevent people from homecoming. There a re still many people who a re trying to find a wa y to go home by a voiding the blockade. This is certa inly bad news for the handling of the COVID-19 pandemic. Aside from such phenomena, this policy wa s contested to have a dequate efficacy to suppress the spiking number of the COVID-19 cases a fter the Eid Al-Fitr holida y. Doubts a risen to the effectiveness of Mudik
restriction, especially when comparing Mudik restriction in 2020 and 2021. Some a rgue that the achievement of Mudik restriction tends to decrease in 2021 compared to 2020.
In 2021, Mudik restriction policy a pplies from 6-17 May 2021, a s stated in Sura t Edaran (SE) Sa tgas Penanganan COVID-19 No.13 Tahun 2021 tentang Peniadaan Mudik Hari Ra ya Lebaran. The regulation turned out to be unable to stem the enthusiasm of the community to do homecoming. The causes include the desire to spent Eid Al-Fitr holida y with their fa mily or because they have lost their job in the city due to the COVID-19 pandemic [1]. At many blocking points, there a re many homecomings a ble to pass the barrier due to the la ck of personnel. Consequently, from a ra ndom test to 6,472 tra vellers a t 381 blocking points, a s many as 4,123 people tested positive for COVID-19. Meanwhile, when doing sprea d ra te comparison cases before and after the Mudik restriction policy. On May 6, 2021 number of positive people is 1,697,305 people a nd 46,496 people died. While Ma y 19, 2021, positive case was a total of 1,753,101 people and who died a total of 48,669 person [1]. This significant increase shows that the Mudik restriction policy is still not optimal. When compared to 2021, the duration of the Mudik restriction in 2020 is longer than 2021, namely from 24 May-7 June 2020, as sta ted in Pera turan Menteri Perhubungan No. PM 25 Tahun 2020 tentang Pengendalia n Transportasi Selama Masa Mudik Idul Fitri Ta hun 1441 Hijriah.
This study was conducted to evaluate the “mudik” restriction during the COVID-19 pandemic, especially during the Eid Al-Fitr holida y in 2021 than Eid Al-Fitr holida y in 2020. This study tried to evaluate the effectiveness of Mudik restriction from the perspective of remote sensing. The a ssumption is tha t a n increase in a ir pollution means an increase in the ra te of homecoming movement.
2. THEORETICAL REVIEW
Theoretically, this study a ddresses the theoretical problem of how to make use of technology-based, nearly-real time data to monitor the efficacy of “mudik” policy during the COVID-19 pandemic in Indonesia. Technically, the mobiliza tion pattern is reflected through sa tellite image, a nd remote sensing rela ted spatial sta tistics observation which ma kes possible to interpret it. The use of technology central to help the decision ma ker to eva luate the implemented regula tion [2][3][4]. Italy, Portugal, Australia, a nd many countries in East Asia make use technology to monitor the citizen beha viour during pandemic situation, including Indonesia [5][6]. The Google Mobility Report a lso documented most of mobilities a risen in ea ch of the essential urba n destination. The report generally shown increasing number of visits to groceries a rea s, while the destination to settlement, offices, a nd urban amenities a re decreasing sha rply. The technology- ba sed observation using nea rly real time data assist government to create enabling environment, to a dapt with current situa tion a nd promote better governance to COVID-19 [5][7][8][9][10]. In more detail, this study is a ctually trying to see how the mobilization pattern among regions.
The previous study working with COVID-19 mostly discussed the releva nt method to improve a daptive governance during pandemic situations. Few works have been presented to ela borate the use of remote sensing a nd spatial sta tistical methods. Hereafter, the study tried to present a strong sense on the use of nearly real time data retrieved from remote sensing pla tforms, the structured a nalysis using a series of spatial a nd statistics techniques, a nd the challenging a pproach on pollution indication to mobiliza tion pattern.
Popula tion mobility such as mudik could increase greenhouse ga sses like NO2 a nd CO [11][12][13]. Mainly, those ga sses emitted from private transportation modes such as private cars [14][15]. According to the stated greenhouse ga sses, those ga sses produced by combustion of fuel in the engine. In the Island of Java, private cars a re one of the most popular types of tra nsportation used during mudik season, thus triggering the increa ses of NO2 a nd CO. With such conditions, monitoring population mobility using the record of NO2 a nd CO a s variables in this study.
This study utilized Google Earth Engine (GEE) to monitor greenhouse gasses emission. GEE is a cloud computing pla tform that allows to store a nd process geospatial data and satellite data a s a basis for decision making. In this study, GEE is used a s a tool to obtain secondary data a s well a s a tool to process data. However, not all da ta is a vailable in GEE, some data such a s particulate matter (PM) data are not a vaila ble, so this study only suffices for CO and NO2 data as the ma in exhaust from motor vehicles. Google Ea rth Engine is used a s a pla tform for processing Sentinel-5 P ima ge data and
its visua liza tion. Meanwhile, the a verage value of each variable in ea ch study a rea was tested using statistical tests in the form of Man-Kendall a nd Sens Slope.
Figure.1 Location Map of The Study Area
The selection of the study area wa s based on the number of COVID-19 spikes and the number of vehicles in Java, especia lly on Pa ntura routes. From the existing parameters, for DKI Jakarta, a reas that qualified are East Jakarta a nd North Ja karta. While from Central Ja va, the qualified a reas are Semarang a nd Kudus.
3. DATA & METHOD
Da ta used in this study is secondary data, derived from Sentinel-5 P image that was processed in Google Earth Engine. Objects/variables that a re used are NO2 a nd CO levels of the study area which includes East Jakarta, North Jakarta, Sema rang, a nd Kudus in Ja nuary-June for each year between 2019-2021. The study steps carried out a re a s follows on the dia gra m:
Figure.2 Workflow Flowchart
In genera l, sta tistical tests provide information about the significance of increase or decrease of the result then it will be necessary to confirm that the experiment had a fair cha nce of establishing a n increase had there been one present to esta blish [16]. In this study, zonal a nalysis is ca rried out on data in Ja nuary-June 2019, 2020, a nd 2021. The statistical tests a re useful to see the trend of data that is obtained from the zonal a nalysis process. The statistical test method that is used to detect trends in this study is Ma nn-Kendall with further a nalysis using Sen’s Slope.
3.1 Mann-Kendall
The sta tistics of trend in long time series da tasets a re evaluated using Ma nn -Kendall trend a nalysis technique. Mann-Kendall test is a flexible method for significant trends in these datasets. For a series of non- pa rametric observations by Mann-Kendall, it is important to know whether the time series dataset is going upward, downward, or staying the same [17]. It can be used to detect monotonic trends in long time series datasets with different domains.
Ma nn-Kendall is used to test of significa nce where the 𝑥 varia ble is a time a s a test for trend and 𝑥𝑖 of a time series is a ssumed to obey the model:
𝑥𝑖= 𝑓(𝑡) + 𝜀𝑖 (1)
Where 𝑓(𝑡) is a continuous monotonic increasing function or decreasing function of time a nd 𝜀𝑖 means the same distribution with zero mean. It is a ssumed that the variance of distribution is constant in time. Ma nn-Kendall method statistic S is ca lcula ted using question:
𝑠 = ∑𝑛−1𝑖−1 ∑𝑛𝑗−𝑖+1𝑠𝑔𝑛(𝑥𝑗− 𝑥𝑖) (2)
Where n is the number of 𝑥𝑗and 𝑥𝑖, those a re the data value in time series i a nd j (j>i).
𝑠𝑔 𝑛(𝑥𝑗 − 𝑥𝑖)is the sign function as:
When the n >=10, the variance is computed by this equation:
𝑣𝑎𝑟(𝑠) =𝑛(𝑛−1)(2𝑛+5)−∑𝑛1−1𝑡𝑖(𝑡𝑖−1)(2𝑡𝑖+5)
18 (3)
When the total number of sample size is more than 10, question below is used to compute Z, va lue:
A positive va lue of z represents the upward trend while the negative value of z shows the downward trend. A significa nce value α is computed for checking upward or downward trend is found in the dataset.
if s>0 if s=0 if s<0
3.2 Sen’s Slope
Sen’s Slope estimator is used to detect and determine magnitude of trend in the datasets. Sen’s Slope estima tor procedure is a simple non-parametric a nd applied following the Mann-Kendall test in time-series data.
It ca n be calculated using the equation:
𝑄 =𝑥𝑗
𝑗 −𝑥𝑘
𝑘 (4)
If there a re n va lues of xj in the time series, the Sen’s Slope estimator is the media n of n(n-½) pairwise slopes. The hence can be calculated using this equation:
𝑄 = 𝑄(2𝑁+1)𝑖𝑓𝑁𝑖𝑠𝑜𝑑𝑑 𝑄 =1
2(𝑄2𝑁) + 𝑄(𝑁2+ 2)𝑖𝑓𝑁𝑖𝑠𝑒𝑣𝑒𝑛
4. RESULT AND DISCUSSION
The NO2 a nalyzed in this study recorded a decreasing pattern in genera l from May 2019, compared to June 2020 in North Ja karta and East Jakarta (Ta ble. 3). This phenomenon was caused by the ongoing COVID-19 pandemic which occurred in June 2020. People were hesita nt to conduct mudik a t this time due to tra vel restrictions issued by the government a nd Indonesian Ulema Council [18]. However, in June 2021, the NO2 recorded in those loca tions were increa sing. This wa s due to people being more confident in ta king the risk of COVID-19 infection because they were more prepa red compared to the year before. Moreover, many people felt the urge of mudik due to restrictions in the previous yea r. Many people were missing their family back a t home, thus forcing them to perform mudik despite COVID-19 Mudik restrictions. The data recorded from Semarang and Kudus a lso showed a comparably the sa me pattern compared to the previous two locations a ccording to the NO2 emission data (Table. 4).
The emission of CO showed the same pattern as NO2 emission for North Jakarta and East Jakarta. There was a decrea se in June 2020, and it wa s increasing in June 2021. However, data from Semarang a nd Kudus showed the opposite pa ttern compared to the previous two loca tions. This wa s ca used by the population mobility within the city of Semarang a nd Kudus as the monitoring of population mobility wa s stricter in North Jakarta a nd Ea st Jakarta. Temporarily within ea ch year, the data showed a decrease in people mobility from January until June Noth Jakarta, East Jakarta, and Kudus (Ta ble. 5). Semarang however, is the only location in which mobility wa s increasing in June compared to January.
(5) (6)
Table.1 NO2 and CO emission During Mudik In 2019, 2020, and 2021 In Study Area
Time NO2 CO
Ma y 2019
June 2020
June 2021
This study only aims to get the conclusion from the pollution data (NO2 a nd CO) in the form of total accumulation (SUM) a ccording to the a nalyzed period. The assumption is tha t if the total a ccumulation in a certain period is increased, it shows tha t there is ma ssive mobility in the study a rea. This study ha s not been a ble to expla in cross -provincial mobiliza tion by remote sensing da ta. It can be an interesting resea rch topic in the future. The selection of variables refers to va rious litera ture explaining emissions from transportation facilities, especially motorized vehicles. There are several va ria bles such a s CO, NO2, a nd PM (particulate matter). However, because the data availa ble in GEE is only CO a nd NO2
da ta, these two data are used to represent overall pollution. From the picture a bove, the study a rea in Ja karta (both East Ja karta and North Ja karta) has higher a ccumulation in NO2 a nd CO than Semarang a nd Kudus. Especially for NO2 which is seen most clearly by the ora nge color filling the a rea . While Central Ja va tends to be predominantly green. This is rea sonable considering the high population in Ja karta as the capital city.
In genera l, those variables are both rela ted to the accumulation of months in the year review. When NO2 is high in certa in months of the year, CO is a lso high in certa in months of the year, and vice versa. I f it is a ssociated with COVID- 19, it ca n be concluded that Ja karta has a higher population mobility than the study area in Central Ja va. The impact is that the number of COVID-19 sufferers/victims in Ja karta is higher due to high mobility, even though it is known that the tra nsmission of COVID-19 is increa sing the higher the mobility ra te between regions.
PSBB
PSSB Transisi (New Normal)
PPKM Mikro PPKM Multilevel
jelasin kebijakan yang diambil, ini semua di rentang 2020-2022
provinsi
Table.2 Sum and Trend of NO2 In 2019, 2020, and 2021 In Study Area
0
Table.3 Sum and Trend of CO In 2019, 2020, and 2021 In Study Area
Fig.3 Average Quarterly Mobility Changes [19]
Mobility da ta is used as a validator a s well a s a comparison of the findings in this study. The data repeated above is only Q1 in 2020 and 2021. With a la rger deviation in Q1 2021. This means that the decline in mobility a t a ll destination points decreases more in the first ha lf of 2021 than 2020. Then for Q2 2020, the deviation is the la rgest compared to Q1, Q3 a nd Q4 2020. This means that the restrictions a re very intensive in the second ha lf of 2020. This shows that the restrictions on going home in 2020 are very intensive, a s ca n be seen from the high ra te of decline in mobility (la rge devia tions). The mobility da ta a bove validates the conclusion regarding the effectiveness of the homecoming ba n from a remote sensing point of view, especially during the Eid Al-Fitr holida y which occurred in the second quarter (Q2). This positive correlation can be seen from the remote sensing point of view which shows that the values in 2019 are higher than in 2021, and the values in 2021 are higher than in 2020. It is justified by the mobility data that the very intensive restrictions occurred more effectively in Q2 of 2020 which had an impact on the low va lue of pollution, so that the assumption of the policy of mudik restrictions in 2020 is a ssumed more effectively than in 2021 is va lidated based on mobility data.
5. CONCLUSION
The observations on remote sensing da ta shows that there is a link between the prohibition of mobility in the Mudik ca se a nd the decreasing value of NO2 a nd CO in the a tmosphere. Even so many people doubt the effectiveness of the first mudik restrictions of COVID-19, which is in 2020 compared to the mudik restrictions in 2021. Ba sed on remote sensing da ta a nalysis, the mudik restriction in 2020 is more effective tha n mudik restriction in 2021, a s seen from pollution comparison that in 2021 is bigger tha n 2020. This conclusion is a lso supported by the population mobility in 2020 and
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