Spatiotemporal Analysis for COVID-19 Delta Variant Using GIS-Based Air Parameter and Spatial Modeling
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Dataset
2.3. Spatial Analytic Method
2.3.1. Calculating Geographic Distribution
2.3.2. Spatially Integrated Statistics
2.3.3. GWR (Geographically Weighted Regression) Model
2.3.4. Ordinary Least Squares (OLS)
2.4. Air Pollutant Concentration Due to COVID-19
3. Results
3.1. Hotspot Clustering
3.2. Weighted Mean Center (WMC)
3.3. Directional Distribution (DD)
3.4. Spatial Clustering
3.5. Geographically Weighted Regression (GWR)
3.6. Air Pollution Levels Due to COVID-19 Lockdown
3.7. Relationship between COVID−19 Incidence and Air Pollutant
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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No | Data | Source |
---|---|---|
1 | Population | Badan Pusat Statistik Surabaya 2020 |
2 | Population density | Badan Pusat Statistik Surabaya 2020 |
3 | Surabaya administrative boundaries (city and village) | Badan Informasi Geospasial dan Open Street Map |
4 | COVID-19 daily data (confirmed, recovered, suspect) | https://lawancovid-19.surabaya.go.id/ (30 June 2021) |
5 | Air Pollution (NO2, SO2, O3, and CO) | Google Earth Engine (Sentinel-5P) |
Week | Date | Length (km) | Width (km) | Area (km2) | Rotation |
---|---|---|---|---|---|
1 | 28 April 2021–4 May 2021 | 4.94367 | 1.75906 | 27.32002 | 64.34984 |
2 | 5 May 2021–11 May 2021 | 5.96700 | 2.43495 | 45.64533 | 104.39636 |
3 | 12 May 2021–19 May 2021 | 6.10525 | 3.35143 | 64.28118 | 95.09706 |
4 | 20 May 2021–25 May 2021 | 5.32776 | 4.12860 | 69.10306 | 96.81598 |
5 | 26 May 2021–1 June 2021 | 7.19094 | 2.99919 | 67.75481 | 73.92413 |
6 | 2 June 2021–8 June 2021 | 8.20458 | 2.37165 | 61.13025 | 104.63646 |
7 | 9 June 2021–15 June 2021 | 6.10076 | 1.34384 | 25.75626 | 49.12235 |
8 | 16 June 2021–22 June 2021 | 10.37618 | 4.00459 | 130.54036 | 123.65276 |
9 | 23 June 2021–29 June 2021 | 3.62656 | 0.73228 | 8.34304 | 62.94312 |
10 | 30 June 2021–6 July 2021 | 5.08497 | 6.69721 | 106.98736 | 42.41460 |
11 | 7 July 2021–13 July 2021 | 5.47192 | 3.65635 | 62.85471 | 120.54456 |
12 | 14 July 2021–20 July 2021 | 3.48135 | 4.07568 | 44.57561 | 9.36094 |
13 | 21 July 2021–27 July 2021 | 5.42577 | 3.75721 | 64.04384 | 90.09924 |
14 | 28 July 2021–3 August 2021 | 7.00457 | 5.80578 | 127.75903 | 100.71435 |
15 | 4 August 2021–10 August 2021 | 7.38358 | 3.36916 | 78.15178 | 102.26742 |
16 | 11 August 2021–17 August 2021 | 7.12187 | 3.01337 | 67.42110 | 59.90758 |
17 | 18 August 2021–24 August 2021 | 3.24594 | 2.13313 | 21.75242 | 120.95107 |
18 | 25 August 2021–31 August 2021 | 10.26110 | 1.89992 | 61.24630 | 96.76604 |
19 | 1 September 2021–7 September 2021 | 7.91130 | 1.57939 | 39.25427 | 93.08196 |
20 | 8 September 2021–14 September 2021 | 7.65780 | 1.92917 | 46.41125 | 78.09290 |
21 | 15 September 2021–21 September 2021 | 5.44430 | 1.21007 | 20.69670 | 48.95904 |
22 | 22 September 2021–28 September 2021 | 1.98095 | 9.99348 | 62.19288 | 139.57942 |
23 | 29 September 2021–5 October 2021 | 1.88549 | 6.52740 | 38.66456 | 33.70470 |
24 | 4 October 2021–12 October 2021 | 0.98472 | 1.98604 | 6.14400 | 143.11851 |
25 | 13 October 2021–19 October 2021 | 1.87011 | 4.72545 | 27.76268 | 177.96124 |
26 | 20 October 2021–26 October 2021 | 0.96843 | 2.19412 | 6.67537 | 0.80506 |
Week | Estimated Coefficient | Standard Error | R2 | ||||
---|---|---|---|---|---|---|---|
Pop_Density | Recovery | Suspect | Pop_Density | Recovery | Suspect | ||
1 | −0.000006 | −0.00145 | 0.5942 | 0.000004 | 0.00081 | 0.0827 | 0.34 |
2 | −0.000006 | 1.58807 | −0.3376 | 0.000005 | 0.44246 | 0.6092 | 0.08 |
3 | −0.000006 | 1.44143 | 0.7252 | 0.000007 | 0.50798 | 1.1458 | 0.37 |
4 | −0.000003 | 0.9533 | −0.1323 | 0.000005 | 0.46152 | 0.7389 | 0.14 |
5 | −0.000012 | 1.76234 | 0.435 | 0.000005 | 0.50614 | 0.8879 | 0.11 |
6 | −0.000008 | 1.62216 | −0.1898 | 0.000009 | 0.61543 | 0.3095 | 0.32 |
7 | −0.000001 | 0.23091 | −0.2002 | 0.000007 | 0.52738 | 0.174 | 0.01 |
8 | −0.000008 | 0.35447 | −0.0628 | 0.000009 | 0.48646 | 0.1758 | 0.01 |
9 | 0.00001 | −0.00515 | 0.1042 | 0.000011 | 0.77978 | 0.1664 | 0.05 |
10 | 0.000008 | 0.07483 | −0.3727 | 0.000014 | 0.89865 | 0.3131 | 0.01 |
11 | −0.000038 | 3.56064 | −2.4404 | 0.000045 | 0.75624 | 3.2563 | 0.41 |
12 | −0.000644 | 4.32694 | −5.6831 | 0.000179 | 0.96196 | 17.562 | 0.55 |
13 | −0.000467 | 1.13803 | −1.3819 | 0.000115 | 0.30656 | 2.2285 | 0.30 |
14 | −0.000347 | 1.40317 | −1.176 | 0.000143 | 0.33388 | 1.2351 | 0.46 |
15 | −0.000306 | 0.84206 | −6.6564 | 0.000133 | 0.38841 | 13.029 | 0.43 |
16 | −0.000228 | 1.87945 | −5.2903 | 0.00011 | 0.37022 | 12.44 | 0.72 |
17 | −0.000129 | 3.09936 | 10.063 | 0.00015 | 0.62592 | 18.485 | 0.68 |
18 | 0.00005 | 3.03727 | 3.4316 | 0.000038 | 0.3922 | 0.4289 | 0.89 |
19 | −0.000056 | 3.83066 | 4.0997 | 0.00003 | 0.60965 | 0.7658 | 0.86 |
20 | −0.000035 | 3.80442 | 4.3494 | 0.000015 | 0.51624 | 0.6211 | 0.85 |
21 | −0.000007 | 4.4022 | 4.9018 | 0.000011 | 0.54769 | 0.6049 | 0.88 |
22 | −0.000026 | 5.63802 | 5.2415 | 0.000014 | 0.73245 | 0.7864 | 0.94 |
23 | −0.000014 | 4.5608 | 5.9564 | 0.000008 | 0.49729 | 0.6306 | 0.79 |
24 | −0.000008 | 4.43153 | 4.6766 | 0.000003 | 0.38015 | 0.4695 | 0.76 |
25 | −0.000004 | 4.37876 | 4.3851 | 0.000003 | 0.42768 | 0.5624 | 0.72 |
26 | −0.000006 | 6.40907 | 7.2381 | 0.000004 | 0.38539 | 0.7607 | 0.96 |
Week | r | R | Coefficient | |||
---|---|---|---|---|---|---|
O3 | CO | SO2 | NO2 | |||
1 | 0.059 | 0.243 | 41.078 | −115.823 | 2196.099 | −8845.032 |
2 | 0.035 | 0.188 | 183.530 | −9.763 | −908.882 | −3114.051 |
3 | 0.006 | 0.077 | −294.505 | 21.238 | −455.957 | −976.079 |
4 | 0.069 | 0.262 | 114.451 | 163.894 | −774.772 | −7748.330 |
5 | 0.074 | 0.272 | −148.850 | 155.290 | 191.222 | −16,298.614 |
6 | 0.084 | 0.289 | 1564.527 | −237.265 | 1601.451 | 4634.166 |
7 | 0.015 | 0.123 | 33.325 | 119.347 | −200.555 | −2408.472 |
8 | 0.023 | 0.151 | −381.530 | −20.501 | 843.237 | 7701.886 |
9 | 0.011 | 0.107 | −511.875 | 26.044 | −420.067 | 7561.081 |
10 | 0.019 | 0.137 | 1464.636 | 219.159 | 795.614 | 2537.760 |
11 | 0.103 | 0.322 | 4849.546 | 1036.376 | 6103.810 | −223,496.785 |
12 | 0.333 | 0.577 | −31,165.367 | 4160.563 | 88,138.945 | −1,199,337.944 |
13 | 0.123 | 0.350 | 27,404.369 | −13,248.590 | −6139.623 | −13,213.022 |
14 | 0.070 | 0.265 | 2107.077 | 5268.257 | 23,460.160 | −614,879.134 |
15 | 0.235 | 0.485 | 6979.684 | −4141.370 | −43,595.925 | −328,846.454 |
16 | 0.008 | 0.092 | 1472.020 | 3265.895 | 3536.649 | −336,158.858 |
17 | 0.117 | 0.341 | −13,664.796 | −481.837 | 5318.525 | −106,384.125 |
18 | 0.054 | 0.233 | −4594.035 | 424.550 | 4146.287 | −24,345.855 |
19 | 0.054 | 0.233 | −2347.285 | 276.720 | −462.544 | −24,110.595 |
20 | 0.021 | 0.146 | 452.573 | −44.539 | −1317.076 | 14,171.958 |
21 | 0.064 | 0.253 | 396.631 | −310.415 | 370.013 | −15,564.079 |
22 | 0.047 | 0.216 | 971.238 | −222.039 | −2481.582 | 36,013.294 |
23 | 0.028 | 0.166 | −741.397 | −27.302 | 1313.003 | 556.694 |
24 | 0.082 | 0.286 | 442.884 | 95.709 | −204.715 | −4617.635 |
25 | 0.040 | 0.199 | −335.258 | 23.522 | −760.015 | −636.684 |
26 | 0.027 | 0.166 | 159.888 | 88.328 | −593.480 | 3458.184 |
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Cahyadi, M.N.; Handayani, H.H.; Warmadewanthi, I.; Rokhmana, C.A.; Sulistiawan, S.S.; Waloedjo, C.S.; Raharjo, A.B.; Endroyono; Atok, M.; Navisa, S.C.; et al. Spatiotemporal Analysis for COVID-19 Delta Variant Using GIS-Based Air Parameter and Spatial Modeling. Int. J. Environ. Res. Public Health 2022, 19, 1614. https://doi.org/10.3390/ijerph19031614
Cahyadi MN, Handayani HH, Warmadewanthi I, Rokhmana CA, Sulistiawan SS, Waloedjo CS, Raharjo AB, Endroyono, Atok M, Navisa SC, et al. Spatiotemporal Analysis for COVID-19 Delta Variant Using GIS-Based Air Parameter and Spatial Modeling. International Journal of Environmental Research and Public Health. 2022; 19(3):1614. https://doi.org/10.3390/ijerph19031614
Chicago/Turabian StyleCahyadi, Mokhamad Nur, Hepi Hapsari Handayani, IDAA Warmadewanthi, Catur Aries Rokhmana, Soni Sunarso Sulistiawan, Christrijogo Sumartono Waloedjo, Agus Budi Raharjo, Endroyono, Mohamad Atok, Shilvy Choiriyatun Navisa, and et al. 2022. "Spatiotemporal Analysis for COVID-19 Delta Variant Using GIS-Based Air Parameter and Spatial Modeling" International Journal of Environmental Research and Public Health 19, no. 3: 1614. https://doi.org/10.3390/ijerph19031614
APA StyleCahyadi, M. N., Handayani, H. H., Warmadewanthi, I., Rokhmana, C. A., Sulistiawan, S. S., Waloedjo, C. S., Raharjo, A. B., Endroyono, Atok, M., Navisa, S. C., Wulansari, M., & Jin, S. (2022). Spatiotemporal Analysis for COVID-19 Delta Variant Using GIS-Based Air Parameter and Spatial Modeling. International Journal of Environmental Research and Public Health, 19(3), 1614. https://doi.org/10.3390/ijerph19031614