We present the results with a multiscale perspective. It is necessary to highlight the importance of analysing spatial patterns of COVID-19 from each case to the regional scale. Therefore, our research is based on two result levels: at a regional scale and at an intra-urban scale in the cities of Santander and Torrelavega (Community of Cantabria, Spain).
Before explaining the results, it is important to introduce our case study. The Autonomous Community of Cantabria (north region of Spain) has just over 580,000 inhabitants and a surface area of 5,300 Km2 (2,055 square miles), which represents an average regional density close to 110 inhabitants / Km2 (285 inhabitants/squared meters). However, the distribution of the population presents internal disparities between the coastal municipalities and the interior valleys ones (Fig. 2).
The capital city Santander is located in the central coastal area with 172,000 inhabitants. Santander, the biggest city of the Community of Cantabria, heads the functional urban area (FUA, identified at a European level) that includes Torrelavega, which is the second most important city. Indeed, the Santander FUA extends to 25 municipalities where live 380,000 inhabitants (just over 65% of the population of Cantabria). This polycentric hinterland Santander-Torrelavega highlights for several factors, such as: number of inhabitants, density, activities concentration, existence of main transport infrastructures and a prominent role in daily pendulum movements (commuting) between central areas and the surroundings [22].
Regarding to the general evolution of the pandemic in Cantabria, the accumulated data exceeded recently the 15,000 cases. As the Fig. 3 shows we are in the second or even third wave. Indeed, nowadays the accumulated incidence in the last fourteen days is very high, above 500 cases per 100,000 inhabitants.
Presented the general framework, the research is focused on spatial patterns of COVID-19. Therefore, next sections present the results based on geostatistical analysis from SITAR. Beginning with the statistical significance of spatial distribution of infected cases, we will continue with 3D bin results from both regional and intra-urban scales.
Exploratory results of spatial autocorrelation
The spatial autocorrelation analysis of the COVID-19 reveals that the distribution of the 13,907 cases is not random. Otherwise, the Moran´s Index confirms that the spatial pattern of COVID-19 cases is statistically significative, and it presents a clustered distribution (Fig. 4). Indeed, the z score of 6.56 (up to 2.58) implies a probability lower than 1% of a random distribution of the COVID-19 cases.
It is interesting to mention that we explored the Moran´s Index including elderly homes cases obtaining that, although the spatial pattern was not random, nevertheless the p value increased from 0 to 0.19 and the probability of not random distribution was less clear (under 5% instead of under 1% without retirement homes cases). These comparative results (with and without considering elderly homes) confirm the importance of excluding retirement homes cases to avoid the distortion of statistical results. Moreover, in the cases of elderly homes, we point out that COVID-19 outbreaks and spatial focus are joined or linked, while in the rest of the cases, the spatial association does not exist.
Additionally, other analysis has been applied to contrast the Moran´s Index results. Specifically, the nearest neighbour distance confirms the nonrandom spatial pattern of COVID-19, which is coincident with a clustered model. In the nearest neighbour analysis, as the Table 1 shows, the average observed distance among cases is 38.7 metres at a regional level and the Z score (standard deviation) is -201.58 (under − 2.58), therefore the spatial pattern is clustered and not random with a confidence again greater than 99%.
Table 1
The spatial average nearest neighbour: COVID-19 cases and COVID-19 locations counts (number of overlapped points).
Nearest neighbour Results
|
Cases (points)
|
Locations counts
|
Observed distance
|
38.7 m
|
126.5 m
|
Expected distance
|
408.2 m
|
538.5 m
|
Ratio nearest neighbour
|
0.095
|
0.217
|
Z Score
|
−201.58
|
−122.07
|
P value
|
0.00
|
0.00
|
Source: Authors´ elaboration based on COVID-19 Microdata Daily Register from the Health Authorities (Government of Cantabria, Spain). |
The preliminary geostatistical analysis is fundamental to support the following research stages. The fact that the COVID-19 spatial pattern is statistically significant allows a deeper analysis based on data mining and 3D space-time bins.
COVID-19 bins at a regional scale with a global temporal period
This section presents the analysis of 3D bins and emerging hotspots for the Autonomous Community of Cantabria throughout the complete period of the data series, that is, from March 1 to November 20, 2020. Hence, the sequence includes the COVID-19 cases corresponding to waves 1 and 2 facing the third wave too.
In the absence of established standard thresholds of time and distance (size of the cubes) for the creation of 3D COVID-19 bins, we set time periods of 4-week and based the distance on preliminary statistical analysis, considering as a reference the expected distance threshold 538.5 m (3.3 miles) derived from exploratory spatial average nearest neighbour analyses (as indicated in Table 1). The 4-week interval is suitable since it includes 2 periods of the usual reference time for the study of accumulated incidents (that is, 14 days) and fulfils the condition of the method to be applied that establishes a minimum of 10 moments of time for development of the bins.
The result provided by the visualization of the cubes at the regional level (Fig. 5) is revealing in that it simplifies the information of the starting points or their corresponding heat map and brings to the fore the hierarchies of the pandemic affectation in the analysed territory. 1,414 bins are identified that respond to differentiated intensities and distributions. The cubes show two outstanding levels of spatial segregation: firstly, the one that refers to the inland coast differences, with a clear articulation in the coastal municipalities, and secondly, its organization is concentrated in areas of high density and mobility, as occurs in the Santander-Torrelavega sector of the FUA of Santander and especially in the western arc of Santander Bay, which is where the peri-urbanization processes of the city of Santander are most intense.
The activity of the cubes of the eastern coastal region is much more prominent than that of the western coast. The main reason of this disparity West-East is the proximity of Bilbao, the tenth most important city in Spain (near 350,000 inhabitants in the city but more than 1 million inhabitants in the metropolitan area). Bilbao is located 100 Km (62 miles) from Santander and has an important role as a pole of economic attraction in the North of Spain and its influence on the eastern municipalities of Cantabria, as well as the outstanding exchange of flows with the neighbouring community are factors to take into account in these results. Furthermore, at a European level, the FUA of Bilbao exceeds its Autonomous Community borders (Basque Country) and it includes the oriental part of Cantabria. It is an important sign of the intense inter-relation of the East part of Cantabria and Bilbao.
On the other hand, inland Cantabria has a layout based on small cubes in most of the territory except for the regional headwaters of the interior valleys.
The first expressive result of emerging hotspots is that of the 1,414 cubes identified in the region, 812 (57%) do not present a pattern that can be associated with a specific hot or cold spot. In fact, from the COVID-19 distribution and the consideration of its spatial pattern over time do not derive any cold spots.
The analysis of emerging hotspots is revealing from the point of view of geoprevention in that it significantly limits the territory on which it would be important to focus the analysis. In fact, of the 1,414 starting cubes, 57% do not present any identifiable pattern such as cold or hot spot. Despite its importance in the number of cubes, it should be noted that only 22% of the cases occurred in these areas. Focusing on the 602 remaining cubes (with statistical significance), all correspond to the hotspot pattern and according to the number of cases the results obtained are presented as follows:
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251 bins (42%) are new hot spots, so it is a hot spot with statistical significance only in the final part of the period analyzed (October-November). These cubes house 2,190 cases (16% of the series analyzed).
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178 bins (30%) are oscillating hot spots. These are significant hot spots in the final period (October-November) but with a previous trend in which it has been a significant cold spot. In this typology less than 90% of the time intervals have been significant hot spots. In Cantabria this typology is the second not only number of bins but also in number of cases, since it accumulates 3,163 (23%). This type is also where the lowest mean age of the cases is detected (41.7 years).
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98 bins (16%) are sporadic hot spots. This typology corresponds to locations that are and are no longer a hot spot several times in the time considered. Less than 90% of the time intervals have been significant hot spots and have never behaved as significant cold spots. This typology is striking in that in a limited number of cubes (16%) it is the one with the most COVID-19 cases, with a total of 3,988 cases (30%). Furthermore, possibly related to the large number of cases in this kind of hot spot is where more deaths have been concentrated without considering those corresponding to residences (42 deaths, equivalent to 31%).
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75 bins (12%) are consecutive hot spots. These are areas with significant hot spots in a single run without interruption in the final time intervals considered. These cubes were not before the last run significant hot spots. These hotspots are also the least common in terms of number of cases, accumulating 1,105 cases (8%).
On the other hand, in addition to the general statistical guidelines, the spatial pattern is very interesting (Fig. 6). The new hot spots establish the areas of new cases that, in urban areas already hit in the first wave by the pandemic, present a peripheral configuration in the form of haloes around areas with a high intensity of cases previously. Hence, it would respond to the space process COVID-19 sprawl in the central and eastern coastal area. On the other hand, in rural areas in most cases they correspond to new locations in the headwaters of the region.
Considering the typologies with alternating cases over time, these are concentrated in the municipalities of the two main cities Santander and Torrelavega, as well as in the periphery of Santander in the arc of the bay. It is noteworthy that these typologies do not intermingle, but rather are segregated in an east-west direction in Santander and north-south in Torrelavega, with sporadic hotspots coinciding with areas with the highest social content and oscillating hotspots with more modest areas.
Second COVID-19 wave using 3D and emerging hotspots analysis at an intra-urban scale
The methodological adaptations of the scale from the spatial point of view may also be accompanied by specific changes in the temporal resolution and the bin distance parameter. Thus, in this section, the emerging hotspots in the municipality of Santander are analysed at the intra-urban level and with a period corresponding to the second wave, so the cases are filtered from August 1 to November 20. The analysis is based on 3,477 COVID-19 cases that are analysed in their space-time cubes at 7-day intervals.
This study more spatial and temporally limited yields results of interest and expressive geospatial information for decision-making from the geoprevention perspective. Considering the average expected distance (based on nearest neighbour analysis: 102.05 m -0.06 milles-) as the distance in the construction of the cubes, almost 700 bins are obtained for the whole of the municipality of Santander (Fig. 7), which present an important development in the entrance streets to the city as well as in central areas, while in the north with a lower population density and to the east with a predominantly high status social gradient the configuration of the cubes is very discreet in number and size.
The analysis of emerging hotspots at this scale results in a filtering of non-significant cases for 278 bins (40%, a much lower proportion than at the regional scale) in which 830 cases (24%) are located. Therefore, we will focus on the typologies of the rest of the cubes (418, 60%), all of them hotspots except for one that gives a cold spot profile.
Table 2
Emerging Hotspots of COVID-19 in the municipality of Santander.
Emerging hotspot typology
|
Bins
|
Cases
|
Deceased*
|
Average age
|
Oscillating hotspots
|
244
|
1,485
|
12
|
42.0
|
Sporadic hotspots
|
88
|
789
|
9
|
44.2
|
Consecutive hotspots
|
58
|
277
|
2
|
41.4
|
New hotspots
|
27
|
95
|
1
|
43.0
|
Total
|
417
|
2,646
|
24
|
42.7
|
*Without considering residents in elderly homes.
Source: Authors´ elaboration based on COVID-19 Microdata Daily Register from the Health Authorities (Government of Cantabria, Spain).
|
Regarding the spatial pattern of emerging hotspots, we highlight the existence of significant patterns in the center and east of the city. Areas of modest social content and high density are identified as sporadic hotspots, areas in which hot spots occur in various periods of time in response to a spatial repetition factor. Oscillating hotspots stand out in neighborhoods of medium and medium-low social content that will be areas of attention for upcoming geoprevention actions since they are identified as significant hotspots in this last period.
Moreover, the consecutive hotspots present a peripheral distribution and correspond to areas of lower population density. Finally, the existence of points that respond to new hotspots with a concentrated distribution in the area of contact with the eastern sector with high social content and modest neighborhoods to the west, both focus of peripheral and opposite position, deserves a mention.