In both cases, Ripley’s isotropic K function (black and red lines) was above the random uniform distribution Ktheo
, suggesting spatial clustering of gunshot reports at any scale within Mexico City, consistent with the nearest neighbor ratio test. The period of lockdown did not appear to affect the spatial patterns of tweets in the city due to the similarity of the estimated functions. Figure 1
shows the isotropic correction of Ripley’s K function due to the number of tweets and with the assumption of no stationary process. We included in the appendices the estimations of the function with different corrections of the Ktrans
edge effects for both periods 
(pp. 147, 216).
2.3. Location of Spatial Intensity in Mexico City
Two maps of intensity were developed with kernel estimation: one for the period before lockdown and one for during the COVID-19 pandemic (Figure 2). In both images, the areas shaded in yellow show the peaks in intensity of gunshot reports in Mexico City.
Figure 2. Kernel estimate of gunshot prevalence in Mexico City pre-COVID-19 (left) and during COVID-19 (right). Source: Authors’ own with R software.
The period before COVID-19 contained a greater intensity of tweets reporting gunshots in the city than the period during the health emergency. On the left of Figure 2, the highest intensity peak across the city had an estimated probability of 0.35. In contrast, during the pandemic, the intensity decreased to a probability of 0.22 in the highest peak. The smoothing out detected in the second period dispersed the intensity of reports across a greater area toward the east, corresponding to the central area of the city, although to a lesser degree (right side of Figure 2). Thus, the location of the greatest intensity of gunshot reports on the left side of Figure 2 corresponds to the western limit of the city. The pandemic modified the pattern detected by the kernel estimation from the west toward the center of the city on the left side of Figure 2.
In both periods, reports of gunshots were concentrated in the central area of the city, leaving the northern and southern parts of the city out of the first order intensity detected in both cases. Although the absolute frequency of reports on Twitter was lower in the second period (from 141 to 101 tweets), the spatial dispersion and distance between points during lockdown was greater. The kernel estimation analysis undertaken revealed the displacement of the spatial pattern of intensity of gunshots in Mexico City during the COVID-19 pandemic.
To analyze the underlying criminological processes that could explain the change in the intensity pattern of tweets about gunshots, we present below the locations of the points in the administrative areas that correspond to the 16 municipalities of Mexico City (Figure 3). This geographic management of data does not consider spatial analysis with polygons but rather serves to contextualize the detected pattern of intensity.
Figure 3. Reports of gunshots pre-COVID-19 (blue) and during COVID-19 (red) in Mexico City. The numbers on the map represent Miguel Hidalgo (016), Cuauhtémoc (015) and Iztapalapa (007). Source: Authors’ own with QGIS software.
2.4. Prevalence by City Municipalities
The peaks in intensity detected by the kernel estimation corresponded to the neighboring municipalities of Miguel Hidalgo (016) for the period prior to the pandemic and Cuauhtemoc (015) during the COVID-19 pandemic (Figure 3). The highest numbers of tweets were concentrated in these two municipalities, with 29 and 18 tweets, respectively. In both periods, the municipality of Iztapalapa (007) remained constant, with an average of 22 tweets for both periods. The size of the buffers in Figure 3 corresponds to the number of reports in the municipalities (Table 2), with the three municipalities mentioned identified by their official codes.
Table 2. Number of tweets per municipality.
|La Magdalena Contreras
|Cuajimalpa de Morelos
|Gustavo A. Madero
Regarding the rest of the city, in the first pre-COVID-19 period (blue), reports were concentrated in the municipalities of Miguel Hidalgo (29), Iztapalapa (23), Benito Juárez (18) and Magdalena Contreras (17). During lockdown (red), tweets were concentrated in the municipalities of Iztapalapa (21), Cuauhtemoc (18) and Benito Juárez (11) (Table 2). In both periods, the same municipalities did not report gunshots (Gustavo A. Madero and Milpa Alta).
shows the decrease in the frequency of gunshot reports in the city by municipality from 141 tweets to 101. The municipalities of Miguel Hidalgo and La Magdalena Contreras registered the greatest decline in reports, from 29 tweets to 8 and from 17 to 7, respectively. In contrast, the municipality of Cuauhtemoc registered more gunshot reports in the second period (18) than before the lockdown. Another atypical value was registered in the municipality of Iztapalapa, with a slight decrease from 23 to 21 tweets, thus not experiencing the pronounced increase or decrease that occurred in other areas of the city. These changes in the frequency of reports by municipality between the two periods were statistically significant (Fisher’s exact test, a non-parametric test with a p
-value of 0.05097; see Appendix A
2.5. Tweets per Mmonth, Day of the Week and Hour of the Day
Before the pandemic, tweets about gunshots were predominantly in the last months of the year in 2018 (October, November and December) as well as in February 2019, accounting for 49.64% of the reports. During the pandemic, tweets were concentrated in the months of October 2019 and January, March and June 2020, constituting 52.47% of the reports. Tweets about gunshots were largely a weekend phenomenon; Fridays, Saturdays and Sundays accounted for 57.44% and 54.45% of the reports before and during the pandemic, respectively. They were most frequent on Sundays for both periods. Regarding the hour of the day, tweets were strongly evident at night after 8:00 p.m. and before 6:00 a.m., with a slight decrease during the COVID-19 period (from 76.59% to 71.28%). However, these variations in tweets by month, day of the week and hour of the day or night were not significant. The results of Fisher’s exact test to analyze variations did not allow for the rejection of the hypothesis of randomness of the observed frequencies. The estimated p-values were 0.2159 for months, 0.5322 for days of the week and 0.3729 for differences between day and night (based on 2000 replicates). In other words, there was no statistical evidence to suggest that the pandemic modified the regularity of reports in the months, days of the week or hours of the day in which the tweets regarding gunshots were generally concentrated in Mexico City.
3. Current Insights
One of the most interesting findings of this analysis was that during lockdown in Mexico City, shootings appeared to have moved from one municipality to a neighboring one (Figure 3). The model suggests that the municipality of Cuauhtemoc (015) had the highest concentration of shootings and experienced a 50% increase in Twitter reports. In contrast, the neighboring municipality of Miguel Hidalgo (016) registered a reduction in the average frequency, with a pronounced fall in reports in the second period of 72.4% (Table 2). This may have been due to changes in crime patterns in the city.
In contrast to the studies conducted in Baltimore, Buffalo, Chicago, New York and Philadelphia, which found an increase in violence associated with firearms during the pandemic in comparison with 2019 
, in Mexico City, an overall decrease in reports of gunshots was reported on Twitter across the two periods. This finding also differs from those studies that did not find significant increases in shootings in 25 large cities in the USA in general 
or in Los Angeles 
The decrease in shootings in Mexico City is consistent with the significant decrease in other types of crimes in the city during the pandemic, such as robberies of public transport 
, burglary and vehicle theft 
. On an international level, this study coincides with the effects of the pandemic on crime: an overall decrease, as indicated by, for example, Shayegh and Malpede (2020) 
in San Francisco, by Mohler et al. (2020) 
in Los Angeles and Indianapolis and by Campedelli (2020) 
in Los Angeles.
Interestingly, this study shows not only a decrease in the reports of shootings but also a change in the spatial intensity of these reports. The kernel estimation spatial analysis shows that this decrease in frequency of reports was accompanied by a change in the spatial pattern of the intensity of tweets in the city. The change detected during the pandemic (right side of Figure 2
) showed an evening out of intensity across the area, resulting in an almost equal probability of tweets in the central area of Mexico City as in the west. As illustrated in Figure 3
, the municipalities of Miguel Hidalgo (016) and Cuauhtemoc (015) share an administrative border, and they registered the greatest frequency of reports for both periods. One explanation for the changes in the number of tweets may be that the municipalities border each other, and according to the first law of geography, “everything is related to everything else, but near things are more related than distant things” 
One possible explanation for this phenomenon, based on the spatial proximity of the municipalities and the routine activities approach, is the differing financial capacity of potential victims of crimes committed with firearms to comply with lockdown measures. The municipality of Cuauhtemoc (015), which includes the historical center of the city, has many street vendors and informal businesses who, as a result of the severe decline in their income, struggled to respect the health lockdown (that is, they continued with their activities despite the restrictions). Furthermore, 48.07% of the occupied population in the municipality work in the informal sector 
. In contrast, in the municipality of Miguel Hidalgo (016), which includes some of the highest income neighborhoods in the city (Polanco and Lomas de Chapultepec), the “stay-at-home” measures were relatively easier to follow, as businesses had the capacity to endure the closures (Figure 3
). At the same time, there is less informal work (street vendors) in the municipality, with 40.50% of the population employed in the formal sector 
. The decrease in commercial and pedestrian activity possibly gave rise to the displacement of crimes committed with firearms from Miguel Hidalgo (016) to Cuauhtemoc (015), where activity continued and criminal opportunities did not decrease significantly.
This explanation is further supported by the different income levels and business costs between municipalities. The proportion of employees reporting an income of more than 5 times the minimum wage in Miguel Hidalgo (016) is 14.64%, compared with 10.99% in the municipality of Cuauhtemoc (015) (the minimum wage in Mexico City is USD 7 
). The annual per capita income also reflects this difference between the municipalities. Miguel Hidalgo (016) has an average income of USD 8748.64 in comparison with that of Cuauhtemoc (015), which reports an average income of USD 6164 
. This difference plays out in all areas. For example, the monthly rental for a 100 m2
commercial property has an average cost of USD 1960.00 in Miguel Hidalgo (016), while in the Cuauhtemoc (015), such a property would go for two thirds of this cost at USD 1330.00 
. These figures illustrate the greater income opportunities available in the municipality of Miguel Hidalgo (016) in comparison with the Cuauhtemoc (015).
An alternative hypothesis regarding the increase in reports of gunshots in Cuauhtemoc (015) during the pandemic relates to the activities of drug trafficking organizations and their disputes over local markets (see spatial patterns and drug hotspots in Mexico City in Vilalta 
). However, a recent study found that crimes associated with organized crime, such as robbery, kidnapping and homicides, remained at similar levels in Mexico City before and during the COVID-19 pandemic 
. As activity levels of organized crime remain constant (for example, for homicide), then it is likely that a significant portion of the increase in tweets reporting gunshots in the municipality of Cuauhtemoc (015) was due to the displacement of other crimes committed with firearms from the neighboring municipality of Miguel Hidalgo (016). The notable fall in reports registered by the latter and the spatial proximity that modified the pattern of spatial intensity in the city, detected by the kernel estimation, supports this explanation.