Fig 1.
Characterization of missing people.
A) Age probability distributions for missing people by gender. Female disappearances are characterized by a peak in the 15-19 year age group, whereas the age distribution for men corresponds to typical working life duration. A value indicates a statistical difference between the two distributions. B) The bar chart shows men and women who are still missing (green bar), located (red bar), and total disappearances (sum of still missing and located people). There are more male disappearances than female, and men have a lower probability of being located. C) Time series of missing people by gender. There were more missing people before and after the COVID-19 pandemic. D) Probability distribution of the number of days people were missing (only for located people). The distribution approximately follows a power-law behavior
, where
and A = 0.1.
Fig 2.
Maps of disappeared people in Mexico City.
A) Disappeared people per municipality. B) Disappeared people divided by the population. C) Located people divided by disappeared people. D) Average housing price per . E) Disappeared men. F) Disappeared Boys (younger than 18 years old). G) Disappeared women. H) Disappeared girls (younger than 18 years old). Municipality abbreviations: Alvaro Obregón (AO), Azcapotzalco (Az), Benito Juárez (BJ), Coyoacán (Cy), Cuajimalpa (Cj), Cuauhtémoc (Ch), Gustavo A. Madero (G), Iztacalco (Ic), Iztapalapa (Ip), La Magdalena Contreras (MC), Miguel Hidalgo (MH), Milpa Alta (MA), Tlahuac (Th), Tlalpan (Tl), Venustiano Carranza (V), and Xochimilco (X).
Fig 3.
Normalized disappearances and Pearson correlation coefficients.
A) Map of normalized disappearances. The east side of the city exhibits the highest rates of disappearances. B) Pearson correlation coefficients considering raw variables. Disappeared people is strongly correlated with reports of drug dealing, both variables also are correlated with the population size and job offers (citizens mobility). C) Normalized disappearances and normalized reports of drug dealing are considered. Housing prices are negatively correlated with disappearances, reports of drug dealing and perception of insecurity. * indicates p – value < 0.05, and ** indicates p – value < 0.01.
Fig 4.
Dimensional reduction and clustering.
A) Data was reduced from 5 to 2 dimensions (normalized disappearances, located/disappearances, housing prices, normalized reports of drug dealing, and security perception). Then, we applied K-means algorithm. Each cluster is represented by a different color. B) Mexico City map result from the K-means clustering.