The question of whether autistic individuals might be predisposed to engaging with computing systems has its origins in work by Baron-Cohen and colleagues in 1997, who highlighted that working with objects and systems rather than people may be reflective of the broad autistic phenotype. This idea gained traction in both the media and among law enforcement personnel, with organizations such as the UK’s National Crime Agency suggesting that autistic individuals may have a greater propensity for cybercrime. However, attempts to test this relationship empirically have produced deeply conflicting results over the past decade, with different studies pointing to autism diagnosis, autistic traits, or neither as the relevant predictor.

Previous research has been marked by methodological differences that may explain some of the contradictory findings. In a 2015 survey of 296 undergraduate students, Seigfried-Spellar and colleagues measured computer deviant behavior and autistic traits, finding that 60 percent of participants reported some form of computer deviant behavior and that those who had engaged in virus writing, identity theft, or cyberbullying recorded significantly higher scores on the Autism Spectrum Quotient than students who had not. However, there was no difference in level of autistic traits between students who had engaged in hacking and those who had not. In a more recent study, Payne and colleagues sampled 290 internet users and found that autistic traits, but not an autism diagnosis, were associated with higher engagement in cyber-dependent crime, while an autism diagnosis was related to a reduced rather than an increased involvement in such crime.

Yet contradictory findings were subsequently reported by Lim and colleagues in 2023, who found that an autism diagnosis, but not autistic traits, predicted engagement in cybercrime in a sample of 302 participants. Cyber-dependent crime was reported by 20 percent of the autistic sample and 11.2 percent of the non-autistic sample. More recently, Maras and colleagues examined the relationship between autism diagnosis and cybercrime among adolescents in the UK Millennium Cohort Study, a longitudinal study of over 19,000 individuals, and found that after controlling for demographic variables, cognitive ability, family background, and environment, autism diagnosis was not a significant predictor of cybercrime involvement. These inconsistencies suggested that methodological limitations, rather than genuine differences in the underlying relationship, might be responsible for the conflicting data patterns.

To address these limitations, Lim and Brewer conducted a partial replication of the 2023 study with a much larger sample of autistic adults. The researchers recruited 344 participants through the online crowdsourcing platform Prolific, of whom 138 reported having a formal diagnosis of autism from a qualified professional and 206 confirmed they had not received such a diagnosis. Participants ranged in age from 18 to 75 years. The sample was substantially larger than the autistic samples in previous studies, which had included only 25 and 23 autistic participants respectively, providing increased statistical power and variance on the key measures.

The study incorporated several methodological improvements designed to address the weaknesses of prior research. Participants who endorsed engagement in cybercrime were asked to provide a brief description of what they did and why, ensuring that any disclosure of cybercrime was in fact actual criminal activity. Responses that were clearly not cybercrime, such as accessing a family member’s email account with their permission or learning to hack a server as part of IT training, were modified to reflect no engagement. The researchers also included two measures to control for differences in participants’ general tendencies to lie or to respond in self-enhancing ways: the Lying in Everyday Situations Scale and the Balanced Inventory of Desirable Responding Short Form. Additionally, they measured autistic traits using the 50-item Autism Spectrum Quotient rather than a shorter version, allowing examination of whether different types of autistic traits were more predictive of cybercriminal activity than others.

The results revealed that individuals with an autism diagnosis were 7.30 times more likely to report engagement in cyber-dependent crime than individuals without a diagnosis, although the researchers noted that the wide confidence interval indicated this estimate was imprecise. In contrast, autistic individuals were not significantly more likely to report engagement in cybercrime more broadly, which included both cyber-dependent and cyber-enabled crime. Advanced digital skills was also a significant predictor of engagement in cyber-dependent crime, but the odds ratio values for this relationship were small. Age, gender, autistic traits, and theory of mind did not significantly predict engagement in cybercrime. A mediation analysis revealed that the relationship between autism diagnosis and cyber-dependent crime was not mediated by advanced digital skills or theory of mind, suggesting that the pathway through which autism diagnosis influences cyber-dependent crime remains unclear.

The researchers conducted exploratory analyses to examine whether differences in self-reported cyber-criminality between autistic and non-autistic participants were due to differences in self-disclosure rather than actual differences in criminal behavior. The results showed that neither group displayed a propensity for lying or socially desirable responding, no group differences were found on either measure, and autism diagnosis remained a significant predictor of cyber-dependent crime after controlling for scores on the lying and social desirability measures. However, the exploratory analyses also revealed that autistic individuals who reported engagement in cybercrime had a higher propensity to lie and were less likely to respond in a socially desirable manner than non-autistic individuals who reported engagement in cybercrime, raising questions about the reliability of self-report data in this research area.

The findings suggest that involvement in cyber-dependent crime may be more prevalent among autistic than non-autistic adults, but the researchers caution that it would be premature to conclude that autism or autistic traits per se are risk factors for engagement in cybercriminal activity. The complex multi-factor predictors identified in the UK Millennium Cohort Study, including being male, domestic violence between parents, low parental monitoring, low wellbeing, self-harm, exclusion from school, and spending more time online gaming, highlight the importance of developing a more sophisticated understanding of the environmental and individual difference variables that might exacerbate cybercrime risk for some autistic individuals. Future research will need to focus on whether there are specific characteristics of autistic individuals that might render them vulnerable to becoming involved in cybercrime, and to corroborate the current findings using objective data rather than self-report measures alone.

Subject of Research: Cyber Dependent Crime May Be Predicted by Autism Diagnosis but Not Autistic Traits

Article Title: Cyber Dependent Crime May Be Predicted by Autism Diagnosis but Not Autistic Traits

Article References: Lim, A., & Brewer, N. (2026). Cyber Dependent Crime May Be Predicted by Autism Diagnosis but Not Autistic Traits. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803-026-07580-1

Image Credits: AI Generated

DOI: 10.1007/s10803-026-07580-1

Keywords: Cyber, Dependent, Crime, Predicted, Autism, Diagnosis, Autistic, Traits, scientific research

The Utopías are the flagship social program of Iztapalapa, a borough of nearly 1.8 million residents that combines the highest population density in Mexico City with some of its deepest deficits: only 5.4 square meters of green space per person, roughly a third of the best-served borough, and a poverty rate of 43.9 percent in 2020. Construction began in 2019, and each site offers a different mix of swimming pools, skateparks, climbing walls, science exhibits and care services, some pairing recreation with women’s health programs. Yet until now, no one had systematically measured who actually uses the network.

The study, published in Frontiers in Big Data, sidesteps the limitations of on-site surveys and entrance headcounts by analyzing six months of anonymized location-based services data, from late September 2023 to the end of March 2024. The researchers processed raw streams of smartphone pings, detecting stays of at least five minutes within a twenty-meter radius and clustering repeated visits into stable stop locations. Each device’s home was inferred from the stop it visited most often during residential hours, on at least fourteen distinct days, and matched to a census-derived wealth map dividing the metropolitan area into 5,648 residential cells.

Because no official boundary maps exist for the Utopías, the team reconstructed each site’s perimeter using the coordinates of WiFi access points installed at the facilities as anchors, tracing walls and fences in satellite imagery. They then characterized every site along three axes a government can observe before building anything: a distinctiveness index scoring the rarity and capital intensity of activities, a transit accessibility score built from GTFS data within a one-kilometer buffer, and the volume and violent share of crime from georeferenced prosecutor’s office records.

The results show a network sharply divided in scale. Two large, well-connected sites, Olini and Meyehualco, drew roughly 2,700 and 1,200 unique visitors respectively, together accounting for more than four in five of all recorded visits across the network’s 4,704 unique visitors. At the other extreme, sites like Papalotl, Tecoloxtitlan and Barco recorded only a handful of visitors each, though these smaller spaces maintained remarkably stable daily use by a small cohort of recurring locals. About fifty-five percent of visitors overall came from outside the host borough, but this metropolitan pull was almost entirely concentrated in the two largest hubs.

Transit connectivity emerged as the strongest predictor of visitor volume, correlating with log-scaled visitor counts at r = 0.64. The number of distinct transit routes and weekday trip frequency within a kilometer carried most of the signal, while a conventional distance-weighted accessibility index, the classic measure of potential access, showed no significant relationship with actual visitation. In other words, what matters is not how centrally a facility sits in the urban geography but whether buses, metro lines and colectivos can deliver people to it, a finding with obvious value since GTFS data are public and this screening can happen before a single facility is built.

What each site offers shaped a different outcome: not how many visitors arrive, but how far they travel. Sites with signature attractions such as a planetarium or a dinosaur park showed a large weekend distance premium, the extra distance weekend visitors cover compared with weekday visitors, with landmark attractions correlating at r = 0.91 with that premium. Everyday facilities like fitness rooms and team sports courts tracked higher visitor volume instead. Notably, the team found no association between a site’s overall offer and its attendance, suggesting signature infrastructure extends a site’s catchment rather than filling it.

Crime told a subtler story still. The total volume of recorded incidents around a site was essentially unrelated to visitation, but the violent share of that crime was strongly and negatively associated with visitor wealth (r = −0.75) and travel distance (r = −0.64). Facilities surrounded by more violent crime drew poorer, more localized catchments, a pattern the authors interpret cautiously as reflecting the overlapping geographies of violence and residential poverty rather than any causal effect on individual choices.

The study has clear limits. Location-based data under-represent the poorest residents, precisely the population the program targets, the panel is not disaggregated by sex, and with only thirteen sites every reported relationship is a correlation across a single six-month window. Still, the temporal signal offers something a one-time survey never could: visits grew steadily from October to a February peak, and the weekly cycle was nearly flat, showing the centers function as everyday weekday infrastructure rather than weekend destinations.

Crossing the two levers most directly under government control, the researchers produced a four-type typology: flagship destinations like Olini with both transit and unique attractions, connected neighborhood hubs, under-connected attractions, and plain neighborhood spaces serving their immediate surroundings. The team’s composite index tracked mean travel distance better than any single component, and their conclusion for planners is stark: infrastructure alone will not close the equity gap. Transport access, screenable from open data before any capital is committed, comes first among the levers any city can pull.

Subject of Research: Realized access and equity of the Utopías community centers in Iztapalapa measured with location-based mobility data

Article Title: Evaluating the reach and equity of public spaces in Mexico City using mobility data: the case of the Utopías in Iztapalapa

Article References: Langle-Chimal, O. D., Cadavid-Aguilar, N., Meouchi-Vélez, A., & Ponce-López, R. (2026). Evaluating the reach and equity of public spaces in Mexico City using mobility data: the case of the Utopías in Iztapalapa. Frontiers in Big Data, 9, Article 1942458. https://doi.org/10.3389/fdata.2026.1942458

Image Credits: AI Generated

DOI: 10.3389/fdata.2026.1942458

Keywords: human mobility data, location-based services, public space, social infrastructure, Mexico City, Iztapalapa, transit accessibility, urban equity, crime, GTFS, visitor catchments, segregation

The study arrives at a moment when wildfire has become an inescapable feature of Southern California life. Los Angeles sits in a landscape where fire ecology predates human settlement by a millennium; chaparral ecosystems historically burned roughly every thirty years, with extended droughts allowing dead vegetation to accumulate as fuel. Fire, in turn, rejuvenates chaparral seeds that inhibit germination until after a blaze, returning nutrients to the soil and enabling new growth. That natural cycle now collides with millions of residents, and the catastrophic fires of recent years have pushed questions about disaster and social order into public debate. Popular narratives often assume that disasters unleash looting and lawlessness, but decades of criminological research on hurricanes, floods, and tornadoes tell a more complicated story, and wildfires have remained a conspicuous gap in that literature.

To close that gap, the researchers assembled tract-level data for the City of Los Angeles and identified seven major wildfires that occurred inside the municipal boundaries during the 2016 to 2022 window. Their statistical strategy centered on random-effects negative binomial regression models, a choice tailored to the structure of crime data. Crime counts in small geographic units are overdispersed, meaning their variance exceeds their mean, and a negative binomial framework accommodates that distribution far better than ordinary linear regression. The random-effects specification, meanwhile, accounts for unobserved, stable characteristics of each census tract, such as entrenched disadvantage or built environment features, that could otherwise confound the estimated relationship between fire exposure and crime.

The team measured wildfire exposure in two distinct ways, and that distinction turned out to be analytically decisive. The first measure identified census tracts that experienced direct wildfire damage, that is, tracts where the fire itself burned. The second captured proximity, identifying tracts located within a quarter mile of burned areas even if no fire touched them directly. Separating these two forms of exposure allowed the authors to test whether the crime effects of wildfire are confined to physically damaged neighborhoods or spill over into surrounding communities, a question with direct implications for how police and emergency managers allocate resources after a disaster.

The results paint a nuanced picture. Census tracts that experienced direct wildfire damage reported significantly lower levels of property crime. Violent crime was also marginally lower in those directly burned tracts, though the evidence there was weaker. When the researchers accounted for proximity to wildfire damage, however, the direct-damage effect on property crime was no longer statistically significant. What proved most robust was the proximity effect: tracts within a quarter mile of burned areas reported significantly lower levels of both violent and property crime. In other words, the strongest crime-suppressing signal appeared not in the neighborhoods the flames consumed, but in the neighborhoods standing just outside them.

The authors interpret these findings through the lens of two foundational criminological frameworks. Social disorganization theory holds that neighborhood crime is shaped by the capacity of local communities to exercise informal social control, a capacity rooted in stable networks, shared trust, and collective efficacy. Disasters can disrupt or reorganize those networks in unpredictable ways. Research after Hurricane Katrina, for example, documented both crime displacement and, in some domains, strengthened collective resources among repopulating residents. The routine activities approach offers a complementary mechanism: crime requires a motivated offender, a suitable target, and the absence of a capable guardian converging in time and space. Wildfires radically alter that convergence calculus. Evacuations empty neighborhoods of suitable targets, closed roads and fire-perimeter checkpoints supply guardianship, and burned or damaged structures simply cease to be burglarizable.

The proximity finding fits neatly into this routine-activities logic. A quarter-mile buffer around a burned area is precisely the zone where emergency access controls, evacuation orders, utility shutoffs, and heavy first-responder presence are most intense, even when the homes themselves remain intact. Fewer people on the street, restricted vehicular movement, and a saturated official guardianship presence can suppress both opportunistic property offenses and interpersonal violence. At the same time, the social disorganization perspective suggests that shared threat experiences can galvanize neighborly cohesion, with residents monitoring one another’s properties and coordinating informally during and after evacuations. The study cannot adjudicate between these mechanisms, but the spatial pattern it documents, with effects strongest near rather than within fire perimeters, is consistent with guardianship and mobility explanations operating at the edge of disaster zones.

The findings also connect to a broader and rapidly growing research program on environment, green space, and crime. Recent work has linked urban greenspace to lower crime risk across hundreds of major U.S. cities, examined park size and community safety, and used quasi-experimental designs to test whether urban greenways such as the Atlanta Beltline reduce offending. Related studies have explored how drought conditions relate to violent crime trends in California counties and how natural hazards reshape spatiotemporal crime patterns nationally. The Los Angeles wildfire analysis adds a distinctive dimension to this literature: rather than a gradual environmental amenity, wildfire is an acute, spatially bounded shock, and its crime effects appear to radiate outward from the burn scar in measurable ways.

For practitioners, the implications are concrete. If crime genuinely declines in and around burned neighborhoods, then post-fire policing strategies premised on looting panics may be misdirected, diverting officers from communities where they are actually needed and potentially exposing evacuated areas to over-policing. Emergency response planning, similarly, could incorporate the finding that a quarter-mile buffer around fire perimeters functions as a distinct criminological zone, one where the priorities may be public safety and recovery support rather than crime suppression. For neighborhood recovery, the results hint that the social fabric of fire-adjacent communities may be more resilient than disaster folklore assumes, though the authors caution that their analysis captures associations over a defined study period rather than the full arc of long-term recovery.

The authors are explicit about the limits of their design and the directions it opens. Their data cover seven wildfires within one city over seven years, and census tracts, while useful units of neighborhood analysis, aggregate individual behavior into coarse spatial bins. The proximity threshold of a quarter mile is one plausible buffer among many, and future work could test whether the effect decays smoothly with distance or exhibits spatial spillovers of the kind modeled in wildfire risk mitigation research. Longer follow-up periods could reveal whether suppressed crime rebounds as neighborhoods rebuild, whether reconstruction and gentrification dynamics, of the kind documented after the 1994 Northridge earthquake in Los Angeles, reshape local crime trajectories, and whether the patterns hold in other fire-prone regions. As climate change intensifies fire regimes across the American West, understanding how wildfires reshape not only landscapes but also the social order of the neighborhoods that survive them is becoming an urgent scientific question, and this study provides one of the clearest spatial maps yet of where that reshaping begins.

Subject of Research: The association between neighborhood wildfire exposure and violent and property crime patterns in Los Angeles

Article Title: We Didn’t Start the Fire: How Neighborhood Crime Patterns in the City of Los Angeles Were Associated with Wildfires

Article References: Barton, M. S., Valasik, M., LaPointe, R., & Lopez, S. (2026). We Didn’t Start the Fire: How Neighborhood Crime Patterns in the City of Los Angeles Were Associated with Wildfires. American Journal of Criminal Justice. https://doi.org/10.1007/s12103-026-09955-7

Image Credits: AI Generated

DOI: 10.1007/s12103-026-09955-7

Keywords: wildfires, crime, Los Angeles, violent crime, property crime, social disorganization theory, routine activities approach, disasters, criminology, neighborhoods, negative binomial regression, emergency response