Time In Neighborhood Research Articles

The health implications of neighborhood networks based on daily mobility in US cities

A large body of research has been dedicated to understanding the neighborhood conditions that impact health, which outcomes are affected, and how these associations vary by demographic and socioeconomic neighborhood and individual characteristics. This literature has focused mostly on the neighborhoods in which individuals reside, thus failing to recognize that residents across race/ethnicity and class spend a non-trivial amount of their time in neighborhoods far from their residential settings. To address this gap, we use mobile phone data from the company SafeGraph to compare racial inequality in neighborhood socioeconomic advantage exposure across three scales: the neighborhoods that residents live in, their adjacent neighborhoods, and the neighborhoods that they regularly visit. We found that the socioeconomic advantage levels in neighborhood networks differ from the levels at the residential and adjacent scales across all ethnoracial neighborhoods. Furthermore, socioeconomic advantage at the network level is associated with diabetes and hypertension prevalence above and beyond its impact at the residential and adjacent levels. We also find ethnoracial differences in these associations, with greater beneficial consequences of network socioeconomic advantage exposure on hypertension and diabetes for white neighborhoods. Future social determinants of health research needs to reconceptualize exposure to include the larger neighborhood network that a community is embedded in based on where their residents travel to and from.

Smartphone Data Reveal Neighborhood-Level Racial Disparities in Police Presence

Abstract Research on policing has focused on documented actions such as stops and arrests—less is known about patrols and presence. We map the neighborhood movement of nearly ten thousand officers across 21 of America's largest cities using anonymized smartphone data. Police spend 0.36% more time in neighborhoods for each percentage point increase in Black residents. This neighborhood-level disparity persists after controlling for density, socioeconomic, and crime-driven demand for policing, and may be lower in cities with more Black police supervisors (but not officers). Patterns of police presence statistically explain 57% of the higher arrest rate in more Black neighborhoods.

“Neighborhood fear of crime and disadvantaged areas: a comparative longitudinal study”

AbstractThe current study explores how neighborhood fear of crime (“worry about specific crimes”) changes over time in neighborhoods with different levels of disadvantage. With a comparative design, 81 and 123- neighborhoods in two cities in Sweden are followed over a six-year period. Fear of crime trajectories are assessed through growth curve modeling. We consider how differences in neighborhood processes, such as social and physical disorder, might influence fear of crime levels. The results show that fear of crime increased over time in both cities and the increase was related to higher levels of physical disorder in several areas. Furthermore, the change differed depending on the neighborhood type – in highly disadvantaged neighborhoods, the fear was stable at a high level. In contrast, the largest increases in fear could be seen in the areas with the lowest disadvantage in both cities. Theoretical and practical implications are discussed.

Nonlinear Cosine Neighborhood Time Series-Based Deep Learning for the Prediction and Analysis of COVID-19 in India

The widening of coronavirus disease (COVID-19) across the globe has put both the government and humanity at risk. The funds of part of the biggest recessions are stressed out due to the severe infectivity rates and highly communicable nature of this disease. Due to the expanding consequence of cases being registered and their successive significance on the civic body administration and health professionals, certain prediction methods are intended to be necessitated to predict the number of cases in the future. In this paper, nonlinear cosine-based time series learning (NCTL) is introduced for the prediction and analysis of COVID-19 in India. First, the nonlinear least squares regressive feature selection (NLS-RFS) model is used for choosing the relevant features by considering both the active cases with less prediction error. Next, the cosine-based neighborhood filter algorithm is applied to attain the optimum filtered features to select relevant features with minimum prediction time. Finally, cosine neighborhood-based LSTM is used for the prediction of the number of COVID-19 cases being registered in India to the fore and consequence of precautionary measures like social distancing, lockdown, and declaring containment zones on the outspread of COVID-19. The existing deep learning methods’ prediction accuracy was not enhanced with lesser time. In order to overcome the issue, the nonlinear cosine-based time series learning (NCTL) method has been introduced. The aim of the proposed NCTL method is to predict the number of COVID-19 cases with less prediction time and prediction error. This helps to enhance the prediction accuracy for considering the time series with accurate prediction results. The experiment of the NCTL method is conducted using metrics such as accuracy, prediction error, prediction accuracy, and prediction time with respect to diverse samples. The simulation result illustrates that the NCTL method increases the prediction accuracy by 8%, reduces the prediction time by 18%, and minimizes the prediction error by 31% compared to state-of-the-art works in a computationally efficient manner.

Neighborhood Urban Design and Outdoor Later Life: An Objective Assessment of Out-of-Home Time and Physical Activity Among Older Adults in Barcelona.

This study explores how older adults' time out-of-home and physical activity (PA) are associated with the provision of urban open spaces (green spaces, plazas, and boulevards) and microelements (street trees and benches) in their neighborhoods. The authors used data from 103 residents in Barcelona and matched it to official geospatial data. The authors adjusted a set of mixed-effects linear regressions, both for the entire sample and also stratified by age and gender. For the entire sample, the percentage of green spaces showed a positive association with neighborhood time out-of-home and PA, while participants' PA also showed a positive association with the presence of benches. Outdoor time among older women was not associated with any of the measured exposures. For men, the provision of green spaces and benches was positively associated with time out-of-home and PA. These results could inform the design of urban spaces that aim to encourage outdoor activity among older adults.

Neighborhoods, Activity Spaces, and the Span of Adolescent Exposures.

Since the inception of urban sociology, the "neighborhood" has served as the dominant context thought to capture developmentally significant youth experiences beyond the home. Yet no large-scale study has examined patterns of exposure to the most commonly used operationalization of neighborhood - the census tract - among urban youth. Using smartphone GPS data from the Adolescent Health and Development in Context study (N=1405), we estimate the amount of time youth spend in residential neighborhoods and consider explanations for variation in neighborhood exposure. On average, youth (ages 11 to 17) spend 5.7% of their waking time in their neighborhood but not at home, 60% at home, and 34.3% outside their neighborhood. Multilevel negative binomial regression models indicate that residence in economically disadvantaged neighborhoods is associated with less time in neighborhood. Higher levels of local violence and the absence of a neighborhood school the youth is eligible to attend are negatively associated with time in neighborhood and mediate the concentrated disadvantage effect. Fractional multinomial logit models indicate that higher violence is linked with increased time at home while school absence is associated with increased outside-neighborhood time. Theoretical development and empirical research on neighborhood effects should incorporate findings on the extent and nature of neighborhood and broader activity space exposures among urban youth.

What Differences Do They See? Using Mixed Methods to Capture Adolescent Perceptions of Neighborhood Contexts.

Recent neighborhood research emphasizes the importance of individuals' perceptions of their neighborhoods, as well as expands the definition of "neighborhood" to include the different contexts encountered during routine daily activities (Coulton et al., 2013; Kwan, 2012). The present study uses qualitative interviews, sketch mapping, and survey data to explore adolescents' experiences of different neighborhoods within their activity space. Participants included 55 racially diverse youth aged 11-19 (M=14.64, SD=2.33) who resided in low-income neighborhoods in a small city in the Midwest. The majority reported spending time in multiple self-defined neighborhoods, noting significant differences between neighborhoods on collective efficacy, street code, and on participant-generated dimensions. Self-defined neighborhoods did not correspond to Census tracts, and Census indicators were not associated with youth's perceptions (e.g., collective efficacy, street code). Youth spent time in neighborhoods that differed significantly on multiple Census indicators of structural disadvantage, though within-individual differences tended to be small in magnitude. Type of routine activity was largely not predictive of distance traveled from home, though some findings suggest youth were more likely to cross neighborhood boundaries to engage in structured activities compared to different unstructured activities. Implications for neighborhood research and interventions are discussed.

Incarceration and the Economic Fortunes of Urban Neighborhoods

New research has identified the consequences of high rates of incarceration on neighborhood crime rates, but few studies have looked beyond crime to examine the collateral effects of incarceration on the social and economic well being of the neighborhoods themselves and their residents. We assess two specific indicia of neighborhood economic well-being, household income and human capital, dimensions that are robust predictors of elevated crime, enforcement and incarceration rates. We decompose incarceration effects by neighborhood racial composition and socio-economic conditions to account for structural disadvantages in labor force and access to wealth that flow from persistent patterns of residential segregation. We use panel methods to examine the effects on incarceration on New York City census tracts over an 11 year period from 1985-1996, a period which saw crime rates rise and fall sharply, and when incarceration rates increased and remained high in concentrated areas throughout the city. We examine whether persistently high incarceration rates erode human capital and depress median household incomes, further intensifying incarceration risks and threatening to create conditions where incarceration and economic disadvantage become endogenous features of certain neighborhoods. We find distinct but overlapping effects for prisons and jails, suggesting that these are parallel processes produced by loosely coupled law enforcement priorities. Incarceration effects are greater for household income than human capital, suggesting a complex relationship between persistent poverty, residential segregation, and incarceration that reinforces a classic poverty trap. Household incomes are lower over time in neighborhoods with higher proportions of African American population, even after controlling for the effects of race on incarceration, but we find no similar effects for Hispanic populations. Spatially targeted policies such as microinvestment and housing development may be needed offset the local embeddedness of poverty and disrupt its connections to incarceration and crime, while education policy and transitional labor market networking can strengthen local human capital.

Post-Traumatic Stress Disorder, Neighborhood Residency and Satisfaction, and Social Network Characteristics among Underserved Women in Baltimore, Maryland

BackgroundPost-traumatic stress disorder (PTSD) prevalence is high, but not well-understood, among women living in urban, impoverished areas. Although previous studies have established social support as an important factor in PTSD development and maintenance, little is known about how perceptions of neighborhood are linked to PTSD. This study examined the relationship between PTSD and social network and neighborhood factors among women with a low socioeconomic status. MethodsWe analyzed cross-sectional data collected from a human immunodeficiency virus/sexually transmitted infection peer network study in Baltimore, Maryland (n = 438). We used bivariate analyses to examine the associations between PTSD and social network characteristics and time in neighborhood and satisfaction. We then constructed multivariable regression models that controlled for the following with PTSD: homelessness, cocaine/heroin use, and unemployment. Main FindingsOverall, 30% of women had PTSD symptom severity consistent with a clinical diagnosis. In the multivariable model, dissatisfaction with neighborhood block (odds ratio [OR], 1.80; p = .03) and living in one's neighborhood for more than 5 years (OR, 1.69; p = .03) were associated with PTSD. Social network factors that were significantly associated with PTSD included a higher number of network members in conflict with the participant (OR, 1.28; p = .02), presence of a network member who would let the participant stay with them (OR, 0.4; p = .004), and the number of network members with whom the participant socialized (OR, 0.6; p = .04). ConclusionsIn this sample of impoverished urban women with a high prevalence of PTSD, duration of residency, satisfaction with neighborhood, and network characteristics were found to be strongly associated with PTSD symptom severity.

Changes in trauma admission rates and mechanisms during recession and recovery: evidence from the Detroit metropolitan area.

Although individual socioeconomic status has been linked with risk of traumatic injury, there has been relatively little research into the question of how economic changes may impact trauma admission rates in neighborhoods with different socioeconomic backgrounds. This study pairs ZIP code-level data on trauma admissions with county-level data on unemployment to assess differences in the type of changes experienced in more and less affluent neighborhoods of the Detroit metropolitan area between 2006 and 2014. Conditional linear growth curve modeling results indicate that trauma admission rates decreased during the "great recession" of 2008-2010 in neighborhoods with the highest unemployment levels, but increased during the same period of time in neighborhoods with lower unemployment. Consequently, citywide disparities in trauma incidence decreased during the recession and widened again as the economy began to improve. Trauma risks and demand for trauma care may shift geographically in relation to broader economic changes. Health care policy and planning should consider these dynamics when anticipating changing demands and needs for efforts at prevention.

Neural pathway prediction based on multi-neuron spike train data

The finding of Neural Pathway is typically based on brain slicing and reconstruction with nanoscale imaging. Nevertheless, this method is not applicable when nanoscale imaging is not available. We propose that multineuron spike train data can be used as an alternative source to predict the neural pathways, and here we discuss two concrete strategies for neural pathway prediction based on this kind of data. Multi-neuron spike train data is composed of spike trains from multiple neurons recorded in the same time interval. The data typically shows whether a specific neuron generates a spike or not at a specific time. Two strategies are proposed for neural pathway prediction: (1) The time-ordered strategy: Synapses exist between neurons that generate spikes at the two neighborhood time points (the time point N and N-1). (2) The spike co-occurrence strategy: Synapses exist between neurons that fire together at the same time point. This strategy is consistent with the Hebb’ sl aw“Cells that fire together, wire together”. Data of the rat hippocampus CA3 pyramidal cell layer based on functional Multineuron Calcium Imaging (fMCI) is used for neural pathway prediction (Including 8 datasets, and each of them records spike activities for 62 to 226 neurons. The datasets were imaged with the frequency of 10Hz [1,2]) using the upper two proposed strategies. We validate the accuracy of the neural pathway prediction strategies based on the following steps. The overall prediction accuracy is an average value based on the 8 dataset. For each of the dataset: (1) Equally divide one dataset as 20 sub datasets according to the time intervals (The sub datasets are denoted as S1,…,S20). Select the first 80% of the sub datasets (S1,…,S16 )f or neural pathway construction, and validate the pathway using the rest of the 20% sub dataset (Here we assume that if the predicted pathway is correct, it should cover the neuronal connections based on the rest of the 20% sub dataset). (2) Select another 80% of the sub datasets for neural pathway construction, and the rest for validation, and repeat this step until all the sub datasets have been selected for validation. (3) The prediction accuracy is the average value of the 20 predictions. There are several important observations and indications based on the prediction results. (1) Although the two prediction strategies seem entirely different, the neural pathways based on the two different strategies are very relevant (The correlation is significant with the Pearson correlation value 0.958). It indicates that although the proposed strategies are different, the results from the different two strategies do not have major conflicts, instead they are very consistent, and support each other. (2) The neural pathway prediction accuracy for the time-ordered strategy is 83%, and the accuracy for the spike co-occurrence strategy is 80%. When we group the two neural pathways together (denoted as the merged strategy), the prediction accuracy reaches 89% (Figure 1 shows the prediction accuracy for each of the dataset using the proposed strategies). This result indicates that better prediction can be made when the predicted pathways from the two strategies are combined together. (3) 27% of the possible connections among neurons are selected for the time-ordered strategy, while 25% of the connections are selected by the spike cooccurrence strategy. If the two results are grouped together, 32% of the possible connections are included. The results seem good, since the coverage is not high (and is consistent with the observation by using electromicroscopy techniques [3,4]), while the predicted accuracy for possible neural pathway reaches 89%. The proposed method is validated on the data in which the distance of two neurons is within approximately 400um [5]. Whether the proposal is applicable

Children’s Objective Physical Activity by Location: Why the Neighborhood Matters

Knowledge of where children are active may lead to more informed policies about how and where to intervene and improve physical activity. This study examined where children aged 6-11 were physically active using time-stamped accelerometer data and parent-reported place logs and assessed the association of physical-activity location variation with demographic factors. Children spent most time and did most physical activity at home and school. Although neighborhood time was limited, this time was more proportionally active than time in other locations (e.g., active 42.1% of time in neighborhood vs. 18.1% of time at home). Children with any neighborhood-based physical activity had higher average total physical activity. Policies and environments that encourage children to spend time outdoors in their neighborhoods could result in higher overall physical activity.

Objective Community Integration of Mental Health Consumers Living in Supported Housing and of Others in the Community

Housing programs for people with severe mental illnesses aim to maximize community integration. However, little is known about how the community integration of mental health consumers living in supported housing compares with that of other community residents in the socially disadvantaged communities where supported housing is often located. The purpose of this study was to examine predictors of objective community integration of mental health consumers living in supported housing and of other persons living in the same communities. Participants were 124 adults (60 mental health consumers and 64 other community residents) residing in designated zip codes in the Bronx, New York. Participants were administered measures of psychiatric symptoms, substance use, physical community integration (participation in local activities), social integration (interactions with community members), and citizenship (political activism or volunteering). Mental health consumers living in supported independent housing had significantly lower scores on indicators of objective community integration than other community members. However, differences were relatively small. Among mental health consumers, African-American race, education, and length of time in current residence were associated with better community integration. Findings suggest that mental health consumers living in supported housing may not achieve levels of objective community integration that are comparable with other community members; however, psychiatric factors did not account for this difference. Length of time in neighborhoods appears to be an important factor in facilitating social integration.