Neighborhood Area Research Articles

Path Loss Analysis of ZigBee for Smart Meter Network Deployment in NAN

A fundamental and vital aspect of Smart Metering infrastructure is the communication technologies and techniques associated with it, especially between the Smart Meters and the Data Concentrator Unit. Among many available communication technologies, ZigBee provides a low-cost, low-power, and easy-to-deploy network solution for a Smart Meter network. There exists limited literature that discusses ZigBee as a potential communication technology for long-range networks. Hence thorough analysis is demanded on the suitability of ZigBee for smart meter deployment under different types of environmental conditions, coverage ranges, and obstacles. This work evaluates the performance of an extended ZigBee module in outdoor as well as indoor conditions in the presence of different types of obstacles. Parameters are obtained for path loss exponent and the standard deviation of the Gaussian Random variable to validate the Log Normal Shadowing model for modeling long-range ZigBee communication. The impact of obstacles on path loss is also considered. The results show that the Log Normal Shadowing model is a good approximation for the behavior of ZigBee path loss. Accordingly, the suitability of ZigBee for a Smart Meter network spanned as a Neighborhood Area Network is also assessed based on the approximated model.

Node Allocation Strategy for Low Latency Neighborhood Area Networks in Smart Grid

Node Allocation Strategy for Low Latency Neighborhood Area Networks in Smart Grid

Bridging Built Environment Attributes and Perceived City Images: Exploring Dual Influences on Resident Satisfaction in Revitalizing Post-Industrial Neighborhoods

The deterioration of physical spaces and changes in the social environment have led to significant challenges and low life satisfaction among residents in post-industrial neighborhoods. While resident satisfaction is closely linked to the built environment, physical attributes alone do not directly influence human feelings. The perception and processing of urban environments, or city images, play a critical mediating role. Previous studies have often explored the impact of either city image perception or physical space attributes on resident satisfaction separately, lacking an integrated approach. This study addresses this gap by examining the interplay between subjective perceptions and objective environmental attributes. Unlike previous studies that use the whole neighborhood area for human perception, our study uses the actual activity ranges of residents to represent the living environment. Utilizing data from Shenyang, China, and employing image semantic segmentation technology and multiple regression methods, we analyze how subjective city image factors influence resident satisfaction and how objective urban spatial indicators affect these perceptions. We integrate these aspects to rank objective spatial indicators by their impact on resident satisfaction. The results demonstrate that all city image factors significantly and positively influence resident satisfaction, with the overall impression of the area’s appearance having the greatest impact (β = 0.362). Certain objective spatial indicators also significantly affect subjective city image perceptions. For instance, traffic lights are negatively correlated with the perception of greenery (β = −0.079), while grass is positively correlated (β = 0.626). Key factors affecting resident satisfaction include pedestrian flow, traffic flow, open spaces, sky openness, and green space levels. This study provides essential insights for urban planners and policymakers, helping prioritize sustainable updates in post-industrial neighborhoods. By guiding targeted revitalization strategies, this research contributes to improving the quality of life and advancing sustainable urban development.

Exploring the Relationship Among Financial Hardship, Anxiety, and Depression in Patients With Cancer: A Longitudinal Study.

Financial hardship (FH) is a complex issue in cancer care, affecting material conditions, well-being, and coping behaviors. This study aimed to longitudinally examine FH, anxiety, depressive symptoms, and their associations while incorporating social determinants of health and health care cost covariates in a sample of patients diagnosed with cancer. This prospective, longitudinal cohort study analyzed data from 2,305 participants from the Northwestern University Improving the Management of Symptoms during and following Cancer Treatment trial. Outcomes assessed at baseline and at 3, 6, 9, and 12 months postbaseline included depressive symptoms, anxiety, and FH. Analysis involved random intercept cross-lagged panel models to explore between- and within-person effects, incorporating factors such as age, sex, insurance status, neighborhood area deprivation, health care charges, out-of-pocket costs, and health literacy. The cohort had a mean age of 60.7 (standard deviation [SD] = 12.7) years and was mostly female (64.9%) and White (86.2%). Correlations were found between FH and depressive symptoms (r = 0.310) and anxiety (r = 0.289). A predictive relationship was observed between FH and depressive symptoms, with baseline and 6-month depressive symptom levels predicting later FH (baseline β = .079, P = .070; 6-month β = .072, P = .081) and 9-month FH significantly predicting 12-month depressive symptoms (β = .083, P = .025), even after accounting for health care charges and out-of-pocket costs. Baseline and 9-month anxiety showed a predictive relationship with subsequent FH (baseline β = .097, P = .023; 9-month β = .071, P = .068). FH emerged as a prominent issue, with nearly half of participants experiencing some level of FH. Depressive symptoms and anxiety were related to FH. These findings underscore the need for a comprehensive approach in cancer care that concurrently addresses anxiety, depressive symptoms, and FH, recognizing their interconnected impact.

Urban Green Infrastructure Connectivity: The Role of Private Semi-Natural Areas

Green spaces and blue spaces in cities provide a wealth of benefits to the urban social–ecological system. Unfortunately, urban development fragments natural habitats, reducing connectivity and biodiversity. Urban green–blue infrastructure (UGI) networks can mitigate these effects by providing ecological corridors that enhance habitat connectivity. This study examined UGI connectivity for two indicator species in a rapidly developing city in the southern United States. We mapped and analyzed UGI at a high resolution (0.6 m) across the entire city, with a focus on semi-natural areas in private land and residential neighborhoods. Integrating graph theory and a gravity model, we assessed structural UGI networks and ranked them based on their ability to support functional connectivity. Most of the potential habitat corridors we mapped in this project traversed private lands, including 58% of the priority habitat for the Golden-cheeked Warbler and 69% of the priority habitat for the Rio Grande Wild Turkey. Riparian zones and other areas with dense tree cover were critical linkages in these habitat corridors. Our findings illustrate the important role that private semi-natural areas play in UGI, habitat connectivity, and essential ecosystem services.

Social Determinants of Health and Surgical Desirability of Outcome Ranking in Older Veterans

Evaluating how social determinants of health (SDOH) influence veteran outcomes is crucial, particularly for quality improvement. To measure associations between SDOH, care fragmentation, and surgical outcomes using a Desirability of Outcome Ranking (DOOR). This was a cohort study of US veterans using data from the Veterans Affairs (VA) Surgical Quality Improvement Program (VASQIP; 2013-2019) limited to patients aged 65 years or older with inpatient stays between 2 and 30 days, merged with multiple data sources, including Medicare. Race and ethnicity data were retrieved from VASQIP, Medicare and Medicaid beneficiary summary files, the Veterans Health Administration Corporate Data Warehouse, and the United States Veterans Eligibility Trends and Statistics file. Data were analyzed between September 2023 and February 2024. Living in a highly deprived neighborhood (Area Deprivation Index >85), race and ethnicity used as a social construct, rurality, and care fragmentation (percentage of non-VA care days). DOOR is a composite, patient-centered ranking of 26 outcomes ranging from no complication (1, best) to 90-day mortality or near-death complications (6, worst). A series of proportional odds regressions was used to assess the impact of SDOH and care fragmentation adjusted for clinical risk factors, including presentation acuity (presenting with preoperative acute serious conditions and urgent or emergent surgical procedures). The cohort had 93 644 patients (mean [SD] age, 72.3 [6.2] years; 91 443 [97.6%] male; 74 624 [79.7%] White). Veterans who identified as Black (adjusted odds ratio [aOR], 1.06; 95% CI, 1.02-1.10; P = .048) vs White and veterans with higher care fragmentation (per 20% increase in VA care days relative to all care days: aOR, 1.01; 95% CI, 1.01-1.02; P < .001) were associated with worse (higher) DOOR scores until adjusting for presentation acuity. Living in rural geographic areas was associated with better DOOR scores than living in urban areas (aOR, 0.93; 95% CI, 0.91-0.96; P < .001), and rurality was associated with lower presentation acuity (preoperative acute serious conditions: aOR, 0.88; 95% CI, 0.81-0.95; P = .001). Presentation acuity was higher in veterans identifying as Black, living in deprived neighborhoods, and with increased care fragmentation. Veterans identifying as Black and veterans with greater proportions of non-VA care had worse surgical outcomes. VA programs should direct resources to reduce presentation acuity among Black veterans, incentivize veterans to receive care within the VA where possible, and better coordinate veterans' treatment and records between care sources.

Neurocognitive Profile in Pediatric Kidney Transplant Candidates: Effects of Medical and Sociodemographic Factors.

We evaluated the effects of kidney failure etiology, dialysis, and sociodemographic factors on the subdomains of intellectual functioning in pediatric kidney transplant candidates. This retrospective study included 78 pediatric kidney transplant candidates who completed a Wechsler Intelligence Scale assessment during pre-transplant neuropsychological evaluation between 1/1/2010 and 10/31/2022. Linear regression models were employed to examine the effects of kidney failure etiology, dialysis status, neighborhood area deprivation, and race on subdomains of intellectual functioning. The mean scores of various intellectual functioning domains in pediatric kidney transplant candidates were significantly lower than in the general population (ps <0.001). After adjusting for covariates, patients with congenital anomalies of the kidney and urinary tract had significantly lower processing speed (M=85; 95% CI: 79-91) compared to patients with nephrotic syndrome (M=99; 95% CI: 90-107) and other etiologies (M=84; 95% CI: 78-90) (p=0.003). Patients living in high-level deprivation neighborhoods showed lower working memory performance (M=84, 95% CI: 77-91) than patients living in median-level (M=91, 95% CI: 87-95) and low-level (M=98, 95% CI: 92-104) neighborhood area deprivation (p=0.03). Patients from marginalized racial groups demonstrated lower verbal skills (M=80, 95% CI: 74-87) than White patients (M=92, 95% CI: 88-97) (p=0.02). Additionally, patients receiving dialysis showed higher reasoning skills (M=98, 95% CI: 90-104) than patients without dialysis (M= 90, 95% CI: 86-95) (p=0.04). Neurocognitive development in pediatric kidney transplant candidates is associated with medical and sociodemographic factors. Strategies to monitor, treat, and accommodate neurocognitive concerns need to be considered to optimize long-term medical and social outcomes.

Neighborhood Factors as Correlates of Alcohol Use in the N2 Cohort Study of Black Sexual Minority Men and Transgender Women.

Sexually minoritized men (SMM), transgender women (TW), and particularly Black SMM and Black TW may be disproportionately impacted by alcohol-related problems. Few studies have empirically examined neighborhood factors that may contribute to alcohol use, specifically among these populations. Using data from the N2 longitudinal cohort study in Chicago, IL, survey data from the second wave of longitudinal assessment (n = 126), and GPS mobility data collected during study enrollment were used to evaluate neighborhood alcohol outlet availability, neighborhood disorder, and neighborhood poverty as correlates for individual alcohol use. Neighborhood exposures were measured using 200-m derived activity space areas, created from GPS data, and with publicly accessible geospatial contextual data. Separate multi-variable quasi-poison regression models tested for association between neighborhood alcohol outlet density (AOD), measured separately for on-premise (e.g. bars) and off-premise consumption outlets (e.g. liquor stores), neighborhood poverty (defined as the percentage of neighborhood areas at 150% or greater of the U.S. poverty line), exposure to vacant buildings, and neighborhood violent crime density. Separate analytical models found no significant effect between alcohol use and on-premise neighborhood AOD (IRR = 0.99, p = 0.35), off-premise consumption AOD (IRR = 0.92, p = 0.33), or neighborhood violent crime (IRR = 1.00, p = 0.65). Vacant buildings (IRR = 1.03, p = 0.05) and levels of neighborhood poverty (1.05, p = 0.01) were found to be significantly associated with increased alcohol use. Among this population, opposed to geospatial access, neighborhood measurements indicative of disorder and poverty may have greater influence on shaping alcohol use.

The 50-year spatial transition of suburban industrial parks and impacts on sustainable urbanization in the Tokyo metropolitan area

Suburban Industrial Parks (SIP) promote orderly industrial land agglomeration, contributing to sustainable land use transition. Previous studies mostly focused on socio-economic effectiveness and environmental strategies, while the multiscale spatial transition of SIPs and their roles in sustainable urbanization were not thoroughly examined. This study analyzes the temporal pattern of spatial features (TPSF) of SIPs in the Tokyo Metropolitan Area (TMA) using an original suburban industrial community (SIC) model that integrates SIP with its neighborhood. We investigated 110 SIPs established since 1959 across three investigation periods. The results reveal: 1) SIPs influence industrial land use in their neighborhood areas, mostly ranging from 500 to 2500 m. 2) SIPs’ effects on industrial land agglomerations vary over time, demonstrating three development phases: the first phase exhibits the strongest intensity of agglomeration; the second phase falls to the weak; and the third phase recovers a strong agglomeration. 3) Multiscale industrial agglomeration patterns show that 56 % of SIPs in the TMA contribute to orderly spatial transitions; 4) Main road networks facilitate industrial agglomeration. This study proposes the importance of adaptive land use management strategies for SIPs by development phases, and the SIC model could be an applicable tool for land use planning in metropolitan suburbs.

Engineered Fe3 Triangle for The Rapid and Selective Removal of Aromatic Cationic Pollutants

Heydays, water pollution is proliferating due to widespread industrialization and in- proper planning of its waste, consisting of aromatic hazards [1]. Most industries dispose of the wastewater in their neighborhood areas of the cities, which is directly in touch with several groundwater sources. The wastewater pollutant (specialties the aromatic ones) can easily enter the underground water which in turn contaminants the surface water and consequently harm the human and environment [2-5].

Penghijauan Lingkungan: Strategi Partisipatif untuk Mengoptimalkan Penanaman Tumbuhan

This community service research aims to describe the activities of planting plant varieties to support efforts to green the neighborhood area around RT 1 RW 4 Dukuh Menanggal. The author and the research subjects try to try to reforest the area with plant varieties that are suitable for urban areas in Dukuh Menanggal. This community service research uses descriptive qualitative research with observation and documentation data collection techniques and data analysis using data collection, data presentation, data condensation and data conclusion. The research method used in community service is Participatory Action Research (PAR). The results showed that plant varieties were able to optimally green the neighborhood area of RT 1 RW 4 Dukuh Menanggal.

A mathematical programming model integrating waste pickers in urban area recycling practice

Urbanization has resulted in a significant accumulation of waste in densely populated areas. Effective household waste separation practices within urban areas are crucial for advancing the concept of circular economy. The informal system has contributed remarkably to urban material circulation, but it has not been appropriately valued. Through the partnership among the municipal solid waste authorities, condominium building residents, and waste pickers, the latter can be engaged in the community to assist in household waste separation. This initiative would not only enhance their income and reduce probable occupational risks but also augment the recycling rate in the community. Based on the insights from the current waste picker assignment policy in Taichung City, this study has established a mathematical programming model by considering the overall waste separation volume and proximal distance to the neighborhood, facilitating the assignment of individual waste picker to multiple communities within the neighborhood area. Consequently, concerned waste pickers may increase their income and avoid the constraints of picking up recyclables in high-occupational-risk areas. Additionally, it may reduce the volume of unsorted garbage in the community. According to the findings of case studies, the developed model could obtain an optimal solution or acceptable approximate solution in a short timeframe. In contrast to the outcomes from the existing waste picker assignment policy, the application of the developed model could increase the total volume of sorted recyclables, which was directly proportional to the income of waste pickers, by 2.3–2.6 times.

Intra-neighborhood associations between residential greenness and blood pressure

IntroductionPrevious investigations have reported that individuals living in greener neighborhoods have better cardiovascular health. It is unclear whether the effects reported at large geographic scales persist when examined at an intra-neighborhood level. The effects of greenness have not been thoroughly examined using high-resolution metrics of greenness exposure, and how they vary with spatial scales of assessment or participant characteristics. MethodsWe conducted a cross-sectional assessment of associations between blood pressure and multiple high-resolution measures of residential area greenness in spatially concentrated HEAL Study cohort of the Green Heart Project. We employed generalized linear models, accounting for individual-level covariates, to examine associations between different high-resolution measures of greenness and blood pressure among 667 participants in a 4 sq. mile contiguous neighborhood area in Louisville, KY. ResultsIn adjusted models, we observed significant inverse associations between residential greenness, measured by leaf area index (LAI), and systolic blood pressure (SBP) within 150–250 m and 500 m of homes, but not for Normalized Difference Vegetation Index (NDVI) or grass cover. Weaker associations were also found with diastolic blood pressure (DBP). Significant positive associations were observed between LAI and SBP among participants who reported being female, White, without obesity, non-exercisers, non-smokers, younger age, of lower income, and who had high nearby roadway traffic. We found few significant associations between grass cover and SBP, but an inverse association in those with obesity, but positive associations for those without obesity. ConclusionsWe found that leaf surface area of trees around participants home is strongly associated with lower blood pressure, with little association with grass cover. These effects varied with participant characteristics and spatial scales. More research is needed to test causative links between greenspace types and cardiovascular health and to develop population-, typology-, and place-based evidence to inform greening interventions.

Cannabis Use Reported by Patients Receiving Primary Care in a Large Health System

Despite the changing legal status of cannabis and the potential impact on health, few health systems routinely screen for cannabis use, and data on the epidemiology of cannabis use, and especially medical cannabis use among primary care patients, are limited. To describe the prevalence of, factors associated with, and reasons for past-3 month cannabis use reported by primary care patients. This cross-sectional study used electronic health record data from patients aged 18 years and older who had an annual wellness visit between January 2021 and May 2023 from a primary care clinic within a university-based health system in Los Angeles, California. Factors of interest included age, race and ethnicity, sex, employment status, and neighborhood Area Deprivation Index (ADI). Cannabis use was assessed using the Alcohol Substance Involvement Screening Test (ASSIST). Patients were also asked about reasons for use, symptoms for which they used cannabis, and mode of use. Among the 175 734 patients screened, the median (range) age was 47 (18-102) years; 101 657 (58.0%) were female; 25 278 (15.7%) were Asian, 21 971 (13.7%) were Hispanic, and 51 063 (31.7%) were White. Cannabis use was reported by 29 898 (17.0%), with 10 360 (34.7%) having ASSIST scores indicative of moderate to high risk for cannabis use disorder (CUD). Prevalence of cannabis use was higher among male patients than female patients (14 939 [20.0%] vs 14 916 [14.7%]) and younger patients (18-29 years, 7592 [31.0%]; ≥60 years, 4200 [8.5%]), and lower among those who lived in the most disadvantaged neighborhoods (ADI decile 9-10, 189 [13.8%]; ADI decile 1-2, 12 431 [17.4%]). The most common modes of use included edibles (18 201 [61.6%]), smoking (15 256 [51.7%]), and vaporizing (8555 [29.0%]). While 4375 patients who reported using cannabis (15.6%) did so for medical reasons only, 21 986 patients (75.7%) reported using cannabis to manage symptoms including pain (9196 [31.7%]), stress (14 542 [50.2%]), and sleep (16 221 [56.0%]). The median (IQR) number of symptoms managed was 2 (1-4), which was higher among patients who were at moderate to high risk for CUD (4 [2-6] symptoms). In this study, cannabis use and risk of CUD were common, and more than three-quarters of patients who reported any cannabis use reported doing so to manage a health-related symptom. These findings suggest that integration of information regarding cannabis use for symptom management could help provide a crucial point-of-care opportunity for clinicians to understand their patients' risk for CUD.

LSTM-QDCNN: long short-term memory and quantum dilated convolutional neural network enabled occlusion percentage prediction

ABSTRACT Occlusion detection is vital in longer video sequences of real-time scenarios, as deformable objects generally occlude themselves or be occluded by exterior objects eventually. Occlusion can affect accuracy of the entire model significantly, because optical flow may reveal random attributes in occluded as well as neighbourhood areas. Occlusion is a challenging issue in real-world visual object tracking. Several techniques learn imprecise appearance of target while it becomes occluded by another object in the scene. Occlusion is a complicated issue that causes target loss in tracking procedure. Here, long short-term memory fused quantum dilated convolutional neural network (LSTM-QDCNN) is presented for occlusion percentage prediction. Initially, video frames are extracted from considered input video. The extracted frame is pre-processed by adaptive fuzzy filtering, and object localisation is accomplished by employing sparse fuzzy c-means with local optimal oriented pattern feature. Thereafter, occlusion category detection is conducted by deep Q-network. Lastly, occlusion percentage prediction is done by utilising LSTM-QDCNN. Additionally, LSTM-QDCNN is designed by merging deep LSTM (DLSTM) with quantum dilated convolutional neural network (QDCNN). Furthermore LSTM-QDCNN achieved maximum multiple-object tracking precision of 0.892 and minimum tracking distance of 3.714. In addition, LSTM-QDCNN achieved tracking number about 18.

Demand side management with wireless channel impact in IoT-enabled smart grid system

Demand Side Management (DSM) is a vital issue in smart grids, given the time-varying user demand for electricity and power generation cost over a day. On the other hand, wireless communications with ubiquitous connectivity and low latency have emerged as a suitable option for smart grid. The design of any DSM system using a wireless network must consider the wireless link impairments, which is missing in existing literature. In this paper, we propose a DSM system using a Real-Time Pricing (RTP) mechanism and a wireless Neighborhood Area Network (NAN) with data transfer uncertainty. A Zigbee-based Internet of Things (IoT) model is considered for the communication infrastructure of the NAN. A sample NAN employing XBee and Raspberry Pi modules is also implemented in real-world settings to evaluate its reliability in transferring smart grid data over a wireless link. The proposed DSM system determines the optimal price corresponding to the optimum system welfare based on the two-way wireless communications among users, decision-makers, and energy providers. A novel cost function is adopted to reduce the impact of changes in user numbers on electricity prices. Simulation results indicate that the proposed system benefits users and energy providers. Furthermore, experimental results demonstrate that the success rate of data transfer significantly varies over the implemented wireless NAN, which can substantially impact the performance of the proposed DSM system. Further simulations are then carried out to quantify and analyze the impact of wireless communications on the electricity price, user welfare, and provider welfare.

Influence of social capital on the stunting incidence in the south region of Cikarang sub-district, Bekasi regency, in 2022: a cross- sectional study

ABSTRACT Introduction: The problem of stunting is caused by many factors, including geographical, social, and political problems. The role of social capital has been shown to impact economic development and health in some countries, including the problem of stunting, but has not been widely studied. The purpose of this study is to analyze the effect of social capital on the incidence of stunting in South Cikarang Sub District, Bekasi Regency. Methods: The type of study is observational with a cross-sectional design. The population had 106 neighborhoods as respondents, with simple random sampling technique, and a total sample of 91 people. The research instrument uses a questionnaire whose validity and reliability have been tested. The independent variables studied were 7 variables. Univariate in the form of frequency distribution, Bivariate analysis using chi square followed by logistic regression to obtain a predictive model using SPSS Version 24. Result: 65.9% of neighborhoods have stunting rates in their area. This figure is still above the government's target. Of the 7 variables studied, all of them are related to the incidence of stunting. Suprastructure variables with p=0.001, community participation with p=0.004, community empowerment in development with p=0.001, community control in development and stunting events with a value of p=0.001, motives of empowerment in development with p=0.004, the existence of cadres as agents of change with p=0.001, involvement of various stakeholders with p=0.001. The most dominant factor is the suprastructure of various stakeholders, with p=0.001. The most dominant factor is the t variable, with OR=49.398 at 95% of CI (3.623–673.6). Predictive models are produced so that preventive efforts are expected to reduce the incidence of stunting. Conclusion: The high incidence of stunting in the neighborhood area shows that the social capital of the community still needs to be improved in handling stunting. Community groups in the neighborhoods that have poor suprastructure will be at risk of their area having a stunting incidence.Keywordsocial capital, stunting, incidence

Uncrewed Vehicle Pathfinding Approach Based on Artificial Bee Colony Method

The presented study is dedicated to the dynamic pathfinding problem for UV. Since the automation of UV movement is an important area in many applied domains like robotics, the development of drones, autopilots, and self-learnable platforms, we propose and study a promising approach based on the algorithm of swarm AI. Given the 2D environment with multiple obstacles of rectangular shape, the task is to dynamically calculate a suboptimal path from the starting point to the target. The agent has been represented as UV in 2D space and should find the next optimal movement point from the current position only within a small neighborhood area. This area has been defined as a square region around the current agent’s position. The size of the region has been determined by the attainability of the agent's scanning sensors. If the obstacle is detected by the agent, the latter should be taken into consideration while calculating the next trajectory point. To perform these calculations, the ABC metaheuristic, one of the best representatives of swarm AI, has been used. The validation of the proposed approach has been performed on several 2D maps with different complexity and number of obstacles. Also, to obtain the proper configuration, an inverse problem of identification of guided function weights has been formulated and solved. The outlined results show the perspective of the proposed approach and can complement the existing solutions to the pathfinding problem.

Automatic Building Roof Plane Extraction in Urban Environments for 3D City Modelling Using Remote Sensing Data

Delineating and modelling building roof plane structures is an active research direction in urban-related studies, as understanding roof structure provides essential information for generating highly detailed 3D building models. Traditional deep-learning models have been the main focus of most recent research endeavors aiming to extract pixel-based building roof plane areas from remote-sensing imagery. However, significant challenges arise, such as delineating complex roof boundaries and invisible boundaries. Additionally, challenges during the post-processing phase, where pixel-based building roof plane maps are vectorized, often result in polygons with irregular shapes. In order to address this issue, this study explores a state-of-the-art method for planar graph reconstruction applied to building roof plane extraction. We propose a framework for reconstructing regularized building roof plane structures using aerial imagery and cadastral information. Our framework employs a holistic edge classification architecture based on an attention-based neural network to detect corners and edges between them from aerial imagery. Our experiments focused on three distinct study areas characterized by different roof structure topologies: the Stadsveld–‘t Zwering neighborhood and Oude Markt area, located in Enschede, The Netherlands, and the Lozenets district in Sofia, Bulgaria. The outcomes of our experiments revealed that a model trained with a combined dataset of two different study areas demonstrated a superior performance, capable of delineating edges obscured by shadows or canopy. Our experiment in the Oude Markt area resulted in building roof plane delineation with an F-score value of 0.43 when the model trained on the combined dataset was used. In comparison, the model trained only on the Stadsveld–‘t Zwering dataset achieved an F-score value of 0.37, and the model trained only on the Lozenets dataset achieved an F-score value of 0.32. The results from the developed approach are promising and can be used for 3D city modelling in different urban settings.

Measuring spatial inequality of urban park accessibility and utilisation: A case study of public housing developments in Auckland, New Zealand

Poor spatial accessibility of urban green spaces affects disadvantaged populations, who are at greater risk of socioeconomic related health inequalities. We analyse mobile phone locational data from the Auckland Region of New Zealand to connect a user's 'home' location to nearby urban parks, specifically focusing on public housing tenants - a highly vulnerable group at greater risk of health inequalities.We use a number of traditional accessibility measures and four new park utilisation analyses to explore the relationship between accessibility to urban parks, neighbourhood area deprivation, and user's patterns of actual park usage. We develop four new metrics to assist in understanding the actual utilisation of green spaces available to residents: distance between closest park available and visited (DAT), used park opportunities (UPO), locality of park visits (LPV), and nearest park visitation rate (NPVR).We found that neighbourhoods with the most public housing tend to have a lower number of parks accessible, and those that are accessible are smaller in size. Additionally, people living in these areas tend to travel farther to access urban parks compared to areas with no public housing. More deprived neighbourhoods were revealed to have lower accessibility to urban greens in Auckland, highlighting the urgency to address the emerging divisions in accessibility and opportunities for these neighbourhoods. Our findings have important implications for policy-makers and urban planners. Furthermore, the use of mobile phone locational data can reveal insights into the mobility behaviour of users and can be applied to other locations and population groups.