Intersection Density Research Articles

Bayesian Mediation Analysis with an Application to Explore Racial Disparities in the Diagnostic Age of Breast Cancer

A mediation effect refers to the effect transmitted by a mediator intervening in the relationship between an exposure variable and a response variable. Mediation analysis is widely used to identify significant mediators and to make inferences on their effects. The Bayesian method allows researchers to incorporate prior information from previous knowledge into the analysis, deal with the hierarchical structure of variables, and estimate the quantities of interest from the posterior distributions. This paper proposes three Bayesian mediation analysis methods to make inferences on mediation effects. Our proposed methods are the following: (1) the function of coefficients method; (2) the product of partial difference method; and (3) the re-sampling method. We apply these three methods to explore racial disparities in the diagnostic age of breast cancer patients in Louisiana. We found that African American (AA) patients are diagnosed at an average of 4.37 years younger compared with Caucasian (CA) patients (57.40 versus 61.77, p< 0.0001). We also found that the racial disparity can be explained by patients’ insurance (12.90%), marital status (17.17%), cancer stage (3.27%), and residential environmental factors, including the percent of the population under age 18 (3.07%) and the environmental factor of intersection density (9.02%).

Relationships Between Measures of the Physical Activity-Related Built Environment and Excess Weight in Preschoolers: A Retrospective, Population-Level Cohort Study.

Background: The built environment can impact health outcomes. Our purpose was to examine relationships between built environment variables related to physical activity and excess weight in preschoolers. Methods: In this retrospective, population-level study of 4- to 6-year-olds, anthropometric measurements were taken between 2009 and 2017 in Calgary and Edmonton, Alberta, Canada. Based on BMI z-scores (BMIz), children were classified as normal weight (-2 ≤ BMIz <1) or excess weight (BMIz ≥1; overweight and obesity). Physical activity-related built environment variables were calculated (distances to nearest playground, major park, school; street intersection density; number of playgrounds and major parks within an 800 m buffer zone). Binomial logistic regression models estimated associations between physical activity-related built environment variables and excess weight. Results: Our analysis included 140,368 participants (females: n = 69,454; Calgary: n = 84,101). For Calgary, adjusted odds ratios (aORs) showed the odds of excess weight increased 1% for every 100-intersection increase [1.010 (1.006-1.015); p < 0.0001] and 13.6% when there were ≥4 playgrounds (vs. 0 or 1) within an 800 m buffer zone [1.136 (1.037-1.243); p = 0.0059]. For Edmonton, aORs revealed lower odds of excess weight for every 100 m increase in distances between residences to nearest major park [0.991 (0.986-0.996); p = 0.0005] and school [0.992 (0.990-0.995); p < 0.0001]. The odds of excess weight decreased as the number of major parks within the 800 m buffer zone increased from 0 to 1 [0.943 (0.896-0.992); p = 0.023] and from 0 to ≥3 [0.879 (0.773-0.999); p = 0.048]. Conclusion: The physical activity-related built environment was associated with excess weight in preschoolers, although relationships varied between cities that differed demographically and geographically.

Identifying Important Roadway Corridor Factors which Affect Crashes Influenced by Unsafe Driving Behaviors to Help Law Enforcement Proactively Reduce Crashes

Effective allocation of traffic law enforcement officers can proactively reduce crashes by stopping unsafe drivers before crashes occur, but this can be difficult because of staffing limitations. Therefore, it is important to identify roadway factors that could encourage unsafe driving behaviors so appropriate proactive enforcement methods can be taken. In this paper, signalized arterial roadway corridors in Florida are modeled to identify the important factors that affect the crash rate for seven unsafe driving behaviors. Using a standardized corridor definition, 406 corridors in seven urban Florida counties were identified. These corridors contained 18,518 crashes, including 5053 fatal and injury (FI) crashes, from 2017 to 2021 involving the studied behaviors. Three random forest regression models were developed to identify the important corridor-level factors that affect the crash rate for all crashes (model 1), FI crashes (model 2), and only FI crashes caused by careless or reckless driving (model 3). Corridors in Pasco County, corridors with greater signalized intersection densities, and corridors with six or more lanes had above average predicted crash rates for all three models. In addition, corridors with speed limits greater than 45 mph (model 1), two lanes (models 1 and 3), no school zones (models 1 and 3), bike lanes (models 2 and 3), and no horizontal curvature (models 2 and 3) were predicted to have higher than average crash rates. By considering these factors when making allocation decisions, law enforcement agencies can likely reduce crashes, injuries, and fatalities caused by these unsafe behaviors.

Exploring the Relationship between Urban Vibrancy and Built Environment Using Multi-Source Data: Case Study in Munich

Urbanization has profoundly reshaped the patterns and forms of modern urban landscapes. Understanding how urban transportation and mobility are affected by spatial planning is vital. Urban vibrancy, as a crucial metric for monitoring urban development, contributes to data-driven planning and sustainable growth. However, empirical studies on the relationship between urban vibrancy and the built environment in European cities remain limited, lacking consensus on the contribution of the built environment. This study employs Munich as a case study, utilizing night-time light, housing prices, social media, points of interest (POIs), and NDVI data to measure various aspects of urban vibrancy while constructing a comprehensive assessment framework. Firstly, the spatial distribution patterns and spatial correlation of various types of urban vibrancy are revealed. Concurrently, based on the 5Ds built environment indicator system, the multi-dimensional influence on urban vibrancy is investigated. Subsequently, the Geodetector model explores the heterogeneity between built environment indicators and comprehensive vibrancy along with its economic, social, cultural, and environmental dimensions, elucidating their influence mechanism. The results show the following: (1) The comprehensive vibrancy in Munich exhibits a pronounced uneven distribution, with a higher vibrancy in central and western areas and lower vibrancy in northern and western areas. High-vibrancy areas are concentrated along major roads and metro lines located in commercial and educational centers. (2) Among multiple models, the geographically weighted regression (GWR) model demonstrates the highest explanatory efficacy on the relationship between the built environment and vibrancy. (3) Economic, social, and comprehensive vibrancy are significantly influenced by the built environment, with substantial positive effects from the POI density, building density, and road intersection density, while mixed land use shows little impact. (4) Interactions among built environment factors significantly impact comprehensive vibrancy, with synergistic interactions among the population density, building density, and POI density generating positive effects. These findings provide valuable insights for optimizing the resource allocation and functional layout in Munich, emphasizing the complex spatiotemporal relationship between the built environment and urban vibrancy while offering crucial guidance for planning.

Non-linear effects of built environment factors on mode choice: A tour-based analysis

Understanding the connections between the built environment and travel mode choice is a major research topic in transportation. However, existing studies usually examine the relationship through trip-based analyses rather than tour-based approaches. A tour consists of multiple trips that originate and end at the same place, which is increasingly considered the more appropriate analysis unit for travel behaviors. Applying a tour-based approach, this study employs random forest to investigate the non-linear impacts of built environment factors and tour attributes on different mode combinations of a tour. We find that tour attributes and connectivity-related variables (e.g., block size and intersection density) have a strong association with the use of active travel modes when their values are within a certain threshold. In addition, capturing mode change behaviors offers more nuanced understanding of how various built environment variables shape people’s decision to combine modes in a tour.

Cross-sectional associations between neighborhood characteristics, cognition and dementia risk factor burden in middle-aged and older Australians

Dementia disproportionately affects individuals from disadvantaged backgrounds, including those living in areas of lower neighborhood-level socioeconomic status. It is important to understand whether there are specific neighborhood characteristics associated with dementia risk factors and cognition which may inform dementia risk reduction interventions. We sought to examine whether greenspace, walkability, and crime associated with the cumulative burden of modifiable dementia risk factors and cognition. This was a cross-sectional analysis of 2016–2020 data from the Healthy Brain Project, a population-based cohort of community-dwelling individuals across Australia. Participants were aged 40–70 and free of dementia. Measures included greenspace (greenspace % in the local area, and distance to greenspace, n = 2,181); and intersection density (n = 1,159), and crime (rate of recorded offences; n = 1,159). Outcomes included a modified Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) dementia risk score to index the burden of modifiable vascular dementia risk factors; and composite scores of both memory and attention, derived from the Cogstate Brief Battery. Linear regressions adjusted for age, sex, education, and personal socio-economic status, demonstrated distance to greenspace (b ± SE per 2-fold increase = 0.09 ± 0.03, p =.005) and crime rate (b ± SE per 2-fold increase = 0.07 ± 0.03, p =.018) were associated with higher modified CAIDE. Higher crime was associated with lower memory performance (b ± SE = -0.03 ± 0.01, p =.018). The association between distance to greenspace and modified CAIDE was only present in low-moderate socioeconomic status neighborhoods (p interaction = 0.004). Dementia prevention programs that address modifiable risk factors in midlife should consider the possible role of neighborhood characteristics.

Identifying Corridor-Level Safety Improvements for Urban and Suburban Arterials in Florida Within a Safe System Framework

Many agencies have adopted a Safe System approach to improving roadway safety. The Highway Safety Manual (HSM) provides methods for assessing safety, but these models are site-specific and require extensive data, making them difficult to use at large scales. This paper develops a corridor-level methodology for holistically looking at corridors made of consecutive intersections and roadway segments to identify safety improvements which align with the Safe System approach while requiring less data than HSM methods. Using a standardized definition, a total of 549 corridors on urban and suburban arterials across Florida were identified which experienced over 10,000 fatal and serious injury (FSI) crashes from 2017 through 2021. A negative binomial regression model was developed to predict mean FSI (MFSI) crashes at the corridor level (using corridor length as exposure), with the predicted values adjusted using the Empirical Bayes method to provide more accurate results. The significant factors in the model were traffic volume, intersection densities and sizing, area type, bus stop presence, citation rate, and corridor lighting presence. Increasing citation rates (citations/year/mile) for unsafe driving behaviors by one unit was predicted to reduce MFSI crash frequency in corridors by 2%, and corridors without lighting were predicted to experience 2.79 times more MFSI crashes compared with corridors with lighting. Two sister corridors in South Florida with similar roadway characteristics but different crash frequencies were also analyzed. Improvements to lighting and access control in the identified high-risk corridor could help reduce FSI crashes. Overall, this corridor approach can help agencies proactively improve roadway safety.

Geographically weighted machine learning for modeling spatial heterogeneity in traffic crash frequency and determinants in US

Spatial analyses of traffic crashes have drawn much interest due to the nature of the spatial dependence and spatial heterogeneity in the crash data. This study makes the best of Geographically Weighted Random Forest (GW-RF) model to explore the local associations between crash frequency and various influencing factors in the US, including road network attributes, socio-economic characteristics, and land use factors collected from multiple data sources. Special emphasis is put on modeling the spatial heterogeneity in the effects of a factor on crash frequency in different geographical areas in a data-driven way. The GW-RF model outperforms global models (e.g. Random Forest) and conventional geographically weighted regression, demonstrating superior predictive accuracy and elucidating spatial variations. The GW-RF model reveals spatial distinctions in the effects of certain factors on crash frequency. For example, the importance of intersection density varies significantly across regions, with high significance in the southern and northeastern areas. Low-grade road density emerges as influential in specific cities. The findings highlight the significance of different factors in influencing crash frequency across zones. Road network factors, particularly intersection density, exhibit high importance universally, while socioeconomic variables demonstrate moderate effects. Interestingly, land use variables show relatively lower importance. The outcomes could help to allocate resources and implement tailored interventions to reduce the likelihood of crashes.

Investigating equitable access of children and seniors to urban green spaces and its relationship with neighborhood walkability: a case study of Tehran, Iran

ABSTRACT This study investigates the distribution of urban green spaces for children and seniors in Tehran, delving into their connection with demographic attributes and the walkability of neighborhoods. The findings reveal an unequal distribution, confirmed by the Gini index for children (0.57) and seniors (0.62). Moreover, the Multiple Regression Analysis revealed that the apartment occupancy rate (r = –0.19) and migrant rate (r = –0.13) were inversely correlated with seniors’ access to green spaces, whereas the employment rate (r = 0.16), intersection density (r = 0.27), and land use mix (r = 0.20) exhibited positive associations. Additionally, concerning children, the findings indicated positive correlations between intersection density (r = 0.25) and land use mix (r = 0.12) and their access to green spaces. These findings underscore the importance of adopting a co-benefits strategy and taking into account demographic characteristics and neighborhood walkability in urban planning and decision-making to ensure fair access to urban green spaces for vulnerable populations. Implementing strategies like establishing green spaces in areas with lower walkability and improving neighborhood connectivity can greatly improve the accessibility of urban green spaces for both children and seniors.

Non-linear and synergistic effects of built environment factors on older adults’ walking behavior: An analysis integrating LightGBM and SHAP

Examining the relationship between the built environment and older adults’ walking behavior is of critical importance for the development of aging-friendly cities and communities. Previous studies, however, have paid limited attention to the non-linear and synergistic effects of built environment factors. To this end, based on multi-source data such as the Travel Characteristic Survey of Hong Kong and Google Street View imagery, this study integrates two advanced machine learning models—light gradient-boosting machine (LightGBM) and SHapley Additive exPlanations (SHAP)—to analyze the non-linear and synergistic effects of various built environment factors on older adults’ walking time. The results show that the effect of the built environment is largely non-linear. Critical built environment factors include access to recreational facilities and land-use mix. Access to metro and parks, however, plays a marginal role in affecting older adults’ walking. Furthermore, the synergistic effects of built environment variable pairs (e.g., access to recreational facilities and intersection density) are also identified.

Navigating active Transit: How built environments shape commuting and leisure journeys

This study offers a nuanced investigation into the complexities of active travel behavior in low and middle-income countries (LMICs), with a specific focus on the Mumbai Metropolitan Region (MMR) in India. Utilizing a dual-method approach, the research employs Poisson regression models to analyze the frequency of active commuting and Cox proportional hazards models to explore the duration of such travel. The study reveals that urban design elements, socio-economic factors, and individual demographics collectively shape travel behavior, often in unexpected ways. Contrary to prevailing assumptions, walkable neighborhoods and enhanced public transit accessibility do not universally encourage active commuting. For example, while increased bus stop availability boosts the frequency of work-related active trips, its impact on leisure travel is minimal. Similarly, higher intersection density, generally associated with greater walkability, unexpectedly leads to longer travel durations. The study also finds that proximity to urban centers like the Central Business District exhibits a dual influence—promoting active commuting for work but slightly reducing it for leisure activities. These multifaceted findings underscore the need for more context-specific, multidimensional approaches in urban planning and policy formulation. Future research aims to expand upon these insights through quasi-experimental methods to better understand the socio-cultural, economic, and environmental intricacies influencing travel behavior in LMICs.

Managing Landscape Urbanization and Assessing Biodiversity of Wildlife Habitats: A Study of Bobcats in San Jose, California

In the rapid process of urbanization, crucial habitats for mid-sized felids such as bobcats are increasingly compromised. This study employs Geographic Information System (GIS) tools and Machine Learning to investigate the subtle impacts of urbanization on bobcat habitats. Focused on the San Jose area, our extensive geospatial analysis has developed a complex ecological model for bobcat habitats. Our findings emphasize the significant influence of factors like vegetation cover, water body distribution, road traffic volume, and intersection density on the suitability of habitats for bobcats. Specifically, we discovered that while vegetation cover typically supports habitat suitability, its proximity to busy roads significantly undermines this advantage, indicating a need for strategic urban planning that incorporates wildlife mobility. By synthesizing natural and urban elements, we offer fresh insights into urban ecosystem management and propose specific conservation tactics: identifying optimal wildlife crossings, integrating corridors with urban infrastructure, and placing fencing and signage strategically to facilitate wildlife movement safely. These measures aim to reduce road-related threats and enhance the integrity of natural habitats, strengthening bobcat conservation efforts. More than its direct implications for bobcat conservation, this study offers actionable insights for urban wildlife conservation and introduces innovative methods for assessing and mitigating the broader ecological impacts of urbanization.

Association between network characteristics and bicycle ridership across a large metropolitan region

Numerous studies have explored associations between bicycle network characteristics and bicycle ridership. However, the majority of these studies have been conducted in inner metropolitan regions and as such, there is limited knowledge on how various characteristics of bicycle networks relate to bicycle trips within and across entire metropolitan regions, and how the size and composition of study regions impact on the association between bicycle network characteristics and bicycle ridership. We conducted a retrospective analysis of household travel survey data and bicycle infrastructure in the Greater Melbourne region, Australia. Seven network metrics were calculated (length of the bicycle network, betweenness centrality, degree centrality, network density, network coverage, intersection density and average weighted slope) and Bayesian spatial models were used to explore associations between these network characteristics and bicycle ridership. We demonstrated that bicycle ridership was associated with several network characteristics, and that these characteristics varied according to the outcome (count of the number of trips made by bike or the proportion of trips made by bike) and the size and characteristics of the study region. These findings challenge the utility of approaches based on spatially modeling network characteristics and bicycle ridership when informing the monitoring and evaluation of bicycle networks. Further efforts are required to be able to quantify network characteristics that reflect the myriad of factors that influence comfort and safety for people of all ages and abilities.

Conventional or parking-protected bike lanes? A Full-Bayesian before-and-after assessment

Objective Biking infrastructure plays a crucial role in ensuring cyclists’ safety and encouraging more people to bike. Recently, many North American municipalities started to adopt a new bike lane design, namely the parking-protected bike lane (PPBL), in which the bike lane is placed between the sidewalk and the parking lane. This study aims to assess the safety impacts associated with converting conventional bike lanes (CBLs) to PPBLs. Methods To that end, collision and traffic data were collected at 19 street sections from three corridors in Vancouver and Ottawa before and after the conversion. Poisson-Lognormal Linear Intervention model was developed to undertake a Full Bayesian before-and-after analysis to evaluate the change in the frequency of bike-vehicle collisions and other collaterally affected collisions (i.e., total and rear-end collisions) after implementing PPBLs. Results Reductions of 31.2%, 16.5%, and 4.4% were observed for total, rear-end, and bike collisions, respectively, after implementing the PPBLs, but the results varied significantly depending on the corridor characteristics. Conclusion Overall, PPBLs demonstrated positive impacts on cyclist safety in some corridors, but their performance is highly sensitive to bike path opening density, intersection density, and intersection treatments.

Ground Truthing: Perceived Versus Objectively Measured Built Environment Attributes in South African Communities

Introduction :No ground truthing study of ‘neighbourhood walkability’ has been conducted in Africa that contrasts the lived experience of the neighbourhood-built environment against objectively measured attributes using GIS and subscales of the NEWS-Africa instrument. Methods: This is a cross-sectional survey and observation study. A convenient sample of residents (n=52) aged 18-65 years from four urban suburbs in low- and high-income settings, self-reported transport, leisure-time physical activity (PA) and device-measured moderate-to-vigorous PA (MVPA) were assessed. Built environment constructs was derived from individual-level street network measures (1000m buffer, using ArcGIS 10.51). We also assessed PA between the four groups based on income and GIS walkability. Results: There was no significant relationship between self-reported MVPA and GIS-measured walkability in the study sample. Only intersection density had a significant inversely association with moderate and MVPA (rho=-0.31, P&lt;0.05) in the overall study sample. Self-report transport PA differed between groups (P&lt;0.013). Device-measured vigorous activity differed between groups and was lower in both Low-SES groups compared to the High-SES/Low Walkable group (P&lt;0.04). Total PA was not associated with GIS measured walkability, but inversely associated with positive perceived neighbourhood attributes. Conclusions: There is a modest inverse association between device-measured PA and GIS-measured intersection density. These results suggest that constructs of walkability, whether measured or perceived, may predict volitional and utilitarian PA differently in our study population.

Examining the impact of the urban transportation system on tangible and intangible vitality at the city-block scale in Nanjing, China

Cities’ transportation systems have substantial impacts on urban vitality. Given the increasing availability of data on residents’ activities, cities’ tangible/intangible vitality can be analyzed more accurately. This study examined the associations of tangible and intangible vitality with transportation system features, specifically exploring various transportation modes’ accessibility, features related to block forms, and border vacuums at a block scale across different urban areas. Nanjing, China, was analyzed as a case study. Our findings reveal a declining gradient of urban vitality from the Old Town to the Main City and the New Area. Consequently, we suggest prioritizing efforts to enhance urban vitality in the New Area, particularly in its low-vitality blocks. Strategies for improvement include increasing public transportation accessibility and road density, which can positively influence the overall vitality of the entire city. Improving active travel accessibility has a positive impact on tangible vitality, while enhancing automobile accessibility potentially contributes to intangible vitality. Negative border effects of large transportation projects on tangible vitality should be mitigated. Interestingly, we found that intersection density has opposite effects on tangible and intangible vitality. These insights offer valuable guidance for urban planners aiming to enhance vitality levels across an entire city or within specific areas.

On the intersection density of the Kneser graph [formula omitted

A set F⊂Sym(V) is intersecting if any two of its elements agree on some element of V. Given a finite transitive permutation group G≤Sym(V), the intersection densityρ(G) is the maximum ratio |F||V||G| where F runs through all intersecting sets of G. The intersection densityρ(X) of a vertex-transitive graph X=(V,E) is equal to maxρ(G):G≤Aut(X),G transitive. In this paper, we study the intersection density of the Kneser graph K(n,3), for n≥7. The intersection density of K(n,3) is determined whenever its automorphism group contains PSL2(q), with some exceptional cases depending on the congruence of q. We also briefly consider the intersection density of K(n,2) for values of n where PSL2(q) is a subgroup of its automorphism group.

Children's Independent Mobility, School Travel, and the Surrounding Neighborhood

IntroductionChildren's active travel to and from school (AST) and children's independent mobility (CIM) are consistently positively associated with physical activity (PA); however, few researchers have investigated associations between objective measures of the environment and indicators of AST and CIM in national samples. MethodsA national sample of 2,067 Canadian parents of 7- to 12-year-old children was recruited in December 2020. Regression analyses were used in 2023 to assess the association between geographic information system measures of park density, blue space, population density, greenspace, intersection density, and CIM and AST to and from school. ResultsChildren in areas with high versus low park density (>0.025 vs. ≤0.025) had higher odds of travel to school via active modes (OR: 1.47(1.14, 1.91), p=0.003). Children in neighborhoods in the highest quartile for neighborhood greenspace (Normalized Difference Vegetation Index) were more likely to travel to home actively than those in areas of lower greenspace (OR: 1.70 (1.18, 2.45), p=0.004). On average, children living in areas in the highest versus the lowest quartile for intersection density were more likely to engage in AST to (OR:2.43(1.58, 3.75), p<0.001) and from (OR:2.77(1.80, 4.29), p<0.001) school. ConclusionsThe observed associations underscore a need for city planners and policymakers to ensure sufficient access to parks and neighborhood greenspace, especially if findings are confirmed in longitudinal studies. More research is needed to investigate the role of intersection density in supporting AST and CIM.

Ineffective built environment interventions: How to reduce driving in American suburbs?

Designing effective built environment policies to reduce auto use is key to promoting sustainable transportation in suburban areas. However, most studies on the association between the built environment and auto use focus on the entire region rather than suburban areas. In addition, previous studies often ignore the possible nonlinear association between them. Applying Gradient Boosting Decision Trees to the data in the Twin Cities, USA, this study explores the nonlinear relationships between built environment attributes and driving distance in suburban areas and illustrates how the relationships differ from those in urban areas. The results show that suburban residents are less sensitive to the built environment than urban residents. More importantly, built environment policies that work in urban areas might be infertile in suburban areas. Although many studies have advocated population densification and mixed-use development for driving mitigation, this study suggests that these policies are ineffective in suburban areas. Instead, promoting job accessibility and densifying intersection density are promising to reduce auto use in suburban areas. Densifying transit stops has a small but nontrivial contribution to mitigating auto use.

A dramatic morphological transformation from a spontaneous, organic street pattern to pre-planned-based order: learning from Baghdad city

ABSTRACT A city can be characterised as a measurable pattern that comprises streets surrounded by block patterns. These street patterns are largely responsible for connectivity and accessibility, which enable people to move and interact. The study compares four samples in a case study in Baghdad, Iraq. The assets of Baghdad today belong to its historical period as its prevailing organic zigzagged network pattern determine its character. However, urban areas outside the historical zone are constructed according to modern schemes and a modernist ideology. This paper tests the connectivity and accessibility of the street network for four selected neighbourhoods. To achieve this, five indicators were applied, namely: intersection density Id, street density Sd, link to node ratio LNR, internal node connectivity INC and external point connectivity EPC. Following that, a correlation for each street parameter within the selected regions was analysed to identify the main characteristics of each variable in each area. Besides connectivity and accessibility, the paper’s aim is to characterise the historical neighbourhood of Baghdad by comparing it with other chosen areas. This study applies two stages to its case study; the first reviews traditional urban pattern and compares this to the other three examples. The second stage includes the selection of three different samples within the same city, where every sample has several common variables. Adopting a quantitative method, the outcomes revealed variation across the characteristics and parameters of the street pattern within the four chosen neighbourhoods. This finding offer a precise platform and key plan for future urban design strategies and planning methods. The research concluded that a greater understanding of street patterns and parameters could help to improve the built environment and the transformation processes of the urban form, particularly when considering walkability, connectivity, accessibility, and sustainability.