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Urban Development

Urban Development: A multifaceted process that encompasses the planning, design, and management of cities and their surrounding areas.
This field addresses the social, economic, environmental, and spatial aspects of urban growth and transformation, with the aim of creating livable, sustainable, and equitable communities.
Urban Development encompasses a wide range of topics such as infrastructure, housing, transportation, land use, and public services.
Researchers and professionals in this field utilize interdisciplinary approaches to tackle complex urban challenges and improve the quality of life for urban residents.
Effective Urban Development strategies are crucial for addressing issues like population growth, resource scarcity, and climate change in the built environment.

Most cited protocols related to «Urban Development»

1
Aging, Memory, and Neurological Evaluation
The Memory and Aging Project is funded by the National Institute on Aging and was approved by the Institutional Review Board of Rush University Medical Center. Older persons without known dementia must agree to an assessment of risk factors, blood donation, and a detailed clinical evaluation each year. Further, all participants also agree to donation of brain, the entire spinal cord, and selected nerve and muscles at the time of death.
Study participants are primarily recruited from retirement communities throughout northeastern Illinois Fig. (1). The study primarily enrolls residents of continuous care retirement communities. Several features of these facilities and the study design enhance the validity and generalizability of the study. Because the only exclusion is the inability to sign the Anatomical Gift Act, and because all clinical evaluations are performed as home visits, co-morbidities common in population-based epidemiologic studies are well represented; this reduces the “healthy volunteer effect” seen in many cohort studies [30 (link)]. The home visits reduce participant burden facilitating high rates of follow-up. Follow-up rates are further enhanced because these facilities provide all levels of care from independent living to unskilled and skilled nursing on campus. This also enhances autopsy rates as many participants die on campus and the Anatomical Gift Act allows us to work directly with facility staff and the funeral home to arrange the autopsy. Residents of continuous care retirement communities are predominantly white and tend to be more affluent. Therefore, the study also recruits from Section 8 and Section 202 housing subsidized by the Department of Housing and Urban Development, retirement homes, and through local churches and other social service agencies serving minorities and low-income elderly.
The study design allows the following types of analyses to be conducted within a single dataset Fig. (2): 1) the relation of risk factors with incident AD, incident MCI, and decline in cognitive and motor function; 2) the relation of neurobiologic indices with AD, MCI, and cognitive and motor function; and 3) modeling neurobiologic pathways linking risk factors to clinical phenotypes.
Bennett D.A., Schneider J.A., Buchman A.S., Barnes L.L., Boyle P.A, & Wilson R.S. (2012). Overview and Findings from the Rush Memory and Aging Project. Current Alzheimer research, 9(6), 646-663.
Publication 2012
Aged Autopsy Blood Donation Brain Cognition Continuity of Patient Care Dementia Disorders, Cognitive Ethics Committees, Research Healthy Volunteers Memory Minority Groups Nervousness Phenotype Spinal Cord Temporal Muscle Urban Development Vision Visit, Home
2
Identifying Homelessness Among US Veterans
VA records were examined for all individuals listed on an official roster indicating they had served in the U.S. military as part of the recent conflicts in Iraq or Afghanistan. Specifically, these include those who were deployed as part of Operation Enduring Freedom (OEF), Operation Iraqi Freedom (OIF), and Operation New Dawn (OND). This roster consisted of 941,970 Veterans who separated from the military after deployment as part of OEF/OIF/OND and who were eligible for and enrolled in VA health care services as of July 2011. Of these, 845,593 (89.8%) had at least one documented encounter within the VA system as of April 2012. Records from this latter group were taken from a nationwide VA research database of administrative and clinical data managed by Veterans Informatics and Computing Infrastructure (VINCI) [12 ] to ascertain homelessness.
Veterans were selected for analysis if they belonged to one of three mutually exclusive homeless groups, based on the first administrative record with any of the following homelessness-related indicators: 1) V60.0, 2) V60.x, and 3) non-ICD indicators of homelessness. The first category included all those with records of receiving a V60.0 ICD-9-CM code as part of a VA contact. The second category included those who received any of three V60.x series ICD-9-CM codes related to housing circumstances: V60.1 (inadequate housing), V60.89 (other specified housing or economic circumstances) and V60.9 (unspecified housing or economic circumstances) (see Table 1). The final category included those whose records contained at least one of a set of specific non-ICD VA clinic codes or treatment specialty codes[7 ]. These include VA-specific codes that designate the clinic where the Veteran received a particular homeless service:

522 (Department of Housing and Urban Development VA Shared Housing [HUD-VASH]);

528 (Telephone/Homeless Mentally Ill [HMI]);

529 (Healthcare for Homeless Veterans);

530 (Telephone/HUD-VASH); and

590 (Community outreach to homeless Veterans by staff)

as well as the inpatient treatment specialty codes (homeless services for hospitalized Veterans):

37 Domiciliary care for homeless Veterans (DCHV); and

28 Mental Health Residential and Rehab Treatment Program for Compensated Work Therapy/Treatment Resident (MH RRTP CWT/TR).

Only Veterans who had at least one additional VA visit in the 90 days immediately following the identification of homelessness were retained in the sample to allow for an examination of receipt of at least one service (and associated ICD-9-CM codes) after the initial homeless designation. Outpatient clinic visits in the 90 days immediately after the first indicator of homelessness were categorized as either homeless, mental health, or substance abuse/addiction services. Additionally, comorbidities among VA homeless groups were also explored by summarizing their ICD-9-CM diagnoses. VA medical center region and station identification was also obtained from the administrative data.
Peterson R., Gundlapalli A.V., Metraux S., Carter M.E., Palmer M., Redd A., Samore M.H, & Fargo J.D. (2015). Identifying Homelessness among Veterans Using VA Administrative Data: Opportunities to Expand Detection Criteria. PLoS ONE, 10(7), e0132664.
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Publication 2015
Addictive Behavior Diagnosis Drug Abuse Hospitalization Mental Health Mentally Ill Persons Military Personnel Persons, Homeless Rehabilitation Residential Treatment Substance Abuse Substance Dependence Therapeutics Urban Development Veterans
3
Quantifying Global Ocean Microplastics
In this study we determined abundances and mass of microplastics starting at the lowest size of 0.33 mm, which is a commonly used lower limit for pelagic microplastics [31] (link). The prefixes micro, meso and macro in relation to plastic pollution are poorly defined. Generally accepted microplastic boundaries are based on typical neuston net mesh size (0.33 mm) and an upper boundary of approximately 5.0 mm [31] (link). We have used 4.75 mm as our upper boundary for microplastic because this is a size for standard sieves used for sample analysis in most of the expeditions contributing data to this manuscript. Mesoplastic has a lower limit of 4.75 mm, and no defined upper limit. In this current study we set the upper boundary of mesoplastic at 200 mm, which represents a typical plastic water bottle, chosen because of its ubiquity in the ocean. Macroplastic has no established lower boundary, though we set it at 200 mm, while the upper boundary is unlimited. There is a clear need for consistent measures in the field [31] (link), and herein we followed a practical approach using commonly employed boundaries and logistic considerations (net and sieve sizes) in order to integrate an extensive dataset that covers the entire global ocean, including areas that have never been sampled before.
Of the 1571 field locations that contributed count data (Fig. 1), a total of 1333 stations also had weight data (Fig. S4). All these data were used to calibrate the numerical model prediction of plastic count and weight density [28] . For the comparison, we fit the model results to measured data by a linear system of equations of the form:
where yi is the logarithm of a measured value of plastic count density (pieces km−2) or weight density (g km−2) for each of the N number of samples. K is the number of model output cases with sij a dimensionless model solution at the location of sample yi. βk and εN are the computed weighting coefficients and the error terms for a particular dimensionless model solution sij. This method can be used to fit an arbitrary number of model output cases to any number of measured data points producing a weighting coefficient and error term for each case.
In the model we used a set of three model results (K = 3), corresponding to different input scenarios [28] : urban development within watersheds, coastal population and shipping traffic. Values of β and ε are determined for both the concentration distribution (pieces km−2) and the weight distribution (g km−2) of each of the four size classes based on the linear system of equations. To compare the model results directly to the measured data, the weighting coefficient βk computed above is used to scale the model output for each of the output scenarios.
Eriksen M., Lebreton L.C., Carson H.S., Thiel M., Moore C.J., Borerro J.C., Galgani F., Ryan P.G, & Reisser J. (2014). Plastic Pollution in the World's Oceans: More than 5 Trillion Plastic Pieces Weighing over 250,000 Tons Afloat at Sea. PLoS ONE, 9(12), e111913.
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Publication 2014
Mesoplastics Microplastics Urban Development
4
Puma Spatial Behaviors and Factors
Previous studies on pumas have explored behavior time budgets [13] , minimum area requirements [14] , dispersal of juveniles in fragmented habitat [15] , and the relationship between urban development and puma presence [16] , [17] . We expand upon these studies by using GPS data combined with extensive field reconnaissance to determine the spatial location of pumas while exhibiting four behaviors chosen to reflect both reproductive and non-reproductive activities: feeding, moving, communicating, and denning, and how these are influenced by natural and anthropogenic factors.
Wilmers C.C., Wang Y., Nickel B., Houghtaling P., Shakeri Y., Allen M.L., Kermish-Wells J., Yovovich V, & Williams T. (2013). Scale Dependent Behavioral Responses to Human Development by a Large Predator, the Puma. PLoS ONE, 8(4), e60590.
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Publication 2013
Anthropogenic Effects Puma Reproduction Urban Development
5
Comprehensive Environmental Quality Index Protocol
The data sources are described in detail elsewhere [15 (link)]. Briefly, data sources were considered for EQI inclusion based on temporal, spatial, and quality-related criteria. Temporal appropriateness required data to represent the 2000 to 2005 time period. Data sources were considered spatially appropriate if data were available at, or could be aggregated or interpolated to represent, the county level for all 50 states. Data quality, especially related to data source documentation, was determined by data source managers (in data reports and internal documentation), project investigators, and with the larger field of environmental research, through use and critique of the various data sources.
The air domain included two data sources: the Air Quality System (AQS) [16 ], which is a repository of national ambient air concentrations from monitors across the country for criteria air pollutants; and the National-Scale Air Toxics Assessment (NATA) [17 ], which uses emissions inventory data and air dispersion models to estimate non-residential ambient concentrations of hazardous air pollutants (HAPs).
The water domain comprised five data sources: Watershed Assessment, Tracking < Environmental Results (WATERS) Program Database [18 ], Estimates of Water Use in the U.S. [19 ], National Atmospheric Deposition Program (NDAP) [20 ], Drought Monitor Network [21 ], and National Contaminant Occurrence Database (NCOD) [22 ]. The WATERS Program Database is a collection of data from various EPA-conducted water assessment programs including impairment, water quality standards, pollutant discharge permits, and beach violations and closures. The Estimates of Water Use in the U.S. is calculated by the United States Geological Survey (USGS) and includes county-level estimates of water withdrawals for domestic, agricultural, and industrial uses. The NDAP dataset provides measures of chemicals in precipitation using a network of monitors located throughout the U.S. The Drought Monitor Data provides raster data on the drought status for the entire U.S. on a weekly basis. The NCOD dataset provides data from public water supplies on 69 different contaminants.
The land domain was constructed using data from five sources. The 2002 National Pesticide Use Database [23 ] estimates state-level pesticide usage based on pesticide ingredients and crop type. The 2002 Census of Agriculture [24 ] is a summary of agricultural activity, including information about crops, livestock, and chemicals used. The National Priority Site data [25 ] includes location of and information on sites that have been placed on the National Priority List (NPL), including indicators for major facilities (e.g., Superfund sites), large quantity generators, toxics release inventory, Resources Conservation and Recovery Act treatment, storage and disposal facilities, corrective action facilities, assessment, cleanup, and redevelopment exchange (brownfield sites), and section seven tracking system pesticide producing site locations. The National Geochemical Survey [26 ] contains geochemical data (e.g., arsenic, selenium, mercury, lead, zinc, magnesium, manganese, iron, etc.). The fifth source is the EPA Radon Zone Map [27 ], which identifies areas of the U.S. with the potential for elevated indoor radon levels.
The sociodemographic domain included two data sources: the U.S. Census [28 ] and Federal Bureau of Investigation (FBI) Uniform Crime Report (UCR) [29 ]. The U.S. Census collects population and housing data every 10 years, economic and government data every five years and the American Community Survey annually. FBI UCR rate data are available annually and by crime type (violent or property).
The built environment domain employed five data sources. Dun and Bradstreet collects commercial information on businesses and contains more than 195 million records [30 ]. These data are the only data used in the EQI which are not free, though they are publically available for purchase. Topographically Integrated Geocoding Encoding Reference (TIGER) [31 ] data provides maps and road layers for the U.S. at multiple units of census geography. The Fatality Analysis Reporting System (FARS) [32 ] data is a national census providing the National Highway Traffic Safety administration yearly reports of fatal injuries suffered in motor vehicle crashes. Housing and Urban Development (HUD) [33 ] data provide a count of low-rent and section-eight housing in each housing authority area, which corresponds to cities. The built environment domain also included the percent using public transportation variable from the census, which was not included in the sociodemographic domain; census data have been previously described.
Messer L.C., Jagai J.S., Rappazzo K.M, & Lobdell D.T. (2014). Construction of an environmental quality index for public health research. Environmental Health, 13, 39.
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Publication 2014
Agricultural Crops Air Pollutants Arsenic Crime Crop, Avian Droughts Environmental Pollutants Injuries Iron Livestock Magnesium Manganese Mercury Microtubule-Associated Proteins Pesticides Radon Selenium Traffic Accidents Urban Development Zinc

Most recents protocols related to «Urban Development»

1
Wuhan's Metro Accessibility during COVID-19
Wuhan is central China’s political, economic, and cultural center, with a permanent population of 13,648,900 in 2021. The city is divided into three parts—Wuchang, Hankou, and Hanyang. The confluence of the Yangtze and Han rivers form an urban pattern of two river meetings and three standing towns. Central and urban development areas mainly bound urban space. At the end of 2019, there was an outbreak of the COVID-19 pandemic in Wuhan, which led to the metro being blocked for 3 months, severely restricting people’s activities around the metro stations. After the unblocking, public transportation was gradually put into everyday use and the city’s vibrancy slowly recovered. By January 2021, Wuhan had opened and operated nine metro lines with 210 stations (transfer stations were not counted repeatedly). Using previous studies as references [28 (link)], we used an 800 m radius as the distance threshold for 210 MSAs, as shown in Fig. 2.

Study area in Wuhan, China

Peng J., Hu Y., Liang C., Wan Q., Dai Q, & Yang H. (2023). Understanding nonlinear and synergistic effects of the built environment on urban vibrancy in metro station areas. Journal of Engineering and Applied Science, 70(1), 18.
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Publication 2023
COVID 19 Radius Rivers Urban Development
2
Characterizing Agricultural Impacts on Surface Water Quality
This study was conducted in the agriculturally dominated South Nation River basin located in Eastern Ontario, Canada. The basin has a catchment area of approximately 3900 km2 (Fig. 1A). Six water sampling sites were selected in 2016 (Site: S5, S6, S10, S18, S20, and S24) and an additional three were added (S19, S21, S253) in 2017 and 2018 (Fig. 1, Table 1). These study sites have been described in detail previously [11 (link), 20 (link)]. Dairy livestock farming is a dominant land use in the basin, and liquid and solid livestock manure is frequently applied to land as fertilizer in spring and fall. Corn, soybeans, and grass or alfalfa forage are the predominant crops grown.

Study sites. Location of South Nation river basin in eastern Ontario (A); Sample locations in the river basin (B); (C) Land use features of the nine stream sites: S18, S19, S20, and S21 are agricultural drainage ditch sites, S253 (denoted as Agri_253) is located on a larger stream (Strahler stream order = 4), S5, S6, and S10 are under mixed agricultural and urban development, while S24 is in a forested area under no known influence of anthropogenic activity. Maps sources: Google Maps, OpenStreetMap, and Conservation Ontario (https://camaps.maps.arcgis.com/home/index.html); (D) Partial least squares discriminant analysis (PLS-DA) for stream site classification using land use, hydrological features, and water physiochemical properties. The detailed descriptions of the water physicochemical properties can be found in Table 2

Sample site descriptions. Land uses defined for 200 m radius around each sample site

SITE IDLand use typeUpstream contributing area(km2)Strahler stream orderWater(% land)Urban/Developed(% land)Forest(% land)Wetland(% land)Agriculture(% land)Other(% land)
S5Mixed81407.7971.461.9316.532.29
S6Mixed176508.4231.14060.440
S10Mixed67.5423.7710.342.913.6357.332.01
S18Agriculture< 5200.630099.370
S19Agriculture< 5203.70096.30
S20Agriculture< 5200001000
S21Agriculture< 52013.330086.670
S253Agriculture51407.570092.430
S24Forest< 5106.3193.69000
Sampling sites are located on surface water catchments ranging from < 5 to ∼180 km2 (Table 1). S18, S19, S20 and S21 are located on agricultural drainage ditches (Strahler stream orders ≤2) fed almost exclusively by agricultural sub-surface tile drainage [33 (link)]. S253 (denoted as Agri_253) is located on a larger stream (Strahler stream order 4) fed primarily by agriculturally impacted waters. S5, S6, and S10 are intermediate tributaries (Strahler stream order 4–5) that feed the main river directly and are influenced by a mix of agricultural and urban activities. S24 is located on a small stream (Strahler stream order 1). It drains a forested-wetland area not impacted by any known anthropogenic land use activity. S24 serves as a proxy reference site in the context of anthropogenic land use activity in the study [12 (link)]. Only one reference site was included in our study due to accessibility and availability in a region where most watersheds are impacted in some way by anthropogenic activity. We sampled all sites that were logistically possible within the same day in order to process the samples within 24 hours.
Land use upstream of each site was characterized using the methods described by Wilkes et al. [11 (link)] and was classified coarsely into agricultural land, urban/developed land, treed land, wetlands, water, or other (Table 1). Land use data were obtained in the form of raster layers from Agriculture and Agri-Food Canada (AAFC)‘s Annual Crop Inventory [34 ]. Surface water catchment areas (entire catchment upstream of sample site, and catchment area associated with maximum stream length of 5 km upstream of a sample site) and flow direction were determined within ArcMap 9.2 (Environmental Systems Research Institute, Redlands, CA). Stream order was determined using methods described by Lyautey et al. [35 (link)]. Daily air temperature and rainfall were measured from a Hobo weather station (Onset Computer Corporation, Bourne, MA) near S20. Cumulative rainfall was calculated for the day of sampling and 1, 2, 3, 5, 7, and 10 days prior to sampling.
Shi Y., Khan I.U., Radford D., Guo G., Sunohara M., Craiovan E., Lapen D.R., Pham P, & Chen W. (2023). Core and conditionally rare taxa as indicators of agricultural drainage ditch and stream health and function. BMC Microbiology, 23, 62.
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Publication 2023
Agricultural Crops Alfalfa Anthropogenic Activities Drainage Food Livestock M-200 Maize Microtubule-Associated Proteins Poaceae Radius Rivers Soybeans Urban Development Wetlands
3
Comparative Urban Green Spaces Analysis
Xuzhou is a medium-sized city in the northwest of Jiangsu province, eastern China, with a typical warm monsoon climate which determines that the local natural vegetation is deciduous broad-leaved plants. This city has experienced rapid expansion of urban green spaces in the past three decades. By 2018, the urban green spaces in Xuzhou include 74 parks with various types such as urban park, forest park, wetland, botanical garden, etc. The per-capita urban green space is 17 m2 (Zhao and Huang 2021 (link)).
Hong Kong is a big dense city in southern China. More than 80% of its total area is hilly terrain which is unfavourable for urban development, thus, Hong Kong is one of the most densely populated cities in the world (Wan and Shen 2015 (link)). Although the government has recognized the positive aspects of urban green spaces for sustainable development and resident’s health, and put great efforts to provide green spaces, open spaces accounts for only 9% of the developed land area of the city, and, compared with cities of similar size, Hong Kong’s proportional provision of urban green spaces is among the lowest in the world (Tan et al. 2013 (link)). The local natural vegetation in Hong Kong is tropical or subtropical evergreen broad-leaved plants under the subtropical monsoon climate. Obviously, Xuzhou and Hong Kong have significant differences in terms of urban green spaces, which is conducive to the comparison in VAQ between the two cities and VAF between local and non-local landscapes.
Xu W, & Zhao J. (2023). Investigating Visual Aesthetic Fatigue in Urban Green Spaces. International Journal of Environmental Research, 17(2), 27.
Publication 2023
Climate Forests Green S Head Plants Sustainable Development Urban Development Wetlands
4
Comprehensive Urban Health Assessment Framework
In principle, our model is intended to encompass as comprehensive a range of characteristics of the urban environment as possible, thereby ensuring that consideration of any associated impacts on health in decisions relating to urban development are as complete as possible. In order to achieve this the extent was scoped in the first instance by adopting the categories defined by the Health Map, (24 (link)), a classification of the health determinants associated with the planning of human settlements published by the Royal Society for the Promotion of Public Health, and offering a comprehensive coverage of socio-environmental issues relating to urban planning and design. This classification was validated against similar classifications assembled in five other checklists including: Public Health England's Topics (25 ), Vancouver Healthy Toolkit (26 ), BREEAM Communities (27 ), HUDU Rapid HIA (28 ) and Egan Review (29 ) (see Supplementary material).
We then grouped the 23 aspects of the urban environment in the Health Map into five main “typologies” of urban form (or areas of search): natural environment, buildings, neighborhood design, transport and food; climate change was categorized as a “multiplier” of each element of urban form (Figure 1). Six areas from the Health Map were excluded as they are not explicitly related to elements of the urban form: living, wealth creation, resilient markets, social capital, social networks, work-life balance (as shown in gray in Figure 1).
Eaton E., Hunt A, & Black D. (2023). Developing and testing an environmental economics approach to the valuation and application of urban health externalities. Frontiers in Public Health, 11, 1070200.
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Publication 2023
Climate Change Food Health Promotion Homo sapiens Urban Development
5
Health Impact Appraisal for Urban Systems
In this paper we develop an economic tool that enables stakeholders to quantify the impact on population health of a specific intervention or policy relating to the environment in urban environments.
The tool is known as the Health Appraisal for Urban Systems tool (or HAUS for short). HAUS has three key features:
It is therefore intended for use as a tool to support scenario appraisal and to inform broader conversations around prioritization in health in urban development. The HAUS tool covers non-communicable disease in all populations in the UK, including older adults and children–categories not disaggregated within existing tools.
It is capable of estimating effects at the neighborhood scale, and can be extended to take into account different population sizes impacted but is not designed to be used to estimate effects on an individual or a single family group.
Eaton E., Hunt A, & Black D. (2023). Developing and testing an environmental economics approach to the valuation and application of urban health externalities. Frontiers in Public Health, 11, 1070200.
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Publication 2023
Aged Child Noncommunicable Diseases Population Group Population Health Urban Development

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More about "Urban Development"

Urban Planning, City Design, Municipal Development, Urban Growth, Spatial Planning, Built Environment, Urban Infrastructure, Housing Policy, Transportation Systems, Land Use Management, Public Services, Sustainability, Livability, Equity, Population Dynamics, Resource Optimization, Climate Resilience, Stata Software, Stata/MP, LI-7500.
Urban development is a multifaceted process that encompasses the planning, design, and management of cities and their surrounding areas.
This field addresses the social, economic, environmental, and spatial aspects of urban growth and transformation, with the aim of creating livable, sustainable, and equitable communities.
Urban development encompasses a wide range of topics such as infrastructure, housing, transportation, land use, and public services.
Researchers and professionals in this field utilize interdisciplinary approaches, including the use of analytical tools like STATA software 13 and Stata/MP 12.1, as well as advanced sensors like the LI-7500, to tackle complex urban challenges and improve the quality of life for urban residents.
Effective urban development strategies are crucial for addressing issues like population growth, resource scarcity, and climate change in the built environment.
By leveraging innovative technologies and interdisciplinary collaboration, urban development professionals can create vibrant, resilient, and inclusive cities that meet the needs of their communities.