The Historical Urban Ecological (HUE) data project was created for exploring and analyzing the urban health environments of seven major United States cities - Baltimore, Boston, Brooklyn, Chicago, Cincinnati, Manhattan, and Philidelphia - from 1830 through 1930. The data for each city includes ward boundary changes, street networks, and ward-level data on disease, mortality, crime, and other variables reported by municipal departments. The HUE data set was produced for the "Early Indicators of Later Work Levels, Disease and Death" project, funded by the National Institute of Aging. This collection represents the GIS data for each of the seven American cities, and in addition to ward boundary changes and street networks, includes in-street sewer and water sanitation systems coverage. All cities except Cincinnati include sanitation infrastructure data, and for Baltimore only water infrastructure is available. The city of Chicago includes supplemental GIS layers which reflect a reconstruction of two of Homer Hoyt's maps of average land value (1933 dollars) in the City of Chicago for 1873 and 1892. The square mile areas defined by Hoyt using Chicago's system of mile streets have been fit to the HUE street centerlines for Chicago. The Excel data tables include information about deaths in each ward broken down by cause of death, age, race, gender, as well as information about live births and deliveries.

In 2023, the metropolitan area of New York-Newark-Jersey City had the biggest population in the United States. Based on annual estimates from the census, the metropolitan area had around 19.5 million inhabitants, which was a slight decrease from the previous year. The Los Angeles and Chicago metro areas rounded out the top three. What is a metropolitan statistical area? In general, a metropolitan statistical area (MSA) is a core urbanized area with a population of at least 50,000 inhabitants – the smallest MSA is Carson City, with an estimated population of nearly 56,000. The urban area is made bigger by adjacent communities that are socially and economically linked to the center. MSAs are particularly helpful in tracking demographic change over time in large communities and allow officials to see where the largest pockets of inhabitants are in the country. How many MSAs are in the United States? There were 421 metropolitan statistical areas across the U.S. as of July 2021. The largest city in each MSA is designated the principal city and will be the first name in the title. An additional two cities can be added to the title, and these will be listed in population order based on the most recent census. So, in the example of New York-Newark-Jersey City, New York has the highest population, while Jersey City has the lowest. The U.S. Census Bureau conducts an official population count every ten years, and the new count is expected to be announced by the end of 2030.

Recent advances in quantitative tools for examining urban morphology enable the development of morphometrics that can characterize the size, shape, and placement of buildings; the relationships between them; and their association with broader patterns of development. Although these methods have the potential to provide substantial insight into the ways in which neighborhood morphology shapes the socioeconomic and demographic characteristics of neighborhoods and communities, this question is largely unexplored. Using building footprints in five of the ten largest U.S. metropolitan areas (Atlanta, Boston, Chicago, Houston, and Los Angeles) and the open-source R package, foot, we examine how neighborhood morphology differs across U.S. metropolitan areas and across the urban-exurban landscape. Principal components analysis, unsupervised classification (K-means), and Ordinary Least Squares regression analysis are used to develop a morphological typology of neighborhoods and to examine its association with the spatial, socioeconomic, and demographic characteristics of census tracts. Our findings illustrate substantial variation in the morphology of neighborhoods, both across the five metropolitan areas as well as between central cities, suburbs, and the urban fringe within each metropolitan area. We identify five different types of neighborhoods indicative of different stages of development and distributed unevenly across the urban landscape: these include low-density neighborhoods on the urban fringe; mixed use and high-density residential areas in central cities; and uniform residential neighborhoods in suburban cities. Results from regression analysis illustrate that the prevalence of each of these forms is closely associated with variation in socioeconomic and demographic characteristics such as population density, the prevalence of multifamily housing, and income, race/ethnicity, homeownership, and commuting by car. We conclude by discussing the implications of our findings and suggesting avenues for future research on neighborhood morphology, including ways that it might provide insight into issues such as zoning and land use, housing policy, and residential segregation.

This zipped folder contains supplementary materials for Justin Bologna's 2025 Thesis, Breaking Down the Monolith: Using Network Methods to Identify and Analyze Chicago’s Largest Landlords.

In 1969, Clifford Shaw and Henry D. McKay released the second edition of Juvenile delinquency and urban areas. Their book conducts statistical analysis, without the benefit of computers, on the impact of various socio-economic issues on juvenile delinquency in Chicago, Philadelphia, Boston, Cleveland, Cincinnati, and Richmond. Chicago is the major focus of the research with analysis exploring three time periods within the city 1900 to 1906, 1917 to 1923, and 1927 to 1933. The suburbs are also analyzed from 1934 to 1966. The book contains a number of maps with the rates of delinquency and socio-economic factors divided into “square mile areas”. These are explained by Shaw and McKay (see page 29 footnote 5) as “the basic units the city of Chicago was divided for the presentation of rates of delinquency and other data”. The rate data from 1927 to 1938 is divided into140 ‘square mile areas’ (which in many cases are more than a square mile in area).

Residential yards are a significant component of urban socio-ecological systems; residential land covers 11% of the United States and is often the dominant land use within urban areas. Residential yards also play an important role in the sustainability of urban socio-ecological systems, affecting biogeochemical cycles, water, and the climate via individual- and household-level behaviors. Vegetation, such as trees and grasses, are unevenly distributed across front and back yards, and we sought to understand how similar yards are to each other when compared to their neighboring yards and neighborhoods using aerial imagery. There are many ways to measure yard similarity, and we compared several measures to account for different definitions of ‘neighborness’. We examined the spatial autocorrelation of several yard vegetation characteristics in both front and backyards in Boston, MA, USA. Our study area included 1,027 Census block groups (sub-neighborhood areas) and 175,576 parcels with matched front-backyard pairings (n = 351,152 yards in total) across Boston’s metropolitan area. This data package contains 1) 351,152 yard spatially-referenced yard polygons with five measures of vegetation summarized, 2) the containing block groups, and 3) and *.R script that replicates the analyses reported in Locke, D. H., Ossola, A., Minor, E., & Lin, B. B. (2021). Spatial contagion structures urban vegetation from parcel to landscape. People and Nature, 00, 1–15. https://doi.org/10.1002/pan3.10254

Methods

1. Study Area This study focused on the Boston, MA, metropolitan region (42°21′29″N 71°03′49″W), an area of approximately 703 km2. The region has a humid continental climate (mean annual temperature = 9.6 °C; mean annual precipitation = 1233 mm) (PRISM Climate Group 2015) and was historically covered with mesic forests. Forty-four percent of the land area is residential (Ossola et al., 2019a), which is consistent with other urban areas in western countries such as Baltimore, MD (Avolio et al., 2020), Chicago, IL (Lewis et al., 2019), Adelaide, (Australia)(Ossola et al., 2021), Edinburgh (Scotland), Belfast (Northern Ireland), Cardiff (Wales), and Leicester and Oxford (England) (Loram et al., 2007), and represents more than twice as much land area as parks and open spaces (18.43%) (Ossola et al., 2019b). Backyards compose 14% of all urban land area and contain ~21% of all tree canopy cover; front yards cover ~8% of the area and have ~8% of the study area’s tree canopy cover (Ossola et al., 2019b).

2. Open Data Classified LiDAR point cloud data (year 2014) were obtained from the US Geological Survey (“MA Post-Sandy CMPG 2013–14”, NPS = 0.7 m, vertical and horizontal accuracy = 0.05 m and 0.35 m, respectively). High-resolution RBG-NIR imagery (1 m ground resolution, year 2014) were obtained from the National Agriculture Imagery Program (NAIP, USDA). Residential parcel polygons, building footprints, and road centerline data were downloaded from the open data portals of the Commonwealth of Massachusetts (2017) and the City of Boston (2017).

3. Geospatial analyses All front, corner, and backyards contained in all residential parcels with a house were located and classified in ArcGIS Desktop 10.5 (ESRI, Redlands, CA) by using the workflow described in Ossola and others (2019a, 2019b). Briefly, each house centroid was identified to fit an offset line perpendicular to the closest street centerline. Front and backyards were then located by splitting each parcel polygon with a dividing segment, perpendicular to the offset line, passing through the house centroid, and extending to the parcel’s border. Yards were classified by attributing the front yard as the closest unit to the respective road centerline. Corner yards, which lack clear front/back sides, were assigned to all parcels located within 15 m from street intersections and were excluded from analyses. The workflow used to locate and classify yards exceeded 98% accuracy (Ossola et al., 2019a). Vegetation maps detailing tree height, canopy volume, and tree and grass covers were modelled and validated for their accuracy based on the LiDAR and RBG-NIR imagery as detailed in previous papers (Ossola et al., 2019a, 2019b). Briefly, tree canopy height was extracted from a canopy height model (1.5 m ground resolution) interpolated from the LiDAR data in ArcGIS Desktop 10.5 (ESRI, Redlands, CA). Tree and grass covers were modelled at 1.5 m resolution by using maximum likelihood supervised classification of ~100,000 pixels manually attributed to one of three land cover classes (i.e., tree, grass and non-vegetated cover), and based on the tree canopy height map and the RGB-NIR imagery (Singh et al., 2012). The average vertical accuracy of the tree height data, as recorded by the LiDAR point cloud, is 5.3 cm. The accuracy of the grass and tree canopy cover classification is 91.7% and 98.9%, respectively (Ossola & Hopton, 2018a). Canopy volume was calculated as the product of tree canopy cover and height within each pixel, assuming this volume to be completely occupied by vegetation (Ossola & Hopton, 2018a, 2018b), which overestimates total volume. Because these remotely sensed data view the earth from above, and tree canopy overhangs turf, the turf estimates are plausibly underestimates (Akbari et al., 2003).

References

Akbari, H., Rose, L. S., & Taha, H. (2003). Analyzing the land cover of an urban environment using high-resolution orthophotos. Landscape and Urban Planning, 63(1), 1–14. https://doi.org/10.1016/S0169-2046(02)00165-2 Avolio, M. L., Blanchette, A., Sonti, N. F., & Locke, D. H. (2020). Time Is Not Money: Income Is More Important Than Lifestage for Explaining Patterns of Residential Yard Plant Community Structure and Diversity in Baltimore. Frontiers in Ecology and Evolution, 8(April), 1–14. https://doi.org/10.3389/fevo.2020.00085 Lewis, A. D., Bouman, M. J., Winter, A. M., Hasle, E. A., Stotz, D. F., Johnston, M. K., Klinger, K. R., Rosenthal, A., & Czarnecki, C. A. (2019). Does nature need cities? Pollinators reveal a role for cities in wildlife conservation. Frontiers in Ecology and Evolution, 7(JUN), 1–8. https://doi.org/10.3389/fevo.2019.00220 Loram, A., Tratalos, J., Warren, P. H., & Gaston, K. J. (2007). Urban domestic gardens (X): The extent & structure of the resource in five major cities. Landscape Ecology, 22(4), 601–615. https://doi.org/10.1007/s10980-006-9051-9 Ossola, A., & Hopton, M. E. (2018a). Climate differentiates forest structure across a residential macrosystem. Science of the Total Environment, 639, 1164–1174. https://doi.org/10.1016/j.scitotenv.2018.05.237 Ossola, A., & Hopton, M. E. (2018b). Measuring urban tree loss dynamics across residential landscapes. Science of The Total Environment, 612, 940–949. https://doi.org/10.1016/j.scitotenv.2017.08.103 Ossola, A., Jenerette, G. D., McGrath, A., Chow, W., Hughes, L., & Leishman, M. R. (2021). Small vegetated patches greatly reduce urban surface temperature during a summer heatwave in Adelaide, Australia. Landscape and Urban Planning, 209. https://doi.org/10.1016/j.landurbplan.2021.104046 Ossola, A., Locke, D. H., Lin, B., & Minor, E. (2019a). Greening in style: Urban form, architecture and the structure of front and backyard vegetation. Landscape and Urban Planning, 185(November 2018), 141–157. https://doi.org/10.1016/j.landurbplan.2019.02.014 Ossola, A., Locke, D. H., Lin, B., & Minor, E. S. (2019b). Yards increase forest connectivity in urban landscapes. Landscape Ecology, 7(12). https://doi.org/10.1007/s10980-019-00923-7 Singh, K. K., Vogler, J. B., Shoemaker, D. A., & Meentemeyer, R. K. (2012). LiDAR-Landsat data fusion for large-area assessment of urban land cover: Balancing spatial resolution, data volume and mapping accuracy. ISPRS Journal of Photogrammetry and Remote Sensing, 74(November), 110–121. https://doi.org/10.1016/j.isprsjprs.2012.09.009

This dataset contains all current and active business licenses issued by the Department of Business Affairs and Consumer Protection. This dataset contains a large number of records /rows of data and may not be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Notepad or Wordpad, to view and search.

Data fields requiring description are detailed below.

APPLICATION TYPE: 'ISSUE' is the record associated with the initial license application. 'RENEW' is a subsequent renewal record. All renewal records are created with a term start date and term expiration date. 'C_LOC' is a change of location record. It means the business moved. 'C_CAPA' is a change of capacity record. Only a few license types my file this type of application. 'C_EXPA' only applies to businesses that have liquor licenses. It means the business location expanded.

LICENSE STATUS: 'AAI' means the license was issued.

Business license owners may be accessed at: http://data.cityofchicago.org/Community-Economic-Development/Business-Owners/ezma-pppn To identify the owner of a business, you will need the account number or legal name.

Data Owner: Business Affairs and Consumer Protection

Time Period: Current

Frequency: Data is updated daily

This dataset was collected at the UIC Plant Research Laboratory in Chicago, Illinois, as part of the Community Research on Climate and Urban Science (CROCUS) Urban Integrated Field Laboratory (UIFL) project, led by Argonne National Laboratory. The site provides continuous atmospheric flux measurements, focusing on CO₂, H₂O, and heat and momentum transport in an urban setting. The data is processed at 30 minutes interval using the Eddy Covariance method and includes quality control and diagnostic data generated by EddyPro software. The data is stored in the netCDF files following CF conventions. The UIC Plant Research Laboratory is located near major highways and urban infrastructure, including buildings and parking areas. The surrounding landscape consists of a mix of turf, plants, trees, and impervious surfaces such as concrete and asphalt, making it ideal for studying urban at for studies on urban sustainability, air quality, and the effects of urbanization on atmospheric processes on urban climate dynamics, air quality, and surface-atmosphere exchanges within the city of Chicago. This dataset is funded by the U.S. Department of Energy’s Office of Science, Biological and Environmental Research (BER) program.

City Digital Twin Market Outlook

According to our latest research, the Global City Digital Twin market size was valued at $8.2 billion in 2024 and is projected to reach $41.7 billion by 2033, expanding at a remarkable CAGR of 20.1% during the forecast period of 2025–2033. The accelerating adoption of smart city initiatives, driven by urbanization and the need for sustainable infrastructure management, stands out as a major factor fueling the robust growth of the City Digital Twin market globally. As cities worldwide face mounting pressures to optimize resources, reduce operational costs, and enhance citizen experiences, the deployment of digital twin technologies has become integral to urban transformation strategies. This surge is further supported by advancements in IoT, cloud computing, and AI, which collectively empower city planners, governments, and enterprises to visualize, simulate, and manage urban environments with unprecedented accuracy and efficiency.

Regional Outlook

North America currently commands the largest share of the City Digital Twin market, accounting for approximately 38% of global revenue in 2024. This dominance is attributed to the region’s mature technological infrastructure, early adoption of smart city frameworks, and significant investments from both public and private sectors. The United States, in particular, has established itself as a pioneer, with major metropolitan areas such as New York, Los Angeles, and Chicago leveraging digital twin platforms for urban planning, infrastructure monitoring, and emergency response. Supportive government policies, robust funding for R&D, and strong collaboration between technology providers and municipal authorities have further cemented North America’s leadership in this market. The presence of leading software and hardware vendors, coupled with a culture of innovation, ensures that the region continues to set benchmarks for digital twin implementation in urban environments.

Asia Pacific is emerging as the fastest-growing region in the City Digital Twin market, projected to expand at an impressive CAGR of 23.7% between 2025 and 2033. This rapid growth is propelled by ambitious smart city projects in countries such as China, India, Singapore, and South Korea, where urbanization rates are soaring and governments are prioritizing digital transformation to address infrastructure bottlenecks and environmental challenges. Massive investments in IoT networks, 5G deployment, and cloud-based urban management systems are accelerating the adoption of city digital twins across the region. Additionally, the proliferation of tech startups, increasing foreign direct investment (FDI), and favorable regulatory frameworks are fostering a competitive ecosystem that encourages innovation and scalability. The Asia Pacific region’s focus on sustainable development and energy optimization is also driving demand for advanced digital twin solutions in sectors like utilities, transportation, and real estate.

In contrast, emerging economies in Latin America, the Middle East, and Africa are witnessing a gradual but steady uptake of City Digital Twin technologies. While these regions collectively account for a smaller share of the global market, they are characterized by unique adoption challenges, including budget constraints, limited digital infrastructure, and a shortage of skilled professionals. Nonetheless, increasing urban population densities, government-led smart city initiatives, and international collaborations are paving the way for future growth. Localized demand for improved energy efficiency, water management, and disaster preparedness is driving pilot projects and partnerships with global technology providers. Policy reforms aimed at fostering digital innovation and public-private partnerships are expected to gradually overcome barriers, unlocking new opportunities for city digital twin adoption in these emerging markets.

Report Scope

Recent advances in quantitative tools for examining urban morphology enable the development of morphometrics that can characterize the size, shape, and placement of buildings; the relationships between them; and their association with broader patterns of development. Although these methods have the potential to provide substantial insight into the ways in which neighborhood morphology shapes the socioeconomic and demographic characteristics of neighborhoods and communities, this question is largely unexplored. Using building footprints in five of the ten largest U.S. metropolitan areas (Atlanta, Boston, Chicago, Houston, and Los Angeles) and the open-source R package, foot, we examine how neighborhood morphology differs across U.S. metropolitan areas and across the urban-exurban landscape. Principal components analysis, unsupervised classification (K-means), and Ordinary Least Squares regression analysis are used to develop a morphological typology of neighborhoods and to examine its association with the spatial, socioeconomic, and demographic characteristics of census tracts. Our findings illustrate substantial variation in the morphology of neighborhoods, both across the five metropolitan areas as well as between central cities, suburbs, and the urban fringe within each metropolitan area. We identify five different types of neighborhoods indicative of different stages of development and distributed unevenly across the urban landscape: these include low-density neighborhoods on the urban fringe; mixed use and high-density residential areas in central cities; and uniform residential neighborhoods in suburban cities. Results from regression analysis illustrate that the prevalence of each of these forms is closely associated with variation in socioeconomic and demographic characteristics such as population density, the prevalence of multifamily housing, and income, race/ethnicity, homeownership, and commuting by car. We conclude by discussing the implications of our findings and suggesting avenues for future research on neighborhood morphology, including ways that it might provide insight into issues such as zoning and land use, housing policy, and residential segregation.

Parking Prebook and Routing Platforms Market Outlook

According to our latest research, the Global Parking Prebook and Routing Platforms market size was valued at $2.3 billion in 2024 and is projected to reach $8.7 billion by 2033, expanding at a CAGR of 15.7% during 2024–2033. This robust growth trajectory is primarily driven by the increasing adoption of smart city initiatives and the rising need for efficient urban mobility solutions worldwide. As urbanization accelerates, congestion and parking challenges have intensified, prompting municipalities, property owners, and private operators to invest in advanced parking management technologies. The integration of IoT, AI, and mobile applications in parking prebook and routing platforms is transforming the way drivers locate, reserve, and pay for parking spaces, significantly enhancing user convenience and operational efficiency. This market's expansion is further fueled by the growing demand for contactless and automated solutions, especially in the wake of the global pandemic, which has heightened awareness around seamless, low-touch urban experiences.

Regional Outlook

North America currently holds the largest share of the global Parking Prebook and Routing Platforms market, accounting for approximately 38% of the total market value in 2024. This dominance is attributed to the region’s mature technology infrastructure, high penetration of smartphones, and proactive smart city policies implemented across major urban centers such as New York, Los Angeles, Toronto, and Chicago. The presence of leading platform providers and a strong culture of early technology adoption further contribute to North America’s market leadership. Additionally, regulatory frameworks supporting digital payment systems, data privacy, and urban mobility innovation have created a favorable environment for the rapid deployment of parking prebook and routing solutions. The region’s commercial and residential real estate sectors are also increasingly leveraging these platforms to optimize parking asset utilization and enhance tenant satisfaction.

The Asia Pacific region is expected to register the fastest growth in the Parking Prebook and Routing Platforms market, projected to expand at a CAGR of 18.2% from 2024 to 2033. This remarkable growth is driven by rapid urbanization, burgeoning middle-class populations, and escalating vehicle ownership rates in countries such as China, India, Japan, and South Korea. Governments across the region are investing heavily in smart city infrastructure, with parking management emerging as a key focus area to address urban congestion and pollution. The proliferation of affordable smartphones and increased internet penetration have also accelerated the adoption of digital parking solutions. Furthermore, strategic partnerships between local governments, technology firms, and mobility service providers are fostering innovation and expanding the reach of parking prebook and routing platforms in both metropolitan and secondary cities.

Emerging economies in Latin America and the Middle East & Africa present unique opportunities and challenges for the Parking Prebook and Routing Platforms market. While infrastructure development and digital transformation initiatives are gaining momentum, fragmented urban planning, limited technology access, and regulatory uncertainties can impede widespread adoption. In these markets, localized demand is often shaped by specific urban mobility issues, such as high-density city centers, informal parking operations, and evolving consumer preferences. Policy reforms aimed at improving urban transport and reducing emissions are gradually encouraging investment in digital parking platforms. However, overcoming barriers related to affordability, digital literacy, and interoperability with existing systems remains critical for unlocking the full potential of these regions.

Report Scope

In 2023, the GDP of the Chicago-Naperville-Elgin metropolitan area amounted to ****** billion chained 2017 U.S. dollars. The GDP of the United States since 1990 can be accessed here. Economic growth and unemployment in Chicago Economic growth in Chicago, measured by the growth in Gross Domestic Product (GDP), was significant in the years between 2001 and 2022. This growth occurred in a period of growth for cities nationally as seen by growth of other major American cities such as Los Angeles and San Francisco. In contrast to Chicago’s growth, San Francisco’s growth rate demonstrated the effect of a new and booming industry. The influence of technology and internet companies saw San Francisco grow nearly ** percent in comparison to the ** percent growth in GDP achieved by Chicago. As a result, Chicago-Naperville-Elgin ranked third in Gross Metropolitan Product of the United States, by metropolitan area in 2022. The drop in GDP output in 2020 can be attributed to the COVID-19 pandemic.

Abstract (en): This survey was undertaken to assemble a broad range of family, household, employment, schooling, and welfare data on families living in urban poverty areas of Chicago. The researchers were seeking to test a variety of theories about urban poverty. Questions concerned respondents' current lives as well as their recall of life events from birth to age 21. Major areas of investigation included household composition, family background, education, time spent in detention or jail, childbirth, fertility, relationship history, current employment, employment history, military service, participation in informal economy, child care, child support, child-rearing, neighborhood and housing characteristics, social networks, current health, current and past public aid use, current income, and major life events. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.. Non-Hispanic whites, non-Hispanic Blacks, and persons of Mexican or Puerto Rican ethnicity, aged 18-44, residing in 1986 in Chicago census tracts with 20 percent or more persons living under the poverty line. Multistage stratified probability sample design yielding 2,490 observations (1,183 Blacks, 364 whites, 489 Mexican-origin persons, and 454 Puerto Rican-origin persons). Though Black respondents include parents (N = 1,020) and non-parents (N = 163), only parents were selected within non-Black groups. Response rates ranged from 73.8 percent for non-Hispanic whites to 82.5 percent for Black parents. 1997-11-04 The documentation and frequencies are being released as PDF files, and an SPSS export file is now available. Also, the SAS data definition statements and SPSS data definition statements have been reissued with minor changes, and SPSS value labels are being released in Part 7 due to SPSS for Windows limitations. Funding insitution(s): Carnegie Corporation. Chicago Community Trust. Ford Foundation. Institute for Research on Poverty. Joyce Foundation. Lloyd A. Fry Foundation. John D. and Catherine T. MacArthur Foundation. Rockefeller Foundation. Spencer Foundation. United States Department of Health and Human Services. William T. Grant Foundation. Woods Charitable Fund. Value labels for this study are being released in a separate file, Part 7, to assist users of SPSS Release 6.1 for Windows. The syntax window in this version of SPSS will read a maximum of 32,767 lines. If all value labels were included in the SPSS data definition file, the number of lines in the file would exceed 32,767 lines.All references to card-image data in the codebook are no longer applicable.During generation of the logical record length data file, ICPSR optimized variable widths to the width of the widest value appearing in the data collection for each variable. However, the principal investigator's user-missing data code definitions were retained even when a variable contained no missing data. As a result, when user-missing data values are defined (e.g., by uncommenting the MISSING VALUES section in the SPSS data definition statements) and exceed the optimized variable width, SPSS's display dictionary output will contain asterisks for the missing data codes.Producer: University of Chicago, Center for the Study of urban Inequality, and the National Opinion Research Center (NORC).

The Project on Human Development in Chicago Neighborhoods (PHDCN) is a large-scale, interdisciplinary study of how families, schools, and neighborhoods affect child and adolescent development. It was designed to advance the understanding of the developmental pathways of both positive and negative human social behaviors. In particular, the project examined the causes and pathways of juvenile delinquency, adult crime, substance abuse, and violence. At the same time, the project also provided a detailed look at the environments in which these social behaviors take place by collecting substantial amounts of data about urban Chicago, including its people, institutions, and resources. PHDCN was directed from the Harvard School of Public Health, and funded by the John D. and Catherine T. MacArthur Foundation, the National Institute of Justice, the National Institute of Mental Health, the U.S. Department of Education, and the Administration for Children, Youth and Families. The project design consisted of two major components. The first was an intensive study of Chicago's neighborhoods, particularly the social, economic, organizational, political, and cultural structures and the dynamic changes that take place in the structures over time. The second component was a series of coordinated longitudinal studies that followed over 6,000 randomly selected children, adolescents, and young adults over time to examine the changing circumstances of their lives, as well as the personal characteristics, that might lead them toward or away from a variety of antisocial behaviors. For more information about the PHDCN series, please visit NACJD's PHDCN Resource Guide.

Recent advances in quantitative tools for examining urban morphology enable the development of morphometrics that can characterize the size, shape, and placement of buildings; the relationships between them; and their association with broader patterns of development. Although these methods have the potential to provide substantial insight into the ways in which neighborhood morphology shapes the socioeconomic and demographic characteristics of neighborhoods and communities, this question is largely unexplored. Using building footprints in five of the ten largest U.S. metropolitan areas (Atlanta, Boston, Chicago, Houston, and Los Angeles) and the open-source R package, foot, we examine how neighborhood morphology differs across U.S. metropolitan areas and across the urban-exurban landscape. Principal components analysis, unsupervised classification (K-means), and Ordinary Least Squares regression analysis are used to develop a morphological typology of neighborhoods and to examine its association with the spatial, socioeconomic, and demographic characteristics of census tracts. Our findings illustrate substantial variation in the morphology of neighborhoods, both across the five metropolitan areas as well as between central cities, suburbs, and the urban fringe within each metropolitan area. We identify five different types of neighborhoods indicative of different stages of development and distributed unevenly across the urban landscape: these include low-density neighborhoods on the urban fringe; mixed use and high-density residential areas in central cities; and uniform residential neighborhoods in suburban cities. Results from regression analysis illustrate that the prevalence of each of these forms is closely associated with variation in socioeconomic and demographic characteristics such as population density, the prevalence of multifamily housing, and income, race/ethnicity, homeownership, and commuting by car. We conclude by discussing the implications of our findings and suggesting avenues for future research on neighborhood morphology, including ways that it might provide insight into issues such as zoning and land use, housing policy, and residential segregation.

A dataset listing Illinois cities by population for 2024.

Urban Mobility Simulation with AI Market Outlook

According to our latest research, the Global Urban Mobility Simulation with AI market size was valued at $1.4 billion in 2024 and is projected to reach $6.9 billion by 2033, expanding at a remarkable CAGR of 19.7% during 2024–2033. The major factor propelling the growth of this market globally is the increasing need for intelligent and data-driven urban mobility solutions to address congestion, sustainability, and efficiency challenges in rapidly urbanizing environments. As cities worldwide grapple with complex transportation networks and the rise of multi-modal transit options, AI-powered simulation technologies are becoming indispensable for urban planners, government agencies, and private mobility providers seeking to optimize infrastructure investments, streamline traffic management, and facilitate the safe integration of autonomous vehicles.

Regional Outlook

North America currently commands the largest share of the Urban Mobility Simulation with AI market, accounting for over 38% of the global revenue in 2024. This dominance is primarily attributed to the region’s mature technological ecosystem, early adoption of smart city initiatives, and robust investments in AI research and development. The United States, in particular, has witnessed significant deployments of AI-driven simulation platforms across metropolitan areas such as New York, Los Angeles, and Chicago, where transportation authorities leverage these tools for dynamic traffic management, urban planning, and autonomous vehicle testing. Favorable government policies, substantial funding for infrastructure modernization, and the presence of leading technology vendors further bolster North America’s leadership in this sector. As a result, the region is expected to maintain its stronghold, with steady growth projected through 2033.

In contrast, the Asia Pacific region is anticipated to exhibit the fastest growth in the Urban Mobility Simulation with AI market, registering an impressive CAGR of 23.5% from 2024 to 2033. Key drivers include rapid urbanization, burgeoning smart city projects, and increasing investments by both public and private sectors in countries such as China, India, Japan, and South Korea. The region’s vast and complex urban landscapes, coupled with mounting congestion and environmental concerns, are compelling authorities to adopt advanced AI-powered simulation solutions for optimizing public transport, reducing emissions, and enhancing commuter safety. Additionally, government-backed initiatives and partnerships with global technology firms are accelerating the deployment of innovative simulation platforms, positioning Asia Pacific as a critical growth engine for the global market.

Meanwhile, emerging economies in Latin America and Middle East & Africa are beginning to recognize the transformative potential of Urban Mobility Simulation with AI, though adoption remains at a nascent stage. Challenges such as limited digital infrastructure, budgetary constraints, and a shortage of skilled professionals have hindered widespread implementation. However, localized demand for improved urban mobility, coupled with increasing policy support and international collaboration, is gradually driving uptake in major cities like São Paulo, Dubai, and Johannesburg. These regions are expected to witness moderate yet accelerating growth as governments prioritize smart mobility solutions to address urbanization pressures and enhance quality of life.

Report Scope