Gotowe bibliografie tematyczne / Pennsylvania, census

Gotowa bibliografia na temat „Pennsylvania, census”

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Data publikacji: 27 lipca 2024
Data aktualizacji: 29 lipca 2024

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Artykuły w czasopismach na temat "Pennsylvania, census"

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Minderhout, David, i Andrea Frantz. "Native Americans in the Pennsylvania School Curriculum". Practicing Anthropology 31, nr 1 (1.01.2009): 16–20. http://dx.doi.org/10.17730/praa.31.1.v845722p5pn31007.

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In the 2000 U. S. Census, 18,348 people in Pennsylvania indicated that they were Native Americans, an increase of nearly 50% since 1990; another 34,302 identified themselves as "part-Indian." These numbers likely reflect a trend towards a greater acceptance of Native American status in the United States generally and in Pennsylvania in particular. This trend has been going on since the 1960s with the rise of the Red Power movement, and a changing American society that increasingly saw Native Americans as environmentally friendly and historically wronged. Today, in Pennsylvania, hardly a weekend goes by without a powwow or a tribal gathering somewhere in the state. In our on-going research with Pennsylvania's Native Americans since 2004, we have found them to be both proud of their identity and heritage and increasingly frustrated with the lack of recognition they receive from the state and the larger, non-Indian population. Pennsylvania is one of very few states that neither contains a reservation nor officially recognizes any Native American group. No university-level Native American cultural center or studies program exists within the state, and no state agency is dedicated to the issues and concerns of Native Americans. This is ironic because the first two hundred years of European history in Pennsylvania is one of extensive interaction, cooperation and eventually conflict with Native Americans. But, as will be seen in this paper, Native Americans have largely disappeared from the state history books.
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Geyer, Nathaniel R., i Eugene J. Lengerich. "LionVu: A Data-Driven Geographical Web-GIS Tool for Community Health and Decision-Making in a Catchment Area". Geographies 3, nr 2 (18.04.2023): 286–302. http://dx.doi.org/10.3390/geographies3020015.

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In 2018, the Penn State Cancer Institute developed LionVu, a web mapping tool to educate and inform community health professionals about the cancer burden in Pennsylvania and its catchment area of 28 counties in central Pennsylvania. LionVu, redesigned in 2023, uses several open-source JavaScript libraries (i.e., Leaflet, jQuery, Chroma, Geostats, DataTables, and ApexChart) to allow public health researchers the ability to map, download, and chart 21 publicly available datasets for clinical, educational, and epidemiological audiences. County and census tract data used in choropleth maps were all downloaded from the sources website and linked to Pennsylvania and catchment area county and census tract geographies, using a QGIS plugin and Leaflet JavaScript. Two LionVu demonstrations are presented, and 10 other public health related web-GIS applications are reviewed. LionVu fills a role in the public health community by allowing clinical, educational, and epidemiological audiences the ability to visualize and utilize health data at various levels of aggregation and geographical scales (i.e., county, or census tracts). Also, LionVu is a novel application that can translate and can be used, for mapping and graphing purposes. A dialog to demonstrate the potential value of web-based GIS to a wider audience, in the public health research community, is needed.
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Marcoux, Richard, Amadou Noumbissi i Tukufu Zuberi. "Orphans in Three Sahelian Countries: Exploratory Analyses from Census Data". Canadian Studies in Population 37, nr 1-2 (31.12.2010): 245. http://dx.doi.org/10.25336/p65g71.

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Important investments in Africa have reduced slightly the levels child mortality but life expectancy still very low. The number of children without surviving biological parents is increasing and orphans are becoming an important social problem. Because Sahelian societies are mostly patriarchal, becoming fatherless or motherless will have different effects on the well being of the child. This paper examines the levels and trends of the survival status of the parents and then, living arrangements of orphans. We describe characteristics of these children with a special focus on education and economic activities. The paper uses the censuses from Chad, Niger and Senegal made available by the African Census Analysis Project (ACAP) held at University of Pennsylvania. These countries collected information on survival status of each biological parent to estimate adult mortality but the potential of this information for research on child well-being is rarely exploited.
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Nizeyimana, Egide, G. W. Petersen, M. C. Anderson, B. M. Evans, J. M. Hamlett i G. M. Baumer. "Statewide GIS/Census Data Assessment of Nitrogen Loadings from Septic Systems in Pennsylvania". Journal of Environmental Quality 25, nr 2 (marzec 1996): 346–54. http://dx.doi.org/10.2134/jeq1996.00472425002500020020x.

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Marker, John T., i Konstadinos G. Goulias. "Truck Traffic Prediction Using Quick Response Freight Model Under Different Degrees of Geographic Resolution: Geographic Information System Application in Pennsylvania". Transportation Research Record: Journal of the Transportation Research Board 1625, nr 1 (styczeń 1998): 118–23. http://dx.doi.org/10.3141/1625-15.

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The new Quick Response Freight Manual (QRFM) was used to model truck traffic in Centre County, Pennsylvania, using Geographic Information System software. The QRFM methodology of truck traffic estimation follows the three-step process of trip generation, trip distribution, and traffic assignment. Trip generation was estimated by four classes of business employment and number of households and was aggregated to traffic analysis zones. Trip distribution employed a doubly constrained gravity model with travel time–based friction factors. User equilibrium was used for traffic assignment. Model calibration was performed by comaparing total vehicle miles traveled from model output with observed data. A comparison is made in truck traffic estimation between two models when the model resolution is changed, that is, when the size and number of traffic analysis zones (TAZs) is changed. One model uses census tracts and the other uses census blocks and block groups as TAZs. Both models use the same network, which includes all major highways and most local roads in the urbanized region of the county. Results from the two aggregation scales of analysis were compared with each other by using traffic counts in the Roadway Management System of Pennsylvania as reference data. The estimated truck traffic link volumes favor the use of the more disaggregate TAZ scheme, which is based on blocks and block groups.
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Remster, Brianna, i Rory Kramer. "SHIFTING POWER". Du Bois Review: Social Science Research on Race 15, nr 02 (2018): 417–39. http://dx.doi.org/10.1017/s1742058x18000206.

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AbstractWhile prisoners cannot vote, they are counted as residents of the often rural legislative districts where they are incarcerated rather than their home districts. We examine the extent to which incarceration shifts the balance of a representative democracy by considering its impact on legislative apportionment. Drawing on data from the Census, Pennsylvania Department of Corrections, and Pennsylvania Redistricting Commission, we develop a counterfactual framework to examine whether removing and returning prisoners to their home districts affects equal representation. Because prisoners are disproportionately African American, we also employ this counterfactual to assess racial differences in the impact of prison gerrymandering. Findings indicate that incarceration shifts political power from urban districts to suburban and rural districts through legislative apportionment. Moreover, non-White communities suffer the most. We conclude by considering how our findings fit a growing literature on the role of mass incarceration in [re]producing racial inequalities in the contemporary United States.
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Sullivan, Michele. "African American Abolitionists in Chester County: Finding New Stories". Pennsylvania History: A Journal of Mid-Atlantic Studies 90, nr 1 (2023): 48–28. http://dx.doi.org/10.5325/pennhistory.90.1.0048.

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ABSTRACT Historians have traditionally recounted the history of the Underground Railroad in Chester County and elsewhere in Pennsylvania as the work of heroic Quakers. When African Americans are included, for example, in R. C. Smedley’s History of the Underground Railroad in Chester and Neighboring Counties of Pennsylvania, they are nearly always mentioned only by their first names or have often been reduced to the role of “assistants.” Smedley overlooked the important function of free Black churches and residential communities. This article illuminates the stories of African American abolitionists in Chester County, including their part in assisting fugitives escape and the various forms of resistance in which they engaged. Utilizing original documents, letters, archival records, census data, newspapers, and the perspectives of recent historians, this article provides a frame and a context by which to understand the contributions of local Blacks to the larger story of abolition and the Underground Railroad.
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Schwartz, B. S., Jonathan Pollak, Melissa N. Poulsen, Karen Bandeen-Roche, Katherine Moon, Joseph DeWalle, Karen Siegel, Carla Mercado, Giuseppina Imperatore i Annemarie G. Hirsch. "Association of community types and features in a case–control analysis of new onset type 2 diabetes across a diverse geography in Pennsylvania". BMJ Open 11, nr 1 (styczeń 2021): e043528. http://dx.doi.org/10.1136/bmjopen-2020-043528.

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ObjectivesTo evaluate associations of community types and features with new onset type 2 diabetes in diverse communities. Understanding the location and scale of geographic disparities can lead to community-level interventions.DesignNested case–control study within the open dynamic cohort of health system patients.SettingLarge, integrated health system in 37 counties in central and northeastern Pennsylvania, USA.Participants and analysisWe used electronic health records to identify persons with new-onset type 2 diabetes from 2008 to 2016 (n=15 888). Persons with diabetes were age, sex and year matched (1:5) to persons without diabetes (n=79 435). We used generalised estimating equations to control for individual-level confounding variables, accounting for clustering of persons within communities. Communities were defined as (1) townships, boroughs and city census tracts; (2) urbanised area (large metro), urban cluster (small cities and towns) and rural; (3) combination of the first two; and (4) county. Community socioeconomic deprivation and greenness were evaluated alone and in models stratified by community types.ResultsBorough and city census tract residence (vs townships) were associated (OR (95% CI)) with higher odds of type 2 diabetes (1.10 (1.04 to 1.16) and 1.34 (1.25 to 1.44), respectively). Urbanised areas (vs rural) also had increased odds of type 2 diabetes (1.14 (1.08 to 1.21)). In the combined definition, the strongest associations (vs townships in rural areas) were city census tracts in urban clusters (1.41 (1.22 to 1.62)) and city census tracts in urbanised areas (1.33 (1.22 to 1.45)). Higher community socioeconomic deprivation and lower greenness were each associated with increased odds.ConclusionsUrban residence was associated with higher odds of type 2 diabetes than for other areas. Higher community socioeconomic deprivation in city census tracts and lower greenness in all community types were also associated with type 2 diabetes.
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Ortiz, Angel G., Daniel Wiese, Kristen A. Sorice, Minhhuyen Nguyen, Evelyn T. González, Kevin A. Henry i Shannon M. Lynch. "Liver Cancer Incidence and Area-Level Geographic Disparities in Pennsylvania—A Geo-Additive Approach". International Journal of Environmental Research and Public Health 17, nr 20 (16.10.2020): 7526. http://dx.doi.org/10.3390/ijerph17207526.

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Many neighborhood socioeconomic index measures (nSES) that capture neighborhood deprivation exist but the impact of measure selection on liver cancer (LC) geographic disparities remains unclear. We introduce a Bayesian geoadditive modeling approach to identify clusters in Pennsylvania (PA) with higher than expected LC incidence rates, adjusted for individual-level factors (age, sex, race, diagnosis year) and compared them to models with 7 different nSES index measures to elucidate the impact of nSES and measure selection on LC geospatial variation. LC cases diagnosed from 2007–2014 were obtained from the PA Cancer Registry and linked to nSES measures from U.S. census at the Census Tract (CT) level. Relative Risks (RR) were estimated for each CT, adjusted for individual-level factors (baseline model). Each nSES measure was added to the baseline model and changes in model fit, geographic disparity and state-wide RR ranges were compared. All 7 nSES measures were strongly associated with high risk clusters. Tract-level RR ranges and geographic disparity from the baseline model were attenuated after adjustment for nSES measures. Depending on the nSES measure selected, up to 60% of the LC burden could be explained, suggesting methodologic evaluations of multiple nSES measures may be warranted in future studies to inform LC prevention efforts.
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Schwartz, Brian S., Marynia Kolak, Jonathan S. Pollak, Melissa N. Poulsen, Karen Bandeen-Roche, Katherine A. Moon, Joseph DeWalle i in. "Associations of four indexes of social determinants of health and two community typologies with new onset type 2 diabetes across a diverse geography in Pennsylvania". PLOS ONE 17, nr 9 (16.09.2022): e0274758. http://dx.doi.org/10.1371/journal.pone.0274758.

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Evaluation of geographic disparities in type 2 diabetes (T2D) onset requires multidimensional approaches at a relevant spatial scale to characterize community types and features that could influence this health outcome. Using Geisinger electronic health records (2008–2016), we conducted a nested case-control study of new onset T2D in a 37-county area of Pennsylvania. The study included 15,888 incident T2D cases and 79,435 controls without diabetes, frequency-matched 1:5 on age, sex, and year of diagnosis or encounter. We characterized patients’ residential census tracts by four dimensions of social determinants of health (SDOH) and into a 7-category SDOH census tract typology previously generated for the entire United States by dimension reduction techniques. Finally, because the SDOH census tract typology classified 83% of the study region’s census tracts into two heterogeneous categories, termed rural affordable-like and suburban affluent-like, to further delineate geographies relevant to T2D, we subdivided these two typology categories by administrative community types (U.S. Census Bureau minor civil divisions of township, borough, city). We used generalized estimating equations to examine associations of 1) four SDOH indexes, 2) SDOH census tract typology, and 3) modified typology, with odds of new onset T2D, controlling for individual-level confounding variables. Two SDOH dimensions, higher socioeconomic advantage and higher mobility (tracts with fewer seniors and disabled adults) were independently associated with lower odds of T2D. Compared to rural affordable-like as the reference group, residence in tracts categorized as extreme poverty (odds ratio [95% confidence interval] = 1.11 [1.02, 1.21]) or multilingual working (1.07 [1.03, 1.23]) were associated with higher odds of new onset T2D. Suburban affluent-like was associated with lower odds of T2D (0.92 [0.87, 0.97]). With the modified typology, the strongest association (1.37 [1.15, 1.63]) was observed in cities in the suburban affluent-like category (vs. rural affordable-like–township), followed by cities in the rural affordable-like category (1.20 [1.05, 1.36]). We conclude that in evaluating geographic disparities in T2D onset, it is beneficial to conduct simultaneous evaluation of SDOH in multiple dimensions. Associations with the modified typology showed the importance of incorporating governmentally, behaviorally, and experientially relevant community definitions when evaluating geographic health disparities.
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Książki na temat "Pennsylvania, census"

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Center, Pennsylvania State Data. 2002 economic census: Pennsylvania. Middletown, PA: Pennsylvania State Data Center, 2006.

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Bureau, U. S. Census, red. Pennsylvania, 2000. [Washington, D.C.]: U.S. Dept. of Commerce, Economics and Statistics Administration, U.S. Census Bureau, 2003.

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Steuart, Raeone Christensen. Pennsylvania central 1870 census index. Bountiful, Utah: Precision Indexing, 1997.

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Christensen, Steuart Raeone, red. Pennsylvania west 1870 census index. Bountiful, Utah: Heritage Quest, 1999.

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W, Steuart Bradley, i Precision Indexing, red. Pennsylvania west, 1870 census index. Bountiful, Utah: Precision Indfexing, 1993.

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W, Steuart Bradley, i Precision Indexing, red. Pennsylvania east 1870 census index. Bountiful, Utah: Precision Indexing, 1994.

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Shultz, Mary E. Greene County, Pennsylvania, 1820 census. Bowie, Md: Heritage Books, 1988.

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Center, Pennsylvania State Data. Pennsylvania decennial census population, 1960-2010. Harrisburg, Pennsylvania: Pennsylvania State Data Center, 2011.

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Society, Berks County Genealogical, i Berks County (Pa.). Board of County Commissionaers., red. Pennsylvania septennial census, Berks County, 1779. Reading, Pa: Reprinted by Berks County Genealogical Society, 1989.

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Jackson, Ronald Vern. Pennsylvania, 1860, west, U.S. federal census index. West Jordan, Utah: Genealogical Services, 1998.

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Części książek na temat "Pennsylvania, census"

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Koohi-Kamali, Feridoon, i Ran Liu. "US Multidimensional Poverty by Race, Ethnicity and Motherhood: Evidence from Pennsylvania Census Data". W Measuring Multidimensional Poverty and Deprivation, 223–54. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58368-6_9.

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"Appendix D: Census Demographic Data on Pennsylvania’s Sixty-Seven Counties". W The Realignment of Pennsylvania Politics Since 1960, 354–60. Penn State University Press, 2009. http://dx.doi.org/10.1515/9780271085777-016.

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Seabrook, Nicholas R. "The Unrealized Precedent of DAVIS V. BANDEMER". W Drawing the Lines. Cornell University Press, 2016. http://dx.doi.org/10.7591/cornell/9781501705311.003.0003.

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This chapter examines the involvement of the Supreme Court of the United States in litigation relating to partisan gerrymandering, paying particular attention to a case that attempted to apply the previously established Davis v. Bandemer precedent to congressional elections: Vieth v. Jubelirer. It begins with an overview of Badham v. Eu, which arose from the redrawing of California's congressional districts in the aftermath of the 1980 census and its most significant holding: that the Bandemer precedent, which had initially been applied to the drawing of state legislative districts only, also extends to the drawing of congressional districts. The chapter then considers the circumstances surrounding the Vieth case, in which the alleged political gerrymander concerned the reapportionment plan for the congressional districts in the state of Pennsylvania rather than those for the state assembly. It also analyzes the Supreme Court's 2004 decision in Vieth, focusing on Justice Antonin Scalia's plurality opinion and Justice Anthony Kennedy's concurring opinion.
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Dolney, Timothy J. "A GIS Methodology for Assessing the Safety Hazards of Abandoned Mine Lands (AMLs)". W Geographic Information Systems, 2128–49. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2038-4.ch126.

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Abandoned mine lands (AMLs) associated with past coal-mining scar Pennsylvania’s landscape with environmental and safety hazards. Reclamation projects eliminate and reduce hazards AMLs pose. Due to the large number of AMLs and limited funds, precedence is given to reclaim the most hazardous sites first. These sites are identified through an assessment process that assigns priorities to AMLs. However, priorities are out-dated and do not accurately reflect the current spatial distribution of land use and census data. This article presents a GIS methodology for the prioritization of AMLs using the process of extrapolation and focal statistics. By incorporating current assessment techniques into GIS with current land use and census data, AML priorities were reassigned to accurately reflect the current spatial landscape. Results indicate that current AML priorities assigned by the state do not accurately reflect current land use and census data and underestimate the safety hazards of many sites, including high priority sites.
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