This graph shows the population density in the federal state of Pennsylvania from 1960 to 2018. In 2018, the population density of Pennsylvania stood at 286.2 residents per square mile of land area.

Panama PA: Population Density: People per Square Km data was reported at 55.133 Person/sq km in 2017. This records an increase from the previous number of 54.266 Person/sq km for 2016. Panama PA: Population Density: People per Square Km data is updated yearly, averaging 32.546 Person/sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 55.133 Person/sq km in 2017 and a record low of 15.699 Person/sq km in 1961. Panama PA: Population Density: People per Square Km data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Panama – Table PA.World Bank: Population and Urbanization Statistics. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.; ; Food and Agriculture Organization and World Bank population estimates.; Weighted average;

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined because of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division or incorporated place boundaries in some states and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard Census Bureau geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

Population Density: Total data was reported at 54.424 Person/sq km in 2016. This records an increase from the previous number of 53.593 Person/sq km for 2015. Population Density: Total data is updated yearly, averaging 52.756 Person/sq km from Dec 2012 (Median) to 2016, with 5 observations. The data reached an all-time high of 54.424 Person/sq km in 2016 and a record low of 51.060 Person/sq km in 2012. Population Density: Total data remains active status in CEIC and is reported by National Institute of Statistics and Census. The data is categorized under Global Database’s Panama – Table PA.G004: Population: Population Density.

Storymap outlining Covid-19 activity and population vulnerability in Pennsylvania.Features include:Time enabled map for PA outbreak from March through AprilDaily case updatesMap and table breakdown of CDC's Social Vulnerability IndexKey factors map for population density, highways, and Covid-19 cases.

This raster dataset represents 2020 population density from the Gridded Population of the World, Version 4 (GPWv4) dataset, sourced from the Center for International Earth Science Information Network (CIESIN). The data has been clipped to the Northeast USA and normalized to a 0-100 scale to facilitate comparison between population distribution and recreational use of forests. This raster helps identify spatial outliers, where forest recreation is high in areas with low population density, offering insights for land management and conservation planning.Data Source:GPWv4 Population Density, 2020 Revision 11Clipped to the Northeast (ME, NH, VT, NY, MA, CT, RI, PA, NJ)Use Case:Used to compare forest recreation hotspots with population density, revealing areas where recreation is disproportionate to local population, assisting in identifying outliers for focused study or management efforts.

PA：人口密度：每平方公里人口在12-01-2017达55.133Person/sq km，相较于12-01-2016的54.266Person/sq km有所增长。PA：人口密度：每平方公里人口数据按年更新，12-01-1961至12-01-2017期间平均值为32.546Person/sq km，共57份观测结果。该数据的历史最高值出现于12-01-2017，达55.133Person/sq km，而历史最低值则出现于12-01-1961，为15.699Person/sq km。CEIC提供的PA：人口密度：每平方公里人口数据处于定期更新的状态，数据来源于World Bank，数据归类于Global Database的巴拿马 – 表 PA.世界银行：人口和城市化进程统计。

The 2015 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2010.

description: This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of Pennsylvania. This dataset is the result of clipping the feature class 'NFHAP 2010 HCI Scores and Human Disturbance Data for the Conterminous United States linked to NHDPLUSV1.gdb' to the state boundary of Pennsylvania. Landscape factors include land uses, population density, roads, dams, mines, and point-source pollution sites. The source datasets that were compiled and attributed to catchments were identified as being: (1) meaningful for assessing fish habitat; (2) consistent across the entire study area in the way that they were assembled; (3) representative of conditions in the past 10 years, and (4) of sufficient spatial resolution that they could be used to make valid comparisons among local catchment units. In this data set, these variables are linked to the catchments of the National Hydrography Dataset Plus Version 1 (NHDPlusV1) using the COMID identifier. They can also be linked to the reaches of the NHDPlusV1 using the COMID identifier. Catchment attributes are available for both local catchments (defined as the land area draining directly to a reach; attributes begin with "L_" prefix) and network catchments (defined by all upstream contributing catchments to the reach's outlet, including the reach's own local catchment; attributes begin with "N_" prefix). This shapefile also includes habitat condition scores created based on responsiveness of biological metrics to anthropogenic landscape disturbances throughout ecoregions. Separate scores were created by considering disturbances within local catchments, network catchments, and a cumulative score that accounted for the most limiting disturbance operating on a given biological metric in either local or network catchments. This assessment only scored reaches representing streams and rivers (see the process section for more details). Please use the following citation: Esselman, P., D.M. Infante, L. Wang, W. Taylor, W. Daniel, R. Tingley, J. Fenner, A. Cooper, D. Wieferich, D. Thornbrugh and J. Ross. (April 2011) National Fish Habitat Action Plan (NFHAP) 2010 HCI Scores and Human Disturbance Data (linked to NHDPLUSV1) for Pennsylvania. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F7DJ5CMC; abstract: This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of Pennsylvania. This dataset is the result of clipping the feature class 'NFHAP 2010 HCI Scores and Human Disturbance Data for the Conterminous United States linked to NHDPLUSV1.gdb' to the state boundary of Pennsylvania. Landscape factors include land uses, population density, roads, dams, mines, and point-source pollution sites. The source datasets that were compiled and attributed to catchments were identified as being: (1) meaningful for assessing fish habitat; (2) consistent across the entire study area in the way that they were assembled; (3) representative of conditions in the past 10 years, and (4) of sufficient spatial resolution that they could be used to make valid comparisons among local catchment units. In this data set, these variables are linked to the catchments of the National Hydrography Dataset Plus Version 1 (NHDPlusV1) using the COMID identifier. They can also be linked to the reaches of the NHDPlusV1 using the COMID identifier. Catchment attributes are available for both local catchments (defined as the land area draining directly to a reach; attributes begin with "L_" prefix) and network catchments (defined by all upstream contributing catchments to the reach's outlet, including the reach's own local catchment; attributes begin with "N_" prefix). This shapefile also includes habitat condition scores created based on responsiveness of biological metrics to anthropogenic landscape disturbances throughout ecoregions. Separate scores were created by considering disturbances within local catchments, network catchments, and a cumulative score that accounted for the most limiting disturbance operating on a given biological metric in either local or network catchments. This assessment only scored reaches representing streams and rivers (see the process section for more details). Please use the following citation: Esselman, P., D.M. Infante, L. Wang, W. Taylor, W. Daniel, R. Tingley, J. Fenner, A. Cooper, D. Wieferich, D. Thornbrugh and J. Ross. (April 2011) National Fish Habitat Action Plan (NFHAP) 2010 HCI Scores and Human Disturbance Data (linked to NHDPLUSV1) for Pennsylvania. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F7DJ5CMC

Aedes albopictus is a viable vector for several infectious diseases such as Zika, West Nile, Dengue viruses and others. Originating from Asia, this invasive species is rapidly expanding into North American temperate areas and urbanized places causing major concerns for public health. Previous analyses show that warm temperatures and high humidity during the mosquito season are ideal conditions for A. albopictus development, while its distribution is correlated with population density. To better understand A. albopictus expansion into urban places it is important to consider the role of both environmental and neighborhood factors. The present study aims to assess the relative importance of both environmental variables and neighborhood factors in the prediction of A. albopictus’ presence in Southeast Pennsylvania using MaxEnt (version 3.4.1) machine-learning algorithm. Three models are developed that include: (1) exclusively environmental variables, (2) exclusively neighborhood factors, and (3) a combination of environmental variables and neighborhood factors. Outcomes from the three models are compared in terms of variable importance, accuracy, and the spatial distribution of predicted A. albopictus’ presence. All three models predicted the presence of A. albopictus in urban centers, however, each to a different spatial extent. The combined model resulted in the highest accuracy (74.7%) compared to the model with only environmental variables (73.5%) and to the model with only neighborhood factors (72.1%) separately. Although the combined model does not essentially increase the accuracy in the prediction, the spatial patterns of mosquito distribution are different when compared to environmental or neighborhood factors alone. Environmental variables help to explain conditions associated with mosquitoes in suburban/rural areas, while neighborhood factors summarize the local conditions that can also impact mosquito habitats in predominantly urban places. Overall, the present study shows that MaxEnt is suitable for integrating neighborhood factors associated with mosquito presence that can complement and improve species distribution modeling.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study was designed to investigate whether the presence of CCTV cameras can reduce crime by studying the cameras and crime statistics of a controlled area. The viewsheds of over 100 CCTV cameras within the city of Philadelphia, Pennsylvania were defined and grouped into 13 clusters, and camera locations were digitally mapped. Crime data from 2003-2013 was collected from areas that were visible to the selected cameras, as well as data from control and displacement areas using an incident reporting database that records the location of crime events. Demographic information was also collected from the mapped areas, such as population density, household information, and data on the specific camera(s) in the area. This study also investigated the perception of CCTV cameras, and interviewed members of the public regarding topics such as what they thought the camera could see, who was watching the camera feed, and if they were concerned about being filmed.

The PED ans MAP files contain SNP data used in Urnikyte A et al. (Patterns of genetic structure and adaptive positive selection in the Lithuanian population from high-density SNP data).

The data set consists of 424 samples and 532,836 SNPs after filtering. Genotyping was performed at the Department of Human and Medical Genetics, Biomedical Science Institute, Faculty of Medicine, Vilnius University, Lithuania with the Illumina HumanOmniExpress-12v1.1 (296 samples) and the Infinium OmniExpress-24 (129 samples) arrays (Illumina, San Diego, CA, USA).

Population-Averaged Generalized Estimating Equations (PA-GEEs) results examining the effects of time, distance and site on Evechinus density. Log-link and Poisson errors. **P

gyp_sim.cpp (version 04/10/2016) was developed by Dr Penelope A. Hancockgyp_sim.cpp comprises C++ code for implementing the modelof mosquito-Wolbachia dynamics developed in Hancock et al. 2016,"Predicting Wolbachia invasion dynamics in Aedes aegypti populations usingmodels of density-dependent demographic traits", BMC Biology.To run gyp_sim use the command gyp_sim.exeThe file gyp_sim_inits.txt contains the following inputs:Cohort_means.txt (a file for storing the mean development times of uninfected larvae in each cohort)Cohort_means_wolb.txt (a file for storing the mean development times of infected larvae in each cohort)Cohort_stds.txt (a file for storing the standard deviations of the development times of uninfected larvae in each cohort)Cohort_stds_wolb.txt (a file for storing the standard deviations of the development times of infected larvae in each cohort)mu_p.txt (a file for storing the number of uninfected pupae that eclose on each day)L_file.txt (a file for storing the number of uninfected larvae present on each day)A_file.txt (a file for storing the number of uninfected adults present on each day)mu_p_wolb.txt (a file for storing the number of infected pupae that eclose on each day)L_wolb_file.txt (a file for storing the number of infected larvae present on each day)A_wolb_file.txt (a file for storing the number of infected adults present on each day)FreqA2_file.txt (a file for storing the Wolbachia frequency on the final day of release)lambda.txt (a file for storing the per-capita female fecundity at the time that each cohort is hatched)release_size.txt (a file for storing the size of each Wolbachia release)700 (the day of the first release)0.1 (additional density-INdependent daily mortality experienced by adults In the field environment)0.1 (additional density-INdependent daily mortality experienced by larvae in the field environment)

Main effects for Treatment and Date are not shown, as all interactions were significant. Treatment effect comparisons are in reference to the controls and the first sampling occasion in August 2011. *P

The Department of Justice launched Operation Weed and Seed in 1991 as a means of mobilizing a large and varied array of resources in a comprehensive, coordinated effort to control crime and drug problems and improve the quality of life in targeted high-crime neighborhoods. In the long term, Weed and Seed programs are intended to reduce levels of crime, violence, drug trafficking, and fear of crime, and to create new jobs, improve housing, enhance the quality of neighborhood life, and reduce alcohol and drug use. This baseline data collection effort is the initial step toward assessing the achievement of the long-term objectives. The evaluation was conducted using a quasi-experimental design, matching households in comparison neighborhoods with the Weed and Seed target neighborhoods. Comparison neighborhoods were chosen to match Weed and Seed target neighborhoods on the basis of crime rates, population demographics, housing characteristics, and size and density. Neighborhoods in eight sites were selected: Akron, OH, Bradenton (North Manatee), FL, Hartford, CT, Las Vegas, NV, Pittsburgh, PA, Salt Lake City, UT, Seattle, WA, and Shreveport, LA. The "neighborhood" in Hartford, CT, was actually a public housing development, which is part of the reason for the smaller number of interviews at this site. Baseline data collection tasks included the completion of in-person surveys with residents in the target and matched comparison neighborhoods, and the provision of guidance to the sites in the collection of important process data on a routine uniform basis. The survey questions can be broadly divided into these areas: (1) respondent demographics, (2) household size and income, (3) perceptions of the neighborhood, and (4) perceptions of city services. Questions addressed in the course of gathering the baseline data include: Are the target and comparison areas sufficiently well-matched that analytic contrasts between the areas over time are valid? Is there evidence that the survey measures are accurate and valid measures of the dependent variables of interest -- fear of crime, victimization, etc.? Are the sample sizes and response rates sufficient to provide ample statistical power for later analyses? Variables cover respondents' perceptions of the neighborhood, safety and observed security measures, police effectiveness, and city services, as well as their ratings of neighborhood crime, disorder, and other problems. Other items included respondents' experiences with victimization, calls/contacts with police and satisfaction with police response, and involvement in community meetings and events. Demographic information on respondents includes year of birth, gender, ethnicity, household income, and employment status.