Sudan SD: Population Density: People per Square Km data was reported at 23.258 Person/sq km in 2017. This records an increase from the previous number of 22.689 Person/sq km for 2016. Sudan SD: Population Density: People per Square Km data is updated yearly, averaging 10.545 Person/sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 23.258 Person/sq km in 2017 and a record low of 4.529 Person/sq km in 1961. Sudan SD: 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 Sudan – Table SD.World Bank.WDI: 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;

Sudan SD: Population Density: Inhabitants per sq km data was reported at 26.440 Person in 2022. This records an increase from the previous number of 25.730 Person for 2021. Sudan SD: Population Density: Inhabitants per sq km data is updated yearly, averaging 22.870 Person from Dec 2012 (Median) to 2022, with 11 observations. The data reached an all-time high of 26.440 Person in 2022 and a record low of 19.770 Person in 2012. Sudan SD: Population Density: Inhabitants per sq km data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Sudan – Table SD.OECD.GGI: Social: Demography: Non OECD Member: Annual.

Map containing historical census data from 1900 - 2000 throughout the western United States at the county level. Data includes total population, population density, and percent population change by decade for each county. Population data was obtained from the US Census Bureau and joined to 1:2,000,000 scale National Atlas counties shapefile.

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.

These data were generated to compare different methods of estimating population density from marked and unmarked animal populations. We compare conventional live trapping with two more modern, non-invasive field methods of population estimation: genetic fingerprinting from hair-tube sampling and camera trapping for the European pine marten (Martes martes). We used arrays of camera traps, live traps, and hair tubes to collect the relevant data in the Ring of Gullion in Northern Ireland. We apply marked spatial capture-recapture models to the genetic and live trapping data where individuals were identifiable, and unmarked spatial capture-recapture (uSCR), distance sampling (CT-DS), and random encounter models (REM) to the camera trap data where individual ID was not possible. All five approaches produced plausible and relatively consistent point estimates (0.41 – 0.99 animals per km2), despite differences in precision, cost, and effort being apparent. In addition to the data, we provide novel code for running unmarked spatial capture-recapture (uSCR) and random encounter models (REM) to the camera trap data where individual ID was not possible. Methods All fieldwork was carried out in the Ring of Gullion, Northern Ireland, UK. Cameras Thirty Bushnell HD Trophy Cam 8MP camera traps (model number: 119577) with 8GB SD cards were deployed during June and July 2019. Thirty Bushnell HD Trophy Cam 8MP camera traps (model number: 119577) with 8GB SD cards were deployed during June and July 2019. At the end of the survey period, camera traps were checked and for each detection (the first image in a trigger sequence of an individual pine marten) distance to animal (m) and angle of detection (°) were measured in situ. Noninvasive genetic sampling Twenty hair tubes based on those developed by Mullins et al. (2010), were deployed across the study site between June and July 2019. Hair-tubes were checked weekly and sticky patches and bait were replaced on each visit. Hair samples were frozen at -20oC prior to DNA extraction. Microsatellite analysis to identify individual pine marten was carried out using up to 11 microsatellite markers. Each sample was analysed in duplicate and only samples giving identical results in the replicates were scored. Live traps Twelve Tomahawk 205 live cage traps were deployed along two perpendicular transects spaced approximately 400m apart. Trapping was conducted from August - October 2019 with daily trap checks. Trapped animals were anaesthetised with an intramuscular injection of ketamine (25mg per kg) and midazolam (0.2mg per kg) and scanned for a microchip. Statistical analyses Spatially explicit capture-recapture (SECR) models were used to estimate density for both live trapping and gNIS (Efford & Boulanger, 2019). Occasion lengths for live trapping were one day, whilst for gNIS were one week. For live trapping, we specified a single-use detector type, whilst for gNIS we specified a proximity-based detector type. Density was calculated from camera traps using REM (Rowcliffe et al. 2008), CT-DS (Howe et al. 2017) and uSCR (Chandler & Royle, 2013).

description: This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of South Dakota. 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 South Dakota. 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 South Dakota. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F71834H6; abstract: This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of South Dakota. 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 South Dakota. 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 South Dakota. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F71834H6

Population density is fundamental information for assessing the conservation status of species and support management and conservation actions for in situ populations, but is unknown for many forest species due to their difficulty in detection. The Fecal Standing Crop (FSC) method using detection dogs is an alternative for cryptic or elusive species. An intrinsic difficulty of FSC is the ability to find fecal samples in the field and to estimate the probability of which feces detection is influenced by degradation due to climatic conditions. Our goal was to propose a concurrent FSC parameter estimation using a scat detection dog under different climatic conditions and apply those parameters in a wild deer population. Ten fecal samples of gray brocket deer (Subulo gouazoubira) were placed weekly in a transect (24 x 1200 m) in both dry and wet seasons (12 weeks each). A scat detection dog was then employed to find experimental fecal samples to determine the FSC parameters that were subsequently used with naturally occurring fecal samples (also dog-detected) to estimate population density. The oldest dog found samples were 21 (Dry) and seven (Wet) days after placement, resulting in dog efficiency of 23% (Dry) and 30% (Wet). Adjusting the model to account for efficiency and scat durability, we estimated similar, seasonal, densities of 4.54 individuals km-2 (SD = 2.21, Dry) and 5.52 indiv. km-2 (SD = 3.71, Wet). Synthesis and applications: Our results demonstrate that our concurrent methodology corrected the effects of weather and habitat on FSC parameters thereby allowing for accurate population density estimation. Additionally, this method can provide reasonably precise density estimates with a logistically feasible sample size, as demonstrated by simulation. Following our recommendations, this method allows a reliable estimate of population density because it incorporates any influence of study area, dog ability, and climate in fecal sample detection, providing fundamental information for the conservation of many cryptic and elusive species.

SD：人口密度：每平方公里人口在12-01-2017达23.258Person/sq km，相较于12-01-2016的22.689Person/sq km有所增长。SD：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10.545Person/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23.258Person/sq km，而历史最低值则出现于12-01-1961，为4.529Person/sq km。CEIC提供的SD：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，数据归类于全球数据库的苏丹 – 表 SD.世行.WDI：人口和城市化进程统计。

人口密度：每平方公里的居民在12-01-2022达26.440人，相较于12-01-2021的25.730人有所增长。人口密度：每平方公里的居民数据按年更新，12-01-2012至12-01-2022期间平均值为22.870人，共11份观测结果。该数据的历史最高值出现于12-01-2022，达26.440人，而历史最低值则出现于12-01-2012，为19.770人。CEIC提供的人口密度：每平方公里的居民数据处于定期更新的状态，数据来源于Organisation for Economic Co-operation and Development，数据归类于全球数据库的苏丹 – Table SD.OECD.GGI: Social: Demography: Non OECD Member: Annual。

Socio-demographic characteristics of respondents, SNNPR.

Knowledge of COVID-19 and its prevention and control in urban settings, SNNPR.

African lions are declining across much of their range, yet robust measures of population densities remain rare. The Queen Elizabeth Conservation Area (QECA; 2400 km2) in East Africa’s Albertine Rift has potential to support a significant lion population. However, QECA lions are threatened, and information on the status of lions in the region is lacking. Here, we use a spatially explicit search encounter approach to estimate key population parameters of lions in the QECA. We then compare home range sizes estimated from our models to those from a radio-collaring study implemented a decade earlier. We recorded 8243.5 km of search effort over 93 days, detecting 30 individual lions (16 female and 14 male) on 165 occasions at a rate of 2 lion detections/100 km2. Lion density in the QECA was 2.70 adult lions/100 km2(SD=0.47), while mean abundance was 71 individuals (SD=11.05). Worryingly, the movement parameter for male lions was 3.27 km and 2.22 km for females, suggesting >400%, and >1...

1. The high cost associated with capture-recapture studies presents a major challenge when monitoring and managing wildlife populations. Recently-developed spatial mark-resight (SMR) models were proposed as a cost-effective alternative because they only require a single marking event. However, existing SMR models ignore the marking process and make the tenuous assumption that marked and unmarked populations have the same encounter probabilities. This assumption will be violated in most situations because the marking process results in different spatial distributions of marked and unmarked animals. 2. We developed a generalized SMR model that includes sub-models for the marking and resighting processes, thereby relaxing the assumption that marked and unmarked populations have the same spatial distributions and encounter probabilities. 3. Our simulation study demonstrated that conventional SMR models produce biased density estimates with low credible interval coverage when marked and unmarked animals had differing spatial distributions. In contrast, generalized SMR models produced unbiased density estimates with correct credible interval coverage in all scenarios. 4. We applied our SMR model to grizzly bear (Ursus arctos) data where the marking process occurred along a transportation route through Banff and Yoho National Parks, Canada. Twenty-two grizzly bears were trapped, fitted with radio-collars, and then detected along with unmarked bears on 214 remote cameras. Closed population density estimates (posterior median + 1 SD) averaged from 2012 to 2014 were much lower for conventional SMR models (7.4 + 1.0 bears per 1,000 km2) than for generalized SMR models (12.4 + 1.5). When compared to previous DNA-based estimates, conventional SMR estimates erroneously suggested a 51% decline in density. Conversely, generalized SMR estimates were similar to previous estimates, indicating that the grizzly bear population was relatively stable. 5. Synthesis and application. Conventional SMR models that ignore the marking process should only be used when marked and unmarked animals share the same spatial distribution, such as when a subset of the population has natural marks. Generalized SMR models that include the marking process are much more widely applicable. They represent a promising new approach for reducing the costs of studies aimed at understanding spatial and temporal variation in density.24-May-2017

Bivariable and multivariable binary logistic regression on factors associated with good COVID-19 prevention, SNNPR.

COVID-19 prevention methods practiced by respondents in urban settings, SNNPR.

Attitude towards COVID-19 and its prevention and control in urban settings, SNNPR.

Tests of significance were run based on Euclidean distances for square root transformed data.*statistically significant differences (p

Tests of significance were run based on Euclidean distances for square root transformed data.*statistically significant differences (p

Posterior summaries from the Bayesian-SECR model parameters of camera trap data from Danum Valley, Tabin North and Tawau.

Sampling specifications and marbled cat capture data from the closed survey periods in Danum Valley, Tabin North and Tawau.