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19,493 results for “Populism”
Lack of spatial and temporal genetic structure of Japanese eel (Anguilla japonica) populations
Japanese eel (Anguilla japonica) is an important food source in East Asia whose population has dramatically declined since the 1970s. Despite past analysis with DNA sequencing, microsatellite and isozyme methods, management decisions remain hampered by contradictory findings. For example, it remains unresolved whether Japanese eels are a single panmictic population or whether they harbor significant substructure. Accurate assessment of population genetic substructure, both spatial and temporal, is essential for determining the relevant number of distinct management units appropriate for this species. In the present study, we assayed genetic variation genome-wide using Restriction Site Associated DNA Sequencing (RAD-seq) technology to analyze the population genetic structure of Japanese eels. For analysis of temporal isolation, five "cohort" samples were collected yearly from 2005 to 2009 in the Yangtze River Estuary. For analysis of spatial structure, five "arrival wave" samples were collected in China in 2009, and two arrival wave samples were collected in Japan in 2001. In each cohort of each arrival wave, five individuals were collected for a total of 55 eels sampled. In total, 214,210 loci were identified from these individuals, 106,652 of which satisfied quality checks and were retained for further analysis. There was relatively little population differentiation between arrival waves and cohorts collected either at different locations during the same year (Fst = 0.077) or at the same location collected over subsequent years (Fst = 0.082), and locations displayed no consistent isolation-by-distance.
Life history data on married women of the historical population of île aux Coudres
<p>Evidence from natural populations shows that changes in environmental conditions can cause rapid modifications in the evolutionary potential of phenotypes, partly through genotype-by-environment interactions (G×E). Therefore, the overall rate of microevolution should depend on fluctuations in environmental conditions, even when directional selection is sustained over several generations. We tested this hypothesis in a preindustrial human population that experienced a microevolutionary change in age at first reproduction (AFR) of mothers, using the annual infant mortality rate (IMR) as an indicator of environmental conditions during their early life. Using quantitative genetics analyses, we found that G×Es explained a non-negligible fraction of the additive genetic variance in AFR and in relative fitness, as well as of the genetic covariance between AFR and fitness (i.e. the Robertson-Price covariance). The covariance was stronger for individuals exposed to unfavorable early-life environmental conditions. Our results unravel the presence of G×Es in an important life history trait and its impact on the rate of microevolution, which appears to have been sensitive to short-term fluctuations in local environmental conditions.</p>
Hormone receptors AR, ER, PR and growth factor receptor Her-2 expression in oral squamous cell carcinoma: Correlation with overall survival, disease-free survival and 10-year survival in a high-risk population
<p>Oral squamous cell carcinoma (OSCC) comprises most of head and neck neoplasms and is one of the highest-ranking and lethal cancers in Pakistan due to prevailing mouth habits. Growth and hormonal receptors act as prognostic markers and targets for therapy in some cancers, but their application in OSCC is largely unexplored. This study aimed to evaluate the expression of growth and hormonal receptors in OSCC patients and correlate it with 10-year, overall and disease-free survival. To achieve this objective, immunohistochemistry for Her-2, AR, ER and PR was performed on 100 formalin-fixed paraffin-embedded primary OSCC specimens. Receptor expression was correlated with mouth habits and clinicopathological features and patient survival was analyzed using Kaplan-Meier method and Cox regression univariate analysis. We observed that in 100 patients, there were 57 males and 43 females. Immunopositive Her-2 expression was observed in 21% of patients, AR in 13%, ER in 3% and 0% for PR. Patients with betel quid/areca nut mouth habits had significantly absent Her-2 expression (P=0.035). Also, Her-2 negative patients were also negative for AR expression (P=0.002). Her-2 positive patients had poor 10-year survival (P=0.041). A trend of low survival and high recurrence rate was observed in AR positive patients, but this was not significant (P=0.072). No statistically relevant correlations were seen in the case of ER and PR. In conclusion, Her-2 may be a valuable marker for predicting long-term prognosis of OSCC patients.</p>
Data used for 'Parent-offspring inference in inbred populations'
<p>House mouse population founder genotypes used for imputation and simulation</p>
Population Models of Rotating Field Stars in Kepler
<p>Model populations are constructed using TRILEGAL galaxy models coupled to YREC stellar evolutionary models and a magnetic braking law, and are described in van Saders, Pinsonneault, & Barbieri, "Forward Modeling of the Kepler Stellar Rotation Period Distribution: Interpreting Periods from Mixed and Biased Stellar Populations," 2019, ApJ, 872, 128 and updated in Hall et al. 2021 (Nature Astronomy, 5, 707) to incorporate the Berger et al. 2020 (AJ, 159, 280) Kepler Stellar Properties Catalog. </p> <p>Two model populations are provided: one in which rotation is modeled with a "standard" magnetic braking law of the form in van Saders et al. 2013, and a second in which stars are subject to weakened magnetic braking past a critical Rossby number, Rocrit. </p> <p>Models can be unpacked with the hdf with pandas functionality, using key='sample'. The keys should be self-explanatory with the possible exception of "evo", which is the evolutionary state. Choose evo = 1 to select only the main sequence.</p> <p>If you use these models in your work, please cite van Saders et al. 2019, with an additional citation to Hall et al. 2021 appreciated. </p> <p> </p>
No evidence that relatedness or familiarity modulate male harm in Drosophila melanogaster flies from a wild population
<p>Sexual selection frequently promotes the evolution of aggressive behaviours that help males compete against their rivals, but which may harm females and hamper their fitness. Kin selection theory predicts that optimal male-male competition levels can be reduced when competitors are more genetically related to each other than to the population average, contributing to resolve this sexual conflict. Work in <em>Drosophila melanogaster</em> has spearheaded empirical tests of this idea, but studies so far have been conducted in lab-adapted populations in homogeneous rearing environments that may hamper kin recognition, and used highly skewed sex ratios that may fail to reflect average natural conditions. Here, we performed a fully factorial design with the aim of exploring how rearing environment (i.e. familiarity) and relatedness affect male-male aggression, male harassment, and overall male harm levels in flies from a wild population of <em>Drosophila melanogaster</em>, under more natural conditions. Namely, we: a) manipulated relatedness and familiarity so that larvae reared apart were raised in different environments, as is common in the wild, and b) studied the effects of relatedness and familiarity under average levels of male-male competition in the field. We show that, contrary to previous findings, groups of unrelated-unfamiliar males were as likely to fight with each other and harass females than related-familiar males, and that overall levels of male harm to females were similar across treatments. Our results suggest that the role of kin selection in modulating sexual conflict is yet unclear in <em>Drosophila melanogaster</em>, and call for further studies that focus on natural populations and realistic socio-sexual and ecological environments.</p>
Habitat shapes diversity of gut microbiomes in a wild population of blue tits Cyanistes caeruleus
<p>Microbiome constitutes and important axis of individual variation that, together with genes and the environment, influences an individual's physiology and fitness. Microbiomes are dependent not only on an individual's body condition but also on external factors, such as diet or stress levels, and as such can be involved into feedbacks between the external ecological factors and internal physiology. In our study we used a wild population of blue tits (Cyanistes caeruleus) to investigate the impact of external habitat composition on the microbiome of adult birds. We hypothesized, that – through differences in plant composition, potentially affecting diet complexity – habitat type may impact the diversity and structure of the gut microbiome. Blue tits breeding in dense deciduous forests tended to have more diverse microbiomes, and significantly different in terms of microbiome composition from birds breeding in open, sparsely forested hay meadows. Distinct study plots also tended to differ in a number of parameters describing microbiome diversity. We observed no microbiome differentiation according to individual characteristics such as sex or age. The study emphasizes, that external environment is one of the important modulators of microbiome diversity and calls for more such studies in wild animal populations.</p>
Data from: Towards a better ecological understanding of metacommunity stability: A multiscale framework to disentangle population variability and synchrony effects
<p>1. Despite great progress in our understanding of the mechanisms governing ecosystem stability in local communities, we still lack knowledge at a larger spatial scale. Studying the stability of metacommunities requires assessing the temporal stability and synchrony of populations across space and organizational levels. Previous attempts to disentangle these effects have provided limited ecological interpretations, and conceptual improvements are needed to identify the underlying ecological processes.</p> <p>2. We propose an extended framework aiming at disentangling simultaneously the relative effects of population stability and different types of synchronies on metacommunity stability. We adapted previous methods of decomposing stability into a new set of indices associated with clearer ecological hypotheses. Particularly, we provide synchrony indices that are not affected by statistical properties of the metacommunity but focus on species responses to environment, demography, and interactions. We applied this framework to a unique dataset describing the sorted biomass of individual plant populations, across 12 communities of a species-rich meadow, and for 16 years. The communities were sampled in different treatments of fertilization and dominant removal to evaluate the effect of environmental heterogeneity on stability.</p> <p>3. We found higher stability at a larger spatial scale, mainly due to statistical averaging (portfolio effect). The variability of individual populations was an important determinant of the stability of the whole metacommunity. Consistent with the hypothesis of a common response to environmental conditions, we found that the fluctuations of populations were mostly synchronized (within and between species) at a large spatial scale and tended to destabilize the metacommunity. On the other hand, opposite fluctuations (anti-synchrony) between populations occurred at the local scale, probably due to local species interactions.</p> <p>4. Synthesis Our framework appears as a powerful tool to test how ecological processes occurring simultaneously at different spatial and organizational scales affect the stability of metacommunities. This study advances our ecological understanding of the processes underlying the stability of species-rich environments. --</p>
Data from: Sustained positive consequences of genetic rescue of fitness and behavioural traits in inbred populations of Drosophila melanogaster
<p><span>One solution to alleviate the detrimental genetic effects associated with reductions in population size and fragmentation is to introduce immigrants from other populations. While the effects of this genetic rescue on fitness traits are fairly well known, it is less clear to what extent inbreeding depression and subsequent genetic rescue affects behavioural traits. In this study, replicated crosses between inbred lines of <em>Drosophila melanogaster</em> were performed in order to investigate the effects of inbreeding and genetic rescue on egg-to-adult viability and negative geotaxis behaviour - a locomotor response used to measure e.g. the effects of physiological ageing. Transgenerational effects of outcrossing were investigated by examining the fitness consequences in both the F<sub>1</sub> and F<sub>4</sub> generation. The majority of inbred lines showed evidence for inbreeding depression for both egg-to-adult viability and behavioural performance (95% and 66% of lines, respectively), with inbreeding depression being more pronounced for viability compared to locomotor response. Subsequent outcrossing with immigrants led to an alleviation of the negative effects for both viability and geotaxis response resulting in inbred lines being similar to the outbred controls, with beneficial effects persisting from F<sub>1</sub> to F<sub>4</sub>. Overall, the results clearly show that genetic rescue can provide transgenerational rescue of small, inbred populations by rapidly improving population fitness components. Thus, we show that even the negative effects of inbreeding on behaviour, similar to that of neurodegeneration associated with physiological ageing, can be reversed by genetic rescue.</span></p>
Fisheries bycatch mitigation measures as an efficient tool for the conservation of seabird populations
<p class="MsoNormal"><span>The impact of industrial fisheries on marine biodiversity is conspicuous in large pelagic vertebrate fisheries bycatch. In seabirds, this led to the decline of many populations since the 1980s following the rise of global fishing efforts. Bycatch mitigation measures were implemented in the 2000s, but their effects on the concerned seabird populations remain poorly quantified and understood.</span></p> <p class="MsoNormal"><span>We studied the effects of bycatch mitigation measures on the demography of the white-chinned petrel, one of the most bycatch impacted seabirds whose populations suffered dramatic declines before the implementation of mitigation measures. To do so we 1) built multi-event capture-recapture models to estimate the demographic parameters of a population from Possession Island (southern Indian Ocean) over thirty years, 2) assessed the effect of climate and fishery covariates on demographic parameters, 3) built a population matrix model to estimate stochastic growth rate according to the management in fisheries bycatch, and 4) estimated changes in breeding population density using distance sampling data.</span></p> <p class="MsoNormal"><span>The population declined from the 1980s to the mid-2000s, while trawl and longline fisheries occurred with no bycatch mitigation measures. The negative effects of fishery bycatch through additive mortality and of rat predation on breeding success were likely the main drivers of this decline.</span></p> <p class="MsoNormal"><span>Both modeled population growth rate and observed breeding densities showed an increase since the mid-2000s. We explained this trend by the improvement in survival probability following implementation of fishery bycatch mitigation measures and in breeding success probability with the local control of the rat population and changes in sea ice conditions on foraging grounds.</span></p> <p class="MsoNormal"><span>Synthesis and applications: We provide a holistic approach to assess the effects of management measures by analysing datasets from sampling methods commonly employed in seabirds studies. Our conclusions should encourage the eradication of invasive predatory species in seabirds breeding areas and the strengthening of bycatch mitigation measures for the vulnerable seabird species, especially in international waters, but also the development of such measures considering the other marine large pelagic species threatened by fisheries bycatch (sharks, rays, turtles and marine mammals) since it could be crucial to avoid populations' extinction.</span></p>
Estimated population exposed to floods with a global flood model (CaMa-Flood v4.00)
<p>This is a repository for data and codes to analyze the population exposed to floods at grid level (0.25deg) and country level. The flood hazard map is modeled from the global hydrodynamic model - Catchment-based Macro-scale Floodplain (CaMa-Flood) model. </p>
Morphological plasticity in response to emergence time and population density in Abutilon theophrasti (Malvaceae)
<p>Increased density and delayed emergence are two major biotic factors in nature that have profound and complex effects on plants. No studies have attempt to compare the responses of plants to the two factors via morphological plasticity, particularly in dynamic patterns. We subjected plants of <em>Abutilon theophrasti</em> to four emergence times and three planting densities and measured and analyzed a number of mass and morphological traits at different growth stages. Across both stages, plants emerged in late spring had the highest total mass, and spring and late-spring plants had higher stem mass allocation than later germinants, but plants with delayed emergence had higher leaf and reproductive mass allocation, more leaves and less lateral roots, but lower stem length, stem and root diameter than early-emerged plants. Plants at high density performed lower in total mass and most other traits, but performed better in stem allocation and length, with shorter petioles and lateral roots, than at lower densities. In competition for resources, plants will prefer stem elongation to leaf/root growth, and even at the cost of reproduction to ensure the survival of the present generations when competition is intense or lethal. By contrast, plants will prefer the investment into leaf and reproductive growth to stem/root growth, for offspring persistence, when shortened lifetime does not threaten the contemporary survival. The contrasting strategies revealed the intelligence of plants in balancing between survival, growth and reproduction, depending on environmental contexts.</p>
Data from: Tracking population genetic signatures of local extinction with herbarium specimens
<p><strong><span>Background and Aims </span></strong><span>Habitat degradation and landscape fragmentation dramatically lower population sizes of rare plant species. Decreasing population sizes may, in turn, negatively affect genetic diversity and reproductive fitness which can ultimately lead to local extinction of populations. Although such extinction vortex dynamics have been postulated in theory and modelling for decades, empirical evidence from local extinctions of plant populations is scarce. In particular, comparisons between current vs. historical genetic diversity and differentiation are lacking despite their potential to guide conservation management.</span></p> <p><strong><span>Methods </span></strong><span>We studied the population genetic signatures of the local extinction of <em>Biscutella laevigata</em> subsp. <em>gracilis </em>populations in Central Germany. We used microsatellites to genotype individuals from 15 current populations, one ex-situ population, and 81 herbarium samples from five extant and 22 extinct populations. In the current populations, we recorded population size and fitness proxies, collected seeds for a germination trial and conducted a vegetation survey. The latter served as surrogate for habitat conditions to study how habitat dissimilarity affects functional connectivity among the current populations. </span></p> <p><strong><span>Key Results </span></strong><span>Bayesian clustering revealed similar gene pool distribution in current and historical samples but also indicated that a distinct genetic cluster was significantly associated with extinction probability. Gene flow was affected by both spatial distance and floristic composition of population sites, highlighting the potential of floristic composition as powerful predictor of functional connectivity which may promote decision making for reintroduction measures. For an extinct population, we found a negative relationship between sampling year and heterozygosity. Inbreeding negatively affected germination. </span></p> <p><strong><span>Conclusions</span></strong><span> Our study illustrates the usefulness of historical DNA to study extinction vortices in threatened species. Our novel combination of classical population genetics together with data from herbarium specimens, an ex-situ population and a germination trial underscores the need for genetic rescue measures to prevent extinction of <em>B. laevigata</em> in Central Germany. </span></p>
Estimating abundance in unmarked populations of Golden Eagle
<p> 1. Estimates of species abundance are of key importance in population and ecosystem level research but can be hard to obtain. Study designs using camera-traps are increasingly being used for large-scale monitoring of species that are elusive and/or occur naturally at low densities.</p> <p>2. Golden eagle (Aquila chrysaetos) is one such species, and we investigate whether existing large-scale monitoring programs using baited camera-traps can be used to estimate the abundance of golden eagles, as an alternative to traditional labour-intensive searches for active territories and nest sites during the breeding period.</p> <p>3. The camera-trap data allowed two measures of abundance to be estimated within each of four main study areas in mid and northern Norway; occupancy was measured as the probability of camera site use, and population size was measured as the number of eagle individuals using the camera sites within a study area. Spatial and temporal patterns in occupancy and population size were explored and evaluated against independent estimates of the breeding pair density in the study areas.</p> <p>4. Annual estimates of golden eagle occupancy showed low precision, while estimates of population size were more precise in relation to both estimated and anticipated abundance fluctuations. Estimates of population size may therefore be suitable for monitoring within study area temporal abundance trends, while estimates of occupancy seem unsuitable for such in golden eagles. Across study areas, patterns in both average occupancy and average population density estimated from population size, were consistent with the spatial pattern in average breeding pair densities (r = 0.99, and r = 0.89 respectively). This suggests that camera-trap based estimates of occupancy and population density reflect territory density at large spatial scales. In conclusion, our results suggest that baited camera-traps can be a cost-effective strategy for monitoring the abundance of golden eagles.</p>
Synthetic population for USA_FLORIDA
<p><strong>Synthetic populations for regions of the World (SPW) | Florida</strong></p><p><strong>Dataset information</strong></p><p>A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).</p> <p><strong>License</strong></p><p><a href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0</a></p> <p><strong>Acknowledgment</strong></p><p>This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).</p> <p><strong>Contact information</strong></p><p>Henning.Mortveit@virginia.edu</p> <p><strong>Identifiers</strong></p><table> <thead><tr> <th></th> <th></th> </tr> </thead> <tbody> <tr> <td>Region name</td> <td>Florida</td> </tr> <tr> <td>Region ID</td> <td>usa_140002900</td> </tr> <tr> <td>Model</td> <td>coarse</td> </tr> <tr> <td>Version</td> <td>0_9_0</td> </tr> </tbody> </table> <p><strong>Statistics</strong></p><table> <thead><tr> <th>Name</th> <th>Value</th> </tr> </thead> <tbody> <tr> <td>Population</td> <td>18164740</td> </tr> <tr> <td>Average age</td> <td>40.0</td> </tr> <tr> <td>Households</td> <td>7364047</td> </tr> <tr> <td>Average household size</td> <td>2.5</td> </tr> <tr> <td>Residence locations</td> <td>7364047</td> </tr> <tr> <td>Activity locations</td> <td>1971854</td> </tr> <tr> <td>Average number of activities</td> <td>5.7</td> </tr> <tr> <td>Average travel distance</td> <td>72.6</td> </tr> </tbody> </table> <p><strong>Sources</strong></p><table> <thead><tr> <th>Description</th> <th>Name</th> <th>Version</th> <th>Url</th> </tr> </thead> <tbody> <tr> <td>Activity template data</td> <td>World Bank</td> <td>2021</td> <td><a href="https://data.worldbank.org">https://data.worldbank.org</a></td> </tr> <tr> <td>Administrative boundaries</td> <td>ADCW</td> <td>7.6</td> <td><a href="https://www.adci.com/adc-worldmap">https://www.adci.com/adc-worldmap</a></td> </tr> <tr> <td>Curated POIs based on OSM</td> <td>SLIPO/OSM POIs</td> <td></td> <td><a href="http://slipo.eu/?p=1551">http://slipo.eu/?p=1551</a> <a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a></td> </tr> <tr> <td>Household data</td> <td>IPUMS</td> <td></td> <td><a href="https://international.ipums.org/international">https://international.ipums.org/international</a></td> </tr> <tr> <td>Population count with demographic attributes</td> <td>GPW</td> <td>v4.11</td> <td><a href="https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11">https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11</a></td> </tr> </tbody> </table> <p><strong>Files description</strong></p><p><strong>Base data files (usa_140002900_data_v_0_9.zip)</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>usa_140002900_person_v_0_9.csv</code></td> <td>Data for each person including attributes such as age, gender, and household ID.</td> </tr> <tr> <td><code>usa_140002900_household_v_0_9.csv</code></td> <td>Data at household level.</td> </tr> <tr> <td><code>usa_140002900_residence_locations_v_0_9.csv</code></td> <td>Data about residence locations</td> </tr> <tr> <td><code>usa_140002900_activity_locations_v_0_9.csv</code></td> <td>Data about activity locations, including what activity types are supported at these locations</td> </tr> <tr> <td><code>usa_140002900_activity_location_assignment_v_0_9.csv</code></td> <td>For each person and for each of their activities, this file specifies the location where the activity takes place</td> </tr> </tbody> </table> <p><strong>Derived data files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>usa_140002900_contact_matrix_v_0_9.csv</code></td> <td>A POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model.</td> </tr> </tbody> </table> <p><strong>Validation and measures files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>usa_140002900_household_grouping_validation_v_0_9.pdf</code></td> <td>Validation plots for household construction</td> </tr> <tr> <td><code>usa_140002900_activity_durations_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of time spent on generated activities with survey data</td> </tr> <tr> <td><code>usa_140002900_activity_patterns_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of generated activity patterns by the time of day with survey data</td> </tr> <tr> <td><code>usa_140002900_location_construction_0_9.pdf</code></td> <td>Validation plots for location construction</td> </tr> <tr> <td><code>usa_140002900_location_assignement_0_9.pdf</code></td> <td>Validation plots for location assignment, including travel distribution plots</td> </tr> <tr> <td><code>usa_140002900_usa_140002900_ver_0_9_0_avg_travel_distance.pdf</code></td> <td>Choropleth map visualizing average travel distance</td> </tr> <tr> <td><code>usa_140002900_usa_140002900_ver_0_9_0_travel_distr_combined.pdf</code></td> <td>Travel distance distribution</td> </tr> <tr> <td><code>usa_140002900_usa_140002900_ver_0_9_0_num_activity_loc.pdf</code></td> <td>Choropleth map visualizing number of activity locations</td> </tr> <tr> <td><code>usa_140002900_usa_140002900_ver_0_9_0_avg_age.pdf</code></td> <td>Choropleth map visualizing average age</td> </tr> <tr> <td><code>usa_140002900_usa_140002900_ver_0_9_0_pop_density_per_sqkm.pdf</code></td> <td>Choropleth map visualizing population density</td> </tr> <tr> <td><code>usa_140002900_usa_140002900_ver_0_9_0_pop_size.pdf</code></td> <td>Choropleth map visualizing population size</td> </tr> </tbody> </table>
Synthetic population for USA_NEWMEXICO
<p><strong>Synthetic populations for regions of the World (SPW) | New Mexico</strong></p><p><strong>Dataset information</strong></p><p>A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).</p> <p><strong>License</strong></p><p><a href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0</a></p> <p><strong>Acknowledgment</strong></p><p>This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).</p> <p><strong>Contact information</strong></p><p>Henning.Mortveit@virginia.edu</p> <p><strong>Identifiers</strong></p><table> <thead><tr> <th></th> <th></th> </tr> </thead> <tbody> <tr> <td>Region name</td> <td>New Mexico</td> </tr> <tr> <td>Region ID</td> <td>usa_140002890</td> </tr> <tr> <td>Model</td> <td>coarse</td> </tr> <tr> <td>Version</td> <td>0_9_0</td> </tr> </tbody> </table> <p><strong>Statistics</strong></p><table> <thead><tr> <th>Name</th> <th>Value</th> </tr> </thead> <tbody> <tr> <td>Population</td> <td>2056390</td> </tr> <tr> <td>Average age</td> <td>37.2</td> </tr> <tr> <td>Households</td> <td>833667</td> </tr> <tr> <td>Average household size</td> <td>2.5</td> </tr> <tr> <td>Residence locations</td> <td>833667</td> </tr> <tr> <td>Activity locations</td> <td>160317</td> </tr> <tr> <td>Average number of activities</td> <td>5.7</td> </tr> <tr> <td>Average travel distance</td> <td>76.2</td> </tr> </tbody> </table> <p><strong>Sources</strong></p><table> <thead><tr> <th>Description</th> <th>Name</th> <th>Version</th> <th>Url</th> </tr> </thead> <tbody> <tr> <td>Activity template data</td> <td>World Bank</td> <td>2021</td> <td><a href="https://data.worldbank.org">https://data.worldbank.org</a></td> </tr> <tr> <td>Administrative boundaries</td> <td>ADCW</td> <td>7.6</td> <td><a href="https://www.adci.com/adc-worldmap">https://www.adci.com/adc-worldmap</a></td> </tr> <tr> <td>Curated POIs based on OSM</td> <td>SLIPO/OSM POIs</td> <td></td> <td><a href="http://slipo.eu/?p=1551">http://slipo.eu/?p=1551</a> <a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a></td> </tr> <tr> <td>Household data</td> <td>IPUMS</td> <td></td> <td><a href="https://international.ipums.org/international">https://international.ipums.org/international</a></td> </tr> <tr> <td>Population count with demographic attributes</td> <td>GPW</td> <td>v4.11</td> <td><a href="https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11">https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11</a></td> </tr> </tbody> </table> <p><strong>Files description</strong></p><p><strong>Base data files (usa_140002890_data_v_0_9.zip)</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>usa_140002890_person_v_0_9.csv</code></td> <td>Data for each person including attributes such as age, gender, and household ID.</td> </tr> <tr> <td><code>usa_140002890_household_v_0_9.csv</code></td> <td>Data at household level.</td> </tr> <tr> <td><code>usa_140002890_residence_locations_v_0_9.csv</code></td> <td>Data about residence locations</td> </tr> <tr> <td><code>usa_140002890_activity_locations_v_0_9.csv</code></td> <td>Data about activity locations, including what activity types are supported at these locations</td> </tr> <tr> <td><code>usa_140002890_activity_location_assignment_v_0_9.csv</code></td> <td>For each person and for each of their activities, this file specifies the location where the activity takes place</td> </tr> </tbody> </table> <p><strong>Derived data files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>usa_140002890_contact_matrix_v_0_9.csv</code></td> <td>A POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model.</td> </tr> </tbody> </table> <p><strong>Validation and measures files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>usa_140002890_household_grouping_validation_v_0_9.pdf</code></td> <td>Validation plots for household construction</td> </tr> <tr> <td><code>usa_140002890_activity_durations_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of time spent on generated activities with survey data</td> </tr> <tr> <td><code>usa_140002890_activity_patterns_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of generated activity patterns by the time of day with survey data</td> </tr> <tr> <td><code>usa_140002890_location_construction_0_9.pdf</code></td> <td>Validation plots for location construction</td> </tr> <tr> <td><code>usa_140002890_location_assignement_0_9.pdf</code></td> <td>Validation plots for location assignment, including travel distribution plots</td> </tr> <tr> <td><code>usa_140002890_usa_140002890_ver_0_9_0_avg_travel_distance.pdf</code></td> <td>Choropleth map visualizing average travel distance</td> </tr> <tr> <td><code>usa_140002890_usa_140002890_ver_0_9_0_travel_distr_combined.pdf</code></td> <td>Travel distance distribution</td> </tr> <tr> <td><code>usa_140002890_usa_140002890_ver_0_9_0_num_activity_loc.pdf</code></td> <td>Choropleth map visualizing number of activity locations</td> </tr> <tr> <td><code>usa_140002890_usa_140002890_ver_0_9_0_avg_age.pdf</code></td> <td>Choropleth map visualizing average age</td> </tr> <tr> <td><code>usa_140002890_usa_140002890_ver_0_9_0_pop_density_per_sqkm.pdf</code></td> <td>Choropleth map visualizing population density</td> </tr> <tr> <td><code>usa_140002890_usa_140002890_ver_0_9_0_pop_size.pdf</code></td> <td>Choropleth map visualizing population size</td> </tr> </tbody> </table>
Synthetic population for USA_ALABAMA
<p><strong>Synthetic populations for regions of the World (SPW) | Alabama</strong></p><p><strong>Dataset information</strong></p><p>A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).</p> <p><strong>License</strong></p><p><a href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0</a></p> <p><strong>Acknowledgment</strong></p><p>This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).</p> <p><strong>Contact information</strong></p><p>Henning.Mortveit@virginia.edu</p> <p><strong>Identifiers</strong></p><table> <thead><tr> <th></th> <th></th> </tr> </thead> <tbody> <tr> <td>Region name</td> <td>Alabama</td> </tr> <tr> <td>Region ID</td> <td>usa_140002904</td> </tr> <tr> <td>Model</td> <td>coarse</td> </tr> <tr> <td>Version</td> <td>0_9_0</td> </tr> </tbody> </table> <p><strong>Statistics</strong></p><table> <thead><tr> <th>Name</th> <th>Value</th> </tr> </thead> <tbody> <tr> <td>Population</td> <td>4768478</td> </tr> <tr> <td>Average age</td> <td>37.8</td> </tr> <tr> <td>Households</td> <td>1933164</td> </tr> <tr> <td>Average household size</td> <td>2.5</td> </tr> <tr> <td>Residence locations</td> <td>1933164</td> </tr> <tr> <td>Activity locations</td> <td>398709</td> </tr> <tr> <td>Average number of activities</td> <td>5.7</td> </tr> <tr> <td>Average travel distance</td> <td>65.0</td> </tr> </tbody> </table> <p><strong>Sources</strong></p><table> <thead><tr> <th>Description</th> <th>Name</th> <th>Version</th> <th>Url</th> </tr> </thead> <tbody> <tr> <td>Activity template data</td> <td>World Bank</td> <td>2021</td> <td><a href="https://data.worldbank.org">https://data.worldbank.org</a></td> </tr> <tr> <td>Administrative boundaries</td> <td>ADCW</td> <td>7.6</td> <td><a href="https://www.adci.com/adc-worldmap">https://www.adci.com/adc-worldmap</a></td> </tr> <tr> <td>Curated POIs based on OSM</td> <td>SLIPO/OSM POIs</td> <td></td> <td><a href="http://slipo.eu/?p=1551">http://slipo.eu/?p=1551</a> <a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a></td> </tr> <tr> <td>Household data</td> <td>IPUMS</td> <td></td> <td><a href="https://international.ipums.org/international">https://international.ipums.org/international</a></td> </tr> <tr> <td>Population count with demographic attributes</td> <td>GPW</td> <td>v4.11</td> <td><a href="https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11">https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11</a></td> </tr> </tbody> </table> <p><strong>Files description</strong></p><p><strong>Base data files (usa_140002904_data_v_0_9.zip)</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>usa_140002904_person_v_0_9.csv</code></td> <td>Data for each person including attributes such as age, gender, and household ID.</td> </tr> <tr> <td><code>usa_140002904_household_v_0_9.csv</code></td> <td>Data at household level.</td> </tr> <tr> <td><code>usa_140002904_residence_locations_v_0_9.csv</code></td> <td>Data about residence locations</td> </tr> <tr> <td><code>usa_140002904_activity_locations_v_0_9.csv</code></td> <td>Data about activity locations, including what activity types are supported at these locations</td> </tr> <tr> <td><code>usa_140002904_activity_location_assignment_v_0_9.csv</code></td> <td>For each person and for each of their activities, this file specifies the location where the activity takes place</td> </tr> </tbody> </table> <p><strong>Derived data files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>usa_140002904_contact_matrix_v_0_9.csv</code></td> <td>A POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model.</td> </tr> </tbody> </table> <p><strong>Validation and measures files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>usa_140002904_household_grouping_validation_v_0_9.pdf</code></td> <td>Validation plots for household construction</td> </tr> <tr> <td><code>usa_140002904_activity_durations_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of time spent on generated activities with survey data</td> </tr> <tr> <td><code>usa_140002904_activity_patterns_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of generated activity patterns by the time of day with survey data</td> </tr> <tr> <td><code>usa_140002904_location_construction_0_9.pdf</code></td> <td>Validation plots for location construction</td> </tr> <tr> <td><code>usa_140002904_location_assignement_0_9.pdf</code></td> <td>Validation plots for location assignment, including travel distribution plots</td> </tr> <tr> <td><code>usa_140002904_usa_140002904_ver_0_9_0_avg_travel_distance.pdf</code></td> <td>Choropleth map visualizing average travel distance</td> </tr> <tr> <td><code>usa_140002904_usa_140002904_ver_0_9_0_travel_distr_combined.pdf</code></td> <td>Travel distance distribution</td> </tr> <tr> <td><code>usa_140002904_usa_140002904_ver_0_9_0_num_activity_loc.pdf</code></td> <td>Choropleth map visualizing number of activity locations</td> </tr> <tr> <td><code>usa_140002904_usa_140002904_ver_0_9_0_avg_age.pdf</code></td> <td>Choropleth map visualizing average age</td> </tr> <tr> <td><code>usa_140002904_usa_140002904_ver_0_9_0_pop_density_per_sqkm.pdf</code></td> <td>Choropleth map visualizing population density</td> </tr> <tr> <td><code>usa_140002904_usa_140002904_ver_0_9_0_pop_size.pdf</code></td> <td>Choropleth map visualizing population size</td> </tr> </tbody> </table>
Synthetic population for USA_WASHINGTON
<p><strong>Synthetic populations for regions of the World (SPW) | Washington</strong></p><p><strong>Dataset information</strong></p><p>A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).</p> <p><strong>License</strong></p><p><a href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0</a></p> <p><strong>Acknowledgment</strong></p><p>This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).</p> <p><strong>Contact information</strong></p><p>Henning.Mortveit@virginia.edu</p> <p><strong>Identifiers</strong></p><table> <thead><tr> <th></th> <th></th> </tr> </thead> <tbody> <tr> <td>Region name</td> <td>Washington</td> </tr> <tr> <td>Region ID</td> <td>usa_140002872</td> </tr> <tr> <td>Model</td> <td>coarse</td> </tr> <tr> <td>Version</td> <td>0_9_0</td> </tr> </tbody> </table> <p><strong>Statistics</strong></p><table> <thead><tr> <th>Name</th> <th>Value</th> </tr> </thead> <tbody> <tr> <td>Population</td> <td>6618311</td> </tr> <tr> <td>Average age</td> <td>37.3</td> </tr> <tr> <td>Households</td> <td>2683070</td> </tr> <tr> <td>Average household size</td> <td>2.5</td> </tr> <tr> <td>Residence locations</td> <td>2683070</td> </tr> <tr> <td>Activity locations</td> <td>651472</td> </tr> <tr> <td>Average number of activities</td> <td>5.7</td> </tr> <tr> <td>Average travel distance</td> <td>72.5</td> </tr> </tbody> </table> <p><strong>Sources</strong></p><table> <thead><tr> <th>Description</th> <th>Name</th> <th>Version</th> <th>Url</th> </tr> </thead> <tbody> <tr> <td>Activity template data</td> <td>World Bank</td> <td>2021</td> <td><a href="https://data.worldbank.org">https://data.worldbank.org</a></td> </tr> <tr> <td>Administrative boundaries</td> <td>ADCW</td> <td>7.6</td> <td><a href="https://www.adci.com/adc-worldmap">https://www.adci.com/adc-worldmap</a></td> </tr> <tr> <td>Curated POIs based on OSM</td> <td>SLIPO/OSM POIs</td> <td></td> <td><a href="http://slipo.eu/?p=1551">http://slipo.eu/?p=1551</a> <a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a></td> </tr> <tr> <td>Household data</td> <td>IPUMS</td> <td></td> <td><a href="https://international.ipums.org/international">https://international.ipums.org/international</a></td> </tr> <tr> <td>Population count with demographic attributes</td> <td>GPW</td> <td>v4.11</td> <td><a href="https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11">https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11</a></td> </tr> </tbody> </table> <p><strong>Files description</strong></p><p><strong>Base data files (usa_140002872_data_v_0_9.zip)</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>usa_140002872_person_v_0_9.csv</code></td> <td>Data for each person including attributes such as age, gender, and household ID.</td> </tr> <tr> <td><code>usa_140002872_household_v_0_9.csv</code></td> <td>Data at household level.</td> </tr> <tr> <td><code>usa_140002872_residence_locations_v_0_9.csv</code></td> <td>Data about residence locations</td> </tr> <tr> <td><code>usa_140002872_activity_locations_v_0_9.csv</code></td> <td>Data about activity locations, including what activity types are supported at these locations</td> </tr> <tr> <td><code>usa_140002872_activity_location_assignment_v_0_9.csv</code></td> <td>For each person and for each of their activities, this file specifies the location where the activity takes place</td> </tr> </tbody> </table> <p><strong>Derived data files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>usa_140002872_contact_matrix_v_0_9.csv</code></td> <td>A POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model.</td> </tr> </tbody> </table> <p><strong>Validation and measures files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>usa_140002872_household_grouping_validation_v_0_9.pdf</code></td> <td>Validation plots for household construction</td> </tr> <tr> <td><code>usa_140002872_activity_durations_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of time spent on generated activities with survey data</td> </tr> <tr> <td><code>usa_140002872_activity_patterns_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of generated activity patterns by the time of day with survey data</td> </tr> <tr> <td><code>usa_140002872_location_construction_0_9.pdf</code></td> <td>Validation plots for location construction</td> </tr> <tr> <td><code>usa_140002872_location_assignement_0_9.pdf</code></td> <td>Validation plots for location assignment, including travel distribution plots</td> </tr> <tr> <td><code>usa_140002872_usa_140002872_ver_0_9_0_avg_travel_distance.pdf</code></td> <td>Choropleth map visualizing average travel distance</td> </tr> <tr> <td><code>usa_140002872_usa_140002872_ver_0_9_0_travel_distr_combined.pdf</code></td> <td>Travel distance distribution</td> </tr> <tr> <td><code>usa_140002872_usa_140002872_ver_0_9_0_num_activity_loc.pdf</code></td> <td>Choropleth map visualizing number of activity locations</td> </tr> <tr> <td><code>usa_140002872_usa_140002872_ver_0_9_0_avg_age.pdf</code></td> <td>Choropleth map visualizing average age</td> </tr> <tr> <td><code>usa_140002872_usa_140002872_ver_0_9_0_pop_density_per_sqkm.pdf</code></td> <td>Choropleth map visualizing population density</td> </tr> <tr> <td><code>usa_140002872_usa_140002872_ver_0_9_0_pop_size.pdf</code></td> <td>Choropleth map visualizing population size</td> </tr> </tbody> </table>
Synthetic population for IND_DELHI
<p><strong>Synthetic populations for regions of the World (SPW) | Delhi</strong></p><p><strong>Dataset information</strong></p><p>A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).</p> <p><strong>License</strong></p><p><a href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0</a></p> <p><strong>Acknowledgment</strong></p><p>This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).</p> <p><strong>Contact information</strong></p><p>Henning.Mortveit@virginia.edu</p> <p><strong>Identifiers</strong></p><table> <thead><tr> <th></th> <th></th> </tr> </thead> <tbody> <tr> <td>Region name</td> <td>Delhi</td> </tr> <tr> <td>Region ID</td> <td>ind_140001944</td> </tr> <tr> <td>Model</td> <td>coarse</td> </tr> <tr> <td>Version</td> <td>0_9_0</td> </tr> </tbody> </table> <p><strong>Statistics</strong></p><table> <thead><tr> <th>Name</th> <th>Value</th> </tr> </thead> <tbody> <tr> <td>Population</td> <td>15951510</td> </tr> <tr> <td>Average age</td> <td>28.2</td> </tr> <tr> <td>Households</td> <td>3625935</td> </tr> <tr> <td>Average household size</td> <td>4.4</td> </tr> <tr> <td>Residence locations</td> <td>3625935</td> </tr> <tr> <td>Activity locations</td> <td>1309377</td> </tr> <tr> <td>Average number of activities</td> <td>5.5</td> </tr> <tr> <td>Average travel distance</td> <td>26.6</td> </tr> </tbody> </table> <p><strong>Sources</strong></p><table> <thead><tr> <th>Description</th> <th>Name</th> <th>Version</th> <th>Url</th> </tr> </thead> <tbody> <tr> <td>Activity template data</td> <td>World Bank</td> <td>2021</td> <td><a href="https://data.worldbank.org">https://data.worldbank.org</a></td> </tr> <tr> <td>Administrative boundaries</td> <td>ADCW</td> <td>7.6</td> <td><a href="https://www.adci.com/adc-worldmap">https://www.adci.com/adc-worldmap</a></td> </tr> <tr> <td>Curated POIs based on OSM</td> <td>SLIPO/OSM POIs</td> <td></td> <td><a href="http://slipo.eu/?p=1551">http://slipo.eu/?p=1551</a> <a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a></td> </tr> <tr> <td>Household data</td> <td>DHS</td> <td></td> <td><a href="https://dhsprogram.com">https://dhsprogram.com</a></td> </tr> <tr> <td>Population count with demographic attributes</td> <td>GPW</td> <td>v4.11</td> <td><a href="https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11">https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11</a></td> </tr> </tbody> </table> <p><strong>Files description</strong></p><p><strong>Base data files (ind_140001944_data_v_0_9.zip)</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>ind_140001944_person_v_0_9.csv</code></td> <td>Data for each person including attributes such as age, gender, and household ID.</td> </tr> <tr> <td><code>ind_140001944_household_v_0_9.csv</code></td> <td>Data at household level.</td> </tr> <tr> <td><code>ind_140001944_residence_locations_v_0_9.csv</code></td> <td>Data about residence locations</td> </tr> <tr> <td><code>ind_140001944_activity_locations_v_0_9.csv</code></td> <td>Data about activity locations, including what activity types are supported at these locations</td> </tr> <tr> <td><code>ind_140001944_activity_location_assignment_v_0_9.csv</code></td> <td>For each person and for each of their activities, this file specifies the location where the activity takes place</td> </tr> </tbody> </table> <p><strong>Derived data files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>ind_140001944_contact_matrix_v_0_9.csv</code></td> <td>A POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model.</td> </tr> </tbody> </table> <p><strong>Validation and measures files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>ind_140001944_household_grouping_validation_v_0_9.pdf</code></td> <td>Validation plots for household construction</td> </tr> <tr> <td><code>ind_140001944_activity_durations_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of time spent on generated activities with survey data</td> </tr> <tr> <td><code>ind_140001944_activity_patterns_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of generated activity patterns by the time of day with survey data</td> </tr> <tr> <td><code>ind_140001944_location_construction_0_9.pdf</code></td> <td>Validation plots for location construction</td> </tr> <tr> <td><code>ind_140001944_location_assignement_0_9.pdf</code></td> <td>Validation plots for location assignment, including travel distribution plots</td> </tr> <tr> <td><code>ind_140001944_ind_140001944_ver_0_9_0_avg_travel_distance.pdf</code></td> <td>Choropleth map visualizing average travel distance</td> </tr> <tr> <td><code>ind_140001944_ind_140001944_ver_0_9_0_travel_distr_combined.pdf</code></td> <td>Travel distance distribution</td> </tr> <tr> <td><code>ind_140001944_ind_140001944_ver_0_9_0_num_activity_loc.pdf</code></td> <td>Choropleth map visualizing number of activity locations</td> </tr> <tr> <td><code>ind_140001944_ind_140001944_ver_0_9_0_avg_age.pdf</code></td> <td>Choropleth map visualizing average age</td> </tr> <tr> <td><code>ind_140001944_ind_140001944_ver_0_9_0_pop_density_per_sqkm.pdf</code></td> <td>Choropleth map visualizing population density</td> </tr> <tr> <td><code>ind_140001944_ind_140001944_ver_0_9_0_pop_size.pdf</code></td> <td>Choropleth map visualizing population size</td> </tr> </tbody> </table>
Synthetic population for IND_PUNJAB
<p><strong>Synthetic populations for regions of the World (SPW) | Punjab</strong></p><p><strong>Dataset information</strong></p><p>A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).</p> <p><strong>License</strong></p><p><a href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0</a></p> <p><strong>Acknowledgment</strong></p><p>This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).</p> <p><strong>Contact information</strong></p><p>Henning.Mortveit@virginia.edu</p> <p><strong>Identifiers</strong></p><table> <thead><tr> <th></th> <th></th> </tr> </thead> <tbody> <tr> <td>Region name</td> <td>Punjab</td> </tr> <tr> <td>Region ID</td> <td>ind_140001934</td> </tr> <tr> <td>Model</td> <td>coarse</td> </tr> <tr> <td>Version</td> <td>0_9_0</td> </tr> </tbody> </table> <p><strong>Statistics</strong></p><table> <thead><tr> <th>Name</th> <th>Value</th> </tr> </thead> <tbody> <tr> <td>Population</td> <td>27875875</td> </tr> <tr> <td>Average age</td> <td>30.2</td> </tr> <tr> <td>Households</td> <td>6337346</td> </tr> <tr> <td>Average household size</td> <td>4.4</td> </tr> <tr> <td>Residence locations</td> <td>6337346</td> </tr> <tr> <td>Activity locations</td> <td>1608398</td> </tr> <tr> <td>Average number of activities</td> <td>5.5</td> </tr> <tr> <td>Average travel distance</td> <td>101.9</td> </tr> </tbody> </table> <p><strong>Sources</strong></p><table> <thead><tr> <th>Description</th> <th>Name</th> <th>Version</th> <th>Url</th> </tr> </thead> <tbody> <tr> <td>Activity template data</td> <td>World Bank</td> <td>2021</td> <td><a href="https://data.worldbank.org">https://data.worldbank.org</a></td> </tr> <tr> <td>Administrative boundaries</td> <td>ADCW</td> <td>7.6</td> <td><a href="https://www.adci.com/adc-worldmap">https://www.adci.com/adc-worldmap</a></td> </tr> <tr> <td>Curated POIs based on OSM</td> <td>SLIPO/OSM POIs</td> <td></td> <td><a href="http://slipo.eu/?p=1551">http://slipo.eu/?p=1551</a> <a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a></td> </tr> <tr> <td>Household data</td> <td>DHS</td> <td></td> <td><a href="https://dhsprogram.com">https://dhsprogram.com</a></td> </tr> <tr> <td>Population count with demographic attributes</td> <td>GPW</td> <td>v4.11</td> <td><a href="https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11">https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11</a></td> </tr> </tbody> </table> <p><strong>Files description</strong></p><p><strong>Base data files (ind_140001934_data_v_0_9.zip)</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>ind_140001934_person_v_0_9.csv</code></td> <td>Data for each person including attributes such as age, gender, and household ID.</td> </tr> <tr> <td><code>ind_140001934_household_v_0_9.csv</code></td> <td>Data at household level.</td> </tr> <tr> <td><code>ind_140001934_residence_locations_v_0_9.csv</code></td> <td>Data about residence locations</td> </tr> <tr> <td><code>ind_140001934_activity_locations_v_0_9.csv</code></td> <td>Data about activity locations, including what activity types are supported at these locations</td> </tr> <tr> <td><code>ind_140001934_activity_location_assignment_v_0_9.csv</code></td> <td>For each person and for each of their activities, this file specifies the location where the activity takes place</td> </tr> </tbody> </table> <p><strong>Derived data files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>ind_140001934_contact_matrix_v_0_9.csv</code></td> <td>A POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model.</td> </tr> </tbody> </table> <p><strong>Validation and measures files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>ind_140001934_household_grouping_validation_v_0_9.pdf</code></td> <td>Validation plots for household construction</td> </tr> <tr> <td><code>ind_140001934_activity_durations_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of time spent on generated activities with survey data</td> </tr> <tr> <td><code>ind_140001934_activity_patterns_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of generated activity patterns by the time of day with survey data</td> </tr> <tr> <td><code>ind_140001934_location_construction_0_9.pdf</code></td> <td>Validation plots for location construction</td> </tr> <tr> <td><code>ind_140001934_location_assignement_0_9.pdf</code></td> <td>Validation plots for location assignment, including travel distribution plots</td> </tr> <tr> <td><code>ind_140001934_ind_140001934_ver_0_9_0_avg_travel_distance.pdf</code></td> <td>Choropleth map visualizing average travel distance</td> </tr> <tr> <td><code>ind_140001934_ind_140001934_ver_0_9_0_travel_distr_combined.pdf</code></td> <td>Travel distance distribution</td> </tr> <tr> <td><code>ind_140001934_ind_140001934_ver_0_9_0_num_activity_loc.pdf</code></td> <td>Choropleth map visualizing number of activity locations</td> </tr> <tr> <td><code>ind_140001934_ind_140001934_ver_0_9_0_avg_age.pdf</code></td> <td>Choropleth map visualizing average age</td> </tr> <tr> <td><code>ind_140001934_ind_140001934_ver_0_9_0_pop_density_per_sqkm.pdf</code></td> <td>Choropleth map visualizing population density</td> </tr> <tr> <td><code>ind_140001934_ind_140001934_ver_0_9_0_pop_size.pdf</code></td> <td>Choropleth map visualizing population size</td> </tr> </tbody> </table>
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