Traditional Census Research Articles

Use of environmental DNA in assessment of fish functional and phylogenetic diversity.

Assessing the impact of global changes and protection effectiveness is a key step in monitoring marine fishes. Most traditional census methods are demanding or destructive. Nondisturbing and nonlethal approaches based on video and environmental DNA are alternatives to underwater visual census or fishing. However, their ability to detect multiple biodiversity factors beyond traditional taxonomic diversity is still unknown. For bony fishes and elasmobranchs, we compared the performance of eDNA metabarcoding and long-term remote video to assess species' phylogenetic and functional diversity. We used 10 eDNA samples from 30 L of water each and 25 hr of underwater videos over 4 days on Malpelo Island (pacific coast of Colombia), a remote marine protected area. Metabarcoding of eDNA detected 66% more molecular operational taxonomic units (MOTUs) than species on video. We found 66 and 43 functional entities with a single eDNA marker and videos, respectively, and higher functional richness for eDNA than videos. Despite gaps in genetic reference databases, eDNA also detected a higher fish phylogenetic diversity than videos; accumulation curves showed how 1 eDNA transect detected as much phylogenetic diversity as 25 hr of video. Environmental DNA metabarcoding can be used to affordably, efficiently, and accurately census biodiversity factors in marine systems. Although taxonomic assignments are still limited by species coverage in genetic reference databases, use of MOTUs highlights the potential of eDNA metabarcoding once reference databases have expanded.

Multispecies detection and identification of African mammals in aerial imagery using convolutional neural networks

AbstractSurvey and monitoring of wildlife populations are among the key elements in nature conservation. The use of unmanned aerial vehicles and light aircrafts as aerial image acquisition systems is growing, as they are cheaper alternatives to traditional census methods. However, the manual localization and identification of species within imagery can be time‐consuming and complex. Object detection algorithms, based on convolutional neural networks (CNNs), have shown a good capacity for animal detection. Nevertheless, most of the work has focused on binary detection cases (animal vs. background). The main objective of this study is to compare three recent detection algorithms to detect and identify African mammal species based on high‐resolution aerial images. We evaluated the performance of three multi‐class CNN algorithms: Faster‐RCNN, Libra‐RCNN and RetinaNet. Six species were targeted: topis (Damaliscus lunatus jimela), buffalos (Syncerus caffer), elephants (Loxodonta africana), kobs (Kobus kob), warthogs (Phacochoerus africanus) and waterbucks (Kobus ellipsiprymnus). The best model was then applied to a case study using an independent dataset. The best model was the Libra‐RCNN, with the best mean average precision (0.80 ± 0.02), the lowest degree of interspecies confusion (3.5 ± 1.4%) and the lowest false positive per true positive ratio (1.7 ± 0.2) on the test set. This model was able to detect and correctly identify 73% of all individuals (1115), find 43 individuals of species other than those targeted and detect 84 missed individuals on our independent UAV dataset, with an average processing speed of 12 s/image. This model showed better detection performance than previous studies dealing with similar habitats. It was able to differentiate six animal species in nadir aerial images. Although limitations were observed with warthog identification and individual detection in herds, this model can save time and can perform precise surveys in open savanna.

Optimization of Modelling Population Density Estimation Based on Impervious Surfaces

Population data are key indicators of policymaking, public health, and land use in urban and ecological systems; however, traditional censuses are time-consuming, expensive, and laborious. This study proposes a method of modelling population density estimations based on remote sensing data in Hefei. Four models with impervious surface (IS), night light (NTL), and point of interest (POI) data as independent variables are constructed at the township scale, and the optimal model was applied to pixels to obtain a finer population density distribution. The results show that: (1) impervious surface (IS) data can be effectively extracted by the linear spectral mixture analysis (LSMA) method; (2) there is a high potential of the multi-variable model to estimate the population density, with an adjusted R2 of 0.832, and mean absolute error (MAE) of 0.420 from 10-fold cross validation recorded; (3) downscaling the predicted population density from the township scale to pixels using the multi-variable stepwise regression model achieves a more refined population density distribution. This study provides a promising method for the rapid and effective prediction of population data in interval years, and data support for urban planning and population management.

Stereo Matching Algorithm Based on HSV Color Space and Improved Census Transform

Aiming at the problem that stereo matching accuracy is easily affected by noise and amplitude distortion, a stereo matching algorithm based on HSV color space and improved census transform is proposed. In the cost calculation stage, the color image is first converted from RGB space to HSV space; moreover, the hue channel is used as the matching primitive to establish the hue absolute difference (HAD) cost calculation function, which reduces the amount of calculation and enhances the robustness of matching. Then, to solve the problem of the traditional census transform overrelying on the central pixel and to improve the noise resistance of the algorithm, an improved census method based on neighborhood weighting is also proposed. Finally, the HAD cost and the improved census cost are nonlinearly fused as the initial cost. In the aggregation stage, an outlier elimination method based on confidence interval is proposed. By calculating the confidence interval of the aggregation window, this paper eliminates the cost value that is not in the confidence interval and subsequently filters as well as aggregates the remaining costs to further reduce the noise interference and improve the matching accuracy. Experiments show that the proposed method can not only effectively suppress the influence of noise, but also achieve a more robust matching effect in scenes with changing exposure and lighting conditions.

Monitoring streets through tweets: Using user-generated geographic information to predict gentrification and displacement

The new availability of big data sources provides an opportunity to revisit our ability to predict neighborhood change. This article explores how data on urban activity patterns, specifically, geotagged tweets, improve the understanding of one type of neighborhood change—gentrification—by identifying dynamic connections between neighborhoods and across scales. We first develop a typology of neighborhood change and risk of gentrification from 1990 to 2015 for the San Francisco Bay Area based on conventional demographic data from the Census. Then, we use multivariate regression to analyze geotagged tweets from 2012 to 2015, finding that outsiders are significantly more likely to visit neighborhoods currently undergoing gentrification. Using the factors that best predict gentrification, we identify a subset of neighborhoods that Twitter-based activity suggests are at risk for gentrification over the short term—but are not identified by analysis with traditional census data. The findings suggest that combining Census and social media data can provide new insights on gentrification such as augmenting our ability to identify that processes of change are underway. This blended approach, using Census and big data, can help policymakers implement and target policies that preserve housing affordability and protext tenants more effectively.

Perceived influence of street-level visible greenness exposure in the work and residential environment on life satisfaction: Evidence from Beijing, China

Sensing urban greenness from street view data offers a new and alternative way of measuring the association between greenness exposure and subjective wellbeing in developing countries where traditional census data are poor. This paper focuses on the association between life satisfaction and street-level visible greenness exposure at residential and work locations, using a combination of sensor data and individual cross-sectional survey data (4619 employed respondents) in Beijing, China. We use a single self-reported question (‘“how well are you satisfied with your life as a whole”’) to measure life satisfaction. Street View Greenness (SVG) is taken as a surrogate for street-level visible greenness exposure at residential and work locations. The results suggest that street-level visible greenness exposure in residential locations is positively associated with perceived satisfaction, though such effects are less significant after considering greenness exposure at work locations. The stratified analysis provides the insight that the associations between street-level visible greenness exposure and life satisfaction vary with individual demographic and socioeconomic characteristics such as sex, age educational attainment and income. Males, young adults, people with low income and educational attainment may benefit more from SVG exposure than other groups. Findings of this study suggest that urban greenness in residential and work environments should simultaneously be taken into the design of land use and public policies aiming to improve people’s subjective wellbeing.

How Does Administrative Data Compare with Census Data for the Measurement of Internal Migration? The Case of Northern Ireland, 2001\u20132011

The paper compares and contrasts internal migration measured by healthcard-based administrative data with census figures. This is useful because the collection of population data, its processing, and its dissemination by statistical agencies is becoming more reliant on administrative data. Statistical agencies already use healthcard data to make migration estimates and are increasingly confident about local population estimates from administrative sources. This analysis goes further than this work as it assesses how far healthcard data can produce reliable data products of the kind to which academics are accustomed. It does this by examining migration events versus transitions over a full intercensal period; population flows into and out of small areas; and the extent to which it produces microdata on migration equivalent to that in the census. It is shown that for most demographic groups and places healthcard data is an adequate substitute for census-based migration counts, the exceptions being for student households and younger people. However, census-like information is still needed to provide covariates for analysis and this will still be required whatever the future of the traditional census.

Population and Housing Censuses: An overdue and old-fashioned instrument or still a modern, severely needed and steadfast tool?

Following the release of the special issue of the Statistical Journal of the IAOS on Population and Housing Census, a platform was opened to discuss several topics related to the census, such as the definition of the census, the methodologies and technologies used to conduct a population and housing census. In particular, in a context of increasing access to administrative data, the relevance of the traditional census was discussed, in regard to its huge cost. Finally, the impact of the Covid-19 pandemic on the censuses planed for the years 2020 and 2021 was raised as monitored by international organisations.

Estimating Population Counts with Capture-Recapture Models in the Context of Erroneous Records in Linked Administrative Data

In the absence of a traditional census and a comprehensive population register (as it is thecase in the UK), administrative data sources, i.e. health, school or tax records, can offer analternative to estimate the size of residence population. However, such data sources are designedto capture information only from specific populations which imposes a challenge to theestimation. A suitable method to overcome the challenge is to link administrative data sourcesthat collect information from different but overlapping populations and use capture-recapturemodels to estimate the population counts. There are various assumptions required to obtainunbiased estimates by using capture-recapture models. In practice, especially the assumptionson ‘homogeneous inclusion probabilities’ and ‘no over coverage’ are often not met. This paperproposes a two-step procedure for estimating population counts with capture-recapture modelsthat account for heterogeneity of inclusion probabilities and the over coverage in the datasources. We apply our methodology to the linked Patient Register and Customer InformationSystem dataset which violates both of the aforementioned assumptions. The Patient Registerincludes people who are registered with a National Health Service General Practitioner doctor.In 2011, the Patient Register overestimated the size of England and Wales populationby 4.3% (over coverage) and its sex ratio was different than the 2011 Census estimates (heterogeneousof inclusion probabilities). The Customer Information System dataset providesinformation on all individuals who have ever had a national insurance number and childrenwhose parents have made a child benefit claim relating to them. In 2011, it over estimatedthe size of England and Wales population by 9.5% and its age and sex structure was differentthan the 2011 Census estimates. Applying our approach, we estimate population counts ofthe South East region of England by age, sex and local authority, and compare them withcensus estimates using percentage difference maps.

Detecting Submerged Objects Using Active Acoustics and Deep Neural Networks: A Test Case for Pelagic Fish

The accurate detection and quantification of submerged targets has been recognized as a key challenge in marine exploration, one that traditional census approaches cannot handle efficiently. Here we present a deep learning approach to detect the pattern of a moving fish from the reflections of an active acoustic emitter. To allow for real-time detection, we use a convolutional neural network, which provides the simultaneous labeling of a large buffer of signal samples. This allows to capture the structure of the reflecting signal from the moving target and to separate it from clutter reflections. We evaluate system performance both on synthetic (simulated) data, as well as on real data recorded over 50 sea experiments in a variety of sea conditions. When tested on real signals, the network trained on simulated patterns showed non-trivial detection capabilities, suggesting that transfer learning can be a viable approach in these scenarios, where tagged data is often lacking. However, training the network directly on the real reflections with data augmentation techniques allowed to reach a more favorable precision-recall trade-off, approaching an ideal detection bound. We also evaluate an alternative model based on recurrent neural networks which, despite exhibiting slightly inferior performance, could be applied in scenarios requiring on-line processing of the reflection sequence.

A distance sampling survey of the Critically Endangered Straw-headed BulbulPycnonotus zeylanicusin Singapore

SummaryThe Straw-headed BulbulPycnonotus zeylanicusis one of South-East Asia’s most threatened songbirds due to relentless demand for the regional cage-bird trade. The species was recently uplisted from ‘Endangered’ to ‘Critically Endangered’ only two years after its previous uplisting. Intriguingly, populations in highly urbanised Singapore appear relatively secure. However, the last Singaporean density estimates, derived from traditional census methods, were obtained nearly two decades ago in 2001. A recent population estimate in 2016 was derived from the census work in 2001 coupled with relative abundance indices from population trends. We thus performed systematic field surveys using the distance sampling method, estimating 573 ± 185 individuals nation-wide, with a break-down of 217 ± 81 on the main island of Singapore and 356 ± 104 birds on the satellite of Pulau Ubin. Taken together, the total population estimate reported here comprises 22.9–57.3% of the global wild population, underscoring the importance of Singapore as a stronghold for the species. In spite of its apparently secure status in Singapore, the species remains susceptible to local and foreign trapping pressures. Based on our assessment, we propose a number of local and regional conservation measures to ensure the continued survival of populations in Singapore.

Evaluation of low‐cost consumer‐grade UAVs for conducting comprehensive high‐frequency population censuses of hippopotamus populations

AbstractThe hippopotamus Hippopotamus amphibious (hereafter referred to as hippo) is classified as vulnerable according to the IUCN Red data list. They play a significant role in aquatic systems as allochthonous nutrient providers, and as facilitators and competitors in grasslands. Traditional census methodologies for hippo are difficult and costly to repeat. Previous research has been conducted on the use of unmanned aerial systems (UASs) to conduct hippo population estimates; however, findings either needed justification through additional field testing or used high‐cost UASs that may be unaffordable for management authorities in developing countries in Africa. Therefore, using a low‐cost, consumer‐grade, DJI Phantom 3 Advanced multi‐rotor unmanned aerial vehicle (UAV), 47 surveys were conducted of the hippo population at Ndumo Game Reserve (NGR), South Africa, between August 2016 and July 2017. In addition, comparisons were drawn between the results of and the logistical requirements and costs of the respective helicopter and UAV surveys conducted on the same day of the same hippo population. The use of a consumer‐grade UAV permitted frequent, accurate, and comparatively low‐cost surveys to identify temporal changes in the number of hippos present in NGR and at different locations within NGR. Hippos are a data deficient species, particularly in remote developing countries. UAVs surveys of hippo will allow accurate, highly repeatable, and comparatively low‐cost data collection for management of hippos and the ecosystems within which they occur.

Comparing environmental DNA metabarcoding and underwater visual census to monitor tropical reef fishes

AbstractEnvironmental DNA (eDNA) analysis is a revolutionary method to monitor marine biodiversity from animal DNA traces. Examining the capacity of eDNA to provide accurate biodiversity measures in species‐rich ecosystems such as coral reefs is a prerequisite for their application in long‐term monitoring. Here, we surveyed two Colombian tropical marine reefs, the island of Providencia and Gayraca Bay near Santa Marta, using eDNA and underwater visual census (UVC) methods. We collected a large quantity of surface water (30 L per filter) above the reefs and applied a metabarcoding protocol using three different primer sets targeting the 12S mitochondrial DNA, which are specific to the vertebrates Actinopterygii and Elasmobranchii. By assigning eDNA sequences to species using a public reference database, we detected the presence of 107 and 85 fish species, 106 and 92 genera, and 73 and 57 families in Providencia and Gayraca Bay, respectively. Of the species identified using eDNA, 32.7% (Providencia) and 18.8% (Gayraca) were also found in the UVCs. We further found congruence in genus and species richness and abundance between eDNA and UVC approaches in Providencia but not in Gayraca Bay. Mismatches between eDNA and UVC had a phylogenetic and ecological signal, with eDNA detecting a broader phylogenetic diversity and more effectively detecting smaller species, pelagic species and those in deeper habitats. Altogether, eDNA can be used for fast and broad biodiversity surveys and is applicable to species‐rich ecosystems in the tropics, but improved coverage of the reference database is required before this new method could serve as an effective complement to traditional census methods.

Use of Google Trends as a Novel Epidemiological Tool

Introduction Heart Failure (HF) is an important healthcare issue with wide ranging impacts. Traditional census and epidemiological tools do not adequately address the need for dynamic quantification of disease burden. Analysis of Google search data using Google Trends (GT) can offer dynamic digital insight into epidemiology of heart disease. Hypothesis GT data for HF mirror the trends of HF mortality across the country. It can also be used to detect the efficacy of public outreach program such as Heart Failure Awareness Week (HFAW) in capturing public interest and raising awareness about the disease. Methods GT data (reported as Search Volume Index [SVI]) from 2005-2018 for HF were obtained from Google Trends website. HF age adjusted mortality rates (AAMR) data were obtained from CDC wonder website. JoinPoint regression model was used to analyze temporal trends in HF mortality from 2005-2018. Quadratic regression model was used to analyze association between HF mortality and GT data. One way ANOVA test followed by Tukey post hoc test was used to find differences between monthly GT data. Results Heart failure AAMR decreased significantly during 2005-2011 at an annual percentage change of -2.52 (95%CI: -3.85- -1.17). AAMR increased significantly during the next period 2011-2016 at an APC of 3.51 (95%CI: 2.49 - 4.54). (Figure 1a) These trends had strong positive correlation with the GT data for HF (R = 0.875, p Conclusions GT data mirror the national trends of HF mortality. Google searches for HF were significantly higher in states that had higher HF mortality. GT data can also be used to assess the efficacy of public outreach program such as HFAW. Thus, it can be concurred that GT is an effective and indispensable epidemiological tool that is not currently being used to its potential.

Research on Population Distribution Model Based on Communication Base Station—Take Shanxi Province as An Example

The goal of earthquake prevention and disaster reduction is to scientifically manage the risk of earthquake disasters in order to reduce the impact of earthquake disasters on social economy. The main goal is to reduce casualties. Accurate information of population distribution is an important prerequisite to reduce the impact of natural disasters on population. At present, the population data in the provincial earthquake emergency database are mainly statistical data, which can be divided into population data based on administrative division and population data based on kilometer grid. The population gridded data is closer to the actual distribution of the population than the traditional census statistics, but it is also mainly based on static data. Neither can reflect the changes in the distribution of people during the day, nor can it reflect the impact of holidays on the flow of people, so it is very important to study the dynamic population distribution data. This paper analyzes the main methods of the existing population distribution model, compares the advantages and disadvantages of each model, and points out the limitations of the static population distribution model. At the same time, based on the communication base station and based on the population distribution model of the residential area, Shanxi Province’s work day/holiday population distribution model was established. And through data comparison and analysis, the feasibility of this method is verified.

Fine-Scale Dasymetric Population Mapping with Mobile Phone and Building Use Data Based on Grid Voronoi Method

Fine-scale population mapping is of great significance for capturing the spatial and temporal distribution of the urban population. Compared with traditional census data, population data obtained from mobile phone data has high availability and high real-time performance. However, the spatial distribution of base stations is uneven, and the service boundaries remain uncertain, which brings significant challenges to the accuracy of dasymetric population mapping. This paper proposes a Grid Voronoi method to provide reliable spatial boundaries for base stations and to build a subsequent regression based on mobile phone and building use data. The results show that the Grid Voronoi method gives high fitness in building use regression, and further comparison between the traditional ordinary least squares (OLS) regression model and geographically weighted regression (GWR) model indicates that the building use data can well reflect the heterogeneity of urban geographic space. This method provides a relatively convenient and reliable idea for capturing high-precision population distribution, based on mobile phone and building use data.

Spatiotemporal Patterns of Population Mobility and Its Determinants in Chinese Cities Based on Travel Big Data

Large-scale population mobility has an important impact on the spatial layout of China’s urban systems. Compared with traditional census data, mobile-phone-based travel big data can describe the mobility patterns of a population in a timely, dynamic, complete, and accurate manner. With the travel big dataset supported by Tencent’s location big data, combined with social network analysis (SNA) and a semiparametric geographically weighted regression (SGWR) model, this paper first analyzed the spatiotemporal patterns and characteristics of mobile-data-based population mobility (MBPM), and then revealed the socioeconomic factors related to population mobility during the Spring Festival of 2019, which is the most important festival in China, equivalent to Thanksgiving Day in United States. During this period, the volume of population mobility exceeded 200 million, which became the largest time node of short-term population mobility in the world. The results showed that population mobility presents a spatial structure dominated by two east–west main axes formed by Chengdu, Nanjing, Wuhan, Shanghai; and three north–south main axes formed by Guangzhou, Shenzhen, Shanghai, Wuhan, and Chengdu. The major cities in the four urban agglomerations in China occupy an absolute core position in the population mobility network hierarchy, and the population mobility network presents typical “small world” features and forms 11 closely related groups. Semiparametric geographically weighted regression model results showed that mobile-data-based population mobility variation is significantly related to the value-added of secondary and tertiary industries, foreign capital, average wage, urbanization rate, and value-added of primary industries. When the spatial heterogeneity and nonstationarity was considered, the socioeconomic factors that affect population mobility showed differences between different regions and cities. The patterns of population mobility and determinants explored in this paper can provide a new reference for the balanced development of regional economy.

The Population Census in Austria

Based on the Constitution, the Population Census in Austria is a matter of the central state, serving many political-administrative, planning, research, and other purposes. After its start as a systematic operation in the middle of the 18th century, it developed further by a modern legal basis in 1857 and advances in statistical technology in 1890 (punch cards) and 1971 (machinereadable questionnaires). In the second half of the 20th century, the Population Census became a comprehensive operation, including dwellings, buildings, and workplaces. The communes organized the classical method of on the spot household data collection on behalf of the state. The register-based Census replaced the household collection in 2011. Following a government decision of 2000, which aimed at the sole use of available micro-data, the first steps for creatingthe necessary administrative and statistical registers took place in conjunction with the last traditional census of 2001. With new provisions for linking the records, the Register Census Act of 2006, and a full test census in the same year, the new methodology was established and evaluated in a short period. The first regular Register Census “took place” with reference date 31 October 2011. It is described in the central part of this communication, featuring the advantages, strengths, and weaknesses, the backbone registers, the “comparison registers,” and the redundancy principle, which help to ensure high data quality and the fit of the census into the international framework. Current developments include the annual update of results,changes and improvements in the data sources, and a short outlook on the next census of 2021.

Advances in mapping population and demographic characteristics at small-area levels.

Temporally and spatially highly resolved information on population characteristics, including demographic profile (e.g. age and sex), ethnicity and socio-economic status (e.g. income, occupation, education), are essential for observational health studies at the small-area level. Time-relevant population data are critical as denominators for health statistics, analytics and epidemiology, to calculate rates or risks of disease. Demographic and socio-economic characteristics are key determinants of health and important confounders in the relationship between environmental contaminants and health.In many countries, census data have long been the source of small-area population denominators and confounder information. A strength of the traditional census model has been its careful design and high level of population coverage, allowing high-quality detailed data to be released for small areas periodically, e.g. every 10 years. The timeliness of data, however, becomes a challenge when temporally and spatially highly accurate annual (or even more frequent) data at high spatial resolution are needed, for example, for health surveillance and epidemiological studies. Additionally, the approach to collecting demographic population information is changing in the era of open and big data and may eventually evolve to using combinations of administrative and other data, supplemented by surveys.We discuss different approaches to address these challenges including (i) the US American Community Survey, a rolling sample of the US population census, (ii) the use of spatial analysis techniques to compile temporally and spatially high-resolution demographic data and (iii) the use of administrative and big data sources as proxies for demographic characteristics.

Transition from traditional census to combined and registers based census

Transition from traditional census to combined and registers based census