Temporal and Spatial Consistency

2013 ◽  
pp. 1675-1696
Author(s):  
Oliver Duke-Williams ◽  
John Stillwell
Keyword(s):  
Time Series ◽  
Age Groups ◽  
Register Data ◽  
Data Sets ◽  
Interaction Data ◽  
Patient Register ◽  
Health Authorities ◽  
Spatial Consistency ◽  
Using Data ◽  
Over Time

One of the major problems challenging time series research based on stock and flow data is the inconsistency that occurs over time due to changes in variable definition, data classification and spatial boundary configuration. The census of population is a prime example of a source whose data are fraught with these problems, resulting in even the simplest comparison between the 2001 Census and its predecessor in 1991 being difficult. The first part of this chapter introduces the subject of inconsistencies between related data sets, with general reference to census interaction data. Various types of inconsistency are described. A number of approaches to dealing with inconsistency are then outlined, with examples of how these have been used in practice. The handling of journey to work data of persons who work from home is then used as an illustrative example of the problems posed by inconsistencies in base populations. Home-workers have been treated in different ways in successive UK censuses, a factor which can cause difficulties not only for researchers interested in such working practices, but also for those interested in other aspects of commuting. The latter set of problems are perhaps more pernicious, as users are less likely to be aware of the biases introduced into data sets that are being compared. In the second half of this chapter, we make use of a time series data set of migration interaction data that does have temporal consistency to explore how migration propensities and patterns in England and Wales have changed since 1999 and in particular since the year prior to the 2001 Census. The data used are those that are produced by the Office of National Statistics based on comparisons of NHS patient records from one year to the next and adjusted using data on NHS patients re-registering in different health authorities. The analysis of these data suggests that the massive exodus of individuals from major metropolitan across the country that has been identified in previous studies is continuing apace, particularly from London whose net losses doubled in absolute terms between 1999 and 2004 before reducing marginally in 2005 and 2006. Whilst this pattern of counterurbanisation is evident for all-age flows, it conceals significant variations for certain age groups, not least those aged between 16 and 24, whose migration propensities are high and whose net redistribution is closely connected with the location of universities. The time series analyses are preceded by a comparison of patient register data with corresponding data from the 2001 Census. This suggests strong correlation between the indicators selected and strengthens the argument that patient register data in more recent years provide reliable evidence for researchers and policy makers on how propensities and patterns change over time.

Temporal and Spatial Consistency

2010 ◽  
pp. 89-110 ◽  
Author(s):  
Oliver Duke-Williams ◽  
John Stillwell
Keyword(s):  
Time Series ◽  
Age Groups ◽  
Register Data ◽  
Data Sets ◽  
Interaction Data ◽  
Patient Register ◽  
Health Authorities ◽  
Spatial Consistency ◽  
Using Data ◽  
Over Time

One of the major problems challenging time series research based on stock and flow data is the inconsistency that occurs over time due to changes in variable definition, data classification and spatial boundary configuration. The census of population is a prime example of a source whose data are fraught with these problems, resulting in even the simplest comparison between the 2001 Census and its predecessor in 1991 being difficult. The first part of this chapter introduces the subject of inconsistencies between related data sets, with general reference to census interaction data. Various types of inconsistency are described. A number of approaches to dealing with inconsistency are then outlined, with examples of how these have been used in practice. The handling of journey to work data of persons who work from home is then used as an illustrative example of the problems posed by inconsistencies in base populations. Home-workers have been treated in different ways in successive UK censuses, a factor which can cause difficulties not only for researchers interested in such working practices, but also for those interested in other aspects of commuting. The latter set of problems are perhaps more pernicious, as users are less likely to be aware of the biases introduced into data sets that are being compared. In the second half of this chapter, we make use of a time series data set of migration interaction data that does have temporal consistency to explore how migration propensities and patterns in England and Wales have changed since 1999 and in particular since the year prior to the 2001 Census. The data used are those that are produced by the Office of National Statistics based on comparisons of NHS patient records from one year to the next and adjusted using data on NHS patients re-registering in different health authorities. The analysis of these data suggests that the massive exodus of individuals from major metropolitan across the country that has been identified in previous studies is continuing apace, particularly from London whose net losses doubled in absolute terms between 1999 and 2004 before reducing marginally in 2005 and 2006. Whilst this pattern of counterurbanisation is evident for all-age flows, it conceals significant variations for certain age groups, not least those aged between 16 and 24, whose migration propensities are high and whose net redistribution is closely connected with the location of universities. The time series analyses are preceded by a comparison of patient register data with corresponding data from the 2001 Census. This suggests strong correlation between the indicators selected and strengthens the argument that patient register data in more recent years provide reliable evidence for researchers and policy makers on how propensities and patterns change over time.

scholarly journals A Mechanism-based Outbreak Projection Study of Pertussis (Whooping Cough): Combining Particle Filtering and Compartmental Models with Pre-vaccination Surveillance data

2019 ◽  
Author(s):  
Xiaoyan Li ◽  
Nathaniel D. Osgood
Keyword(s):  
Public Health ◽  
Time Series ◽  
Particle Filtering ◽  
Sequential Monte Carlo ◽  
Predictive Accuracy ◽  
Whooping Cough ◽  
Age Groups ◽  
Health Authorities ◽  
Public Health Authorities ◽  
Transmission Models

AbstractParticle filtering is a contemporary Sequential Monte Carlo state inference and identification methodology that allows filtering of general non-Gaussian and non-linear models in light of time series of empirical observations. Several previous lines of research have demonstrated the capacity to effectively apply particle filtering to low-dimensional compartmental transmission models. We demonstrate here implementation and evaluation of particle filtering to more complex compartmental transmission models for pertussis – including application with models involving 1, 2, and 32 age groups and with two distinct functional forms for contact matrices – using over 35 years of monthly and annual pre-vaccination provincial data from the mid-western Canadian province. Following evaluation of the predictive accuracy of these four particle filtering models, we then performed prediction, intervention experiments and outbreak classification analysis based on the most accurate model. Using that model, we contribute the first full-paper description of particle filter-informed intervention evaluation in health. We conclude that applying particle filtering with relatively high-dimensional pertussis transmission models, and incorporating time series of reported counts, can serve as a valuable technique to assist public health authorities in predicting pertussis outbreak evolution and classify whether there will be an outbreak or not in the next month (Area under the ROC Curve of 0.9) in the context of even aggregate monthly incoming empirical data. Within this use, the particle filtering models can moreover perform counterfactual analysis of interventions to assist the public health authorities in intervention planning. With its grounding in an understanding of disease mechanisms and a representation of the latent state of the system, when compared with other emerging applications of artificial intelligence techniques in outbreak projection, this technique further offers the advantages of high explanatory value and support for investigation of counterfactual scenarios.

scholarly journals Trends of white grouper landings in the Northeastern Mediterranean: reliability and potential use for monitoring

2020 ◽  
Vol 21 (1) ◽  
pp. 183
Author(s):  
SINAN MAVRUK
Keyword(s):  
Time Series ◽  
Age Groups ◽  
Monitoring Program ◽  
Bottom Trawl ◽  
Data Sets ◽  
Trawl Survey ◽  
Catch Per Unit Effort ◽  
Multiple Sources ◽  
Statistical Institute ◽  
The Status

As a consequence of national fishery statistics showing a sharp decline in the landings of white groupers (WG), Epinephelus aeneus (Geoffroy Saint-Hilaire, 1817) after 2010, the decision to ban any further fishing of the species was implemented by the Turkish management authority in 2016. Stakeholders have since strongly objected to this decision claiming that the trends of landing statistics are unreliable. Here, this assertion is questioned using multiple sources of data. The catch per unit effort (CPUE) from the fishery independent bottom trawl survey (2004-2018) officially reported landing statistics (2002-2017) and the microdata set of landings (2012-2016) gathered by the Turkish Statistical Institute (TUIK). Based on the results of this study, there was a clear parallelism amongst the data sets. Landing records and the CPUE time series revealed unimodal non-linear patterns along the time (p<0.001). Landings increased until 2010 and decreased thereon after, whereas CPUE values started to decrease after 2009. In segmented time series, there were no statistically significant differences between the direction and magnitude of slopes of landings and fishery independent data. Cross-correlations between landings and CPUE were statistically significant with one and two-year lag distances. This was because the earlier age groups were sampled with coastal bottom trawl operations. Combined with further efforts, this finding may help to develop a monitoring program for the status of white grouper populations in the northeastern Mediterranean and contribute to a better management strategy.

scholarly journals Sex and Geographic Differences in the Prevalence of Reported Childhood Motor Disability and Their Trends in Taiwan

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Cheng-Fang Tsai ◽  
How-Ran Guo ◽  
Yen-Cheng Tseng ◽  
Der-Chung Lai
Keyword(s):  
Rural Areas ◽  
Prevalence Rate ◽  
National Level ◽  
Age Groups ◽  
Geographic Area ◽  
Motor Disability ◽  
The Government ◽  
Using Data ◽  
Rate Ratios ◽  
Over Time

Motor disability (MD) is not uncommon in children, but data at the national level are scarce. As the Taiwan government certifies and registers disabled residents for providing services on a routine basis, the registry provides a unique opportunity for studying MD. Using data from the registry, we calculated the prevalence of MD by age, sex, and geographic area and assessed the changes from 2004 to 2010. We excluded cases under 3 years old because the government discourages the certification at this age. We found that cases between 3 and 17 years old decreased from 8187 to 6022 per year from 2004 to 2010 and the prevalence generally decreased every year in all age groups. There were more boy cases than girl cases every year, and the prevalence rate ratios ranged from 1.26 to 1.39 (p<0.05 in all years), with a decreasing trend over time (p<0.01). Rural areas had higher prevalence in all the years, and the prevalence rate ratio decreased from 1.31 to 1.23 (p<0.05 in all years), with a decreasing trend over time (p<0.05). Further studies identifying the risk factors contributing to the decreases might help in the prevention of MD in the future.

Patterns in Home Care Use in Manitoba

2005 ◽  
Vol 24 (S1) ◽  
pp. 59-68 ◽  
Author(s):  
Lori Mitchell ◽  
Noralou P. Roos ◽  
Evelyn Shapiro
Keyword(s):  
Nursing Home ◽  
Home Care ◽  
Age Groups ◽  
Client Characteristics ◽  
Care Access ◽  
Home Care Client ◽  
Health Authorities ◽  
Time Patterns ◽  
Regional Health Authorities ◽  
Over Time

ABSTRACTAdministrative home care data from the Manitoba Support Services Payroll (MSSP) system for fiscal years 1995/1996 to 1998/1999 were utilized to study home care client characteristics and changes in home care use over time. Patterns in home care access and use after hospitalization, before admission to a nursing home, and before death were examined. The study found that the majority of home care clients were female, aged 65 and over, and not married. The proportion of Manitobans using home care increased slowly, but significantly, over the 4 years. The greatest increases were found among the older age groups. The average number of days that clients received home care before death or before admission to a nursing home was stable over time, while a significant increase over time in home care use after hospitalization was experienced. These findings can be useful to regional health authorities for planning and budgeting.

scholarly journals Quantifying Interagency Differences in Tropical Cyclone Best-Track Wind Speed Estimates

2010 ◽  
Vol 138 (4) ◽  
pp. 1459-1473 ◽  
Author(s):  
Kenneth R. Knapp ◽  
Michael C. Kruk
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Tropical Cyclone ◽  
Temporal Changes ◽  
Data Availability ◽  
Spatially Inhomogeneous ◽  
The World ◽  
Using Data ◽  
Over Time

Abstract Numerous agencies around the world perform postseason analysis of tropical cyclone position and intensity, a process described as “best tracking.” However, this process is temporally and spatially inhomogeneous because data availability, operational techniques, and knowledge have changed over time and differ among agencies. The net result is that positions and intensities often vary for any given storm for different agencies. In light of these differences, it is imperative to analyze and document the interagency differences in tropical cyclone intensities. To that end, maximum sustained winds from different agencies were compared using data from the International Best Track Archive for Climate Stewardship (IBTrACS) global tropical cyclone dataset. Comparisons were made for a recent 5-yr period to investigate the current differences, where linear systematic differences were evident. Time series of the comparisons also showed temporal changes in the systematic differences, which suggest changes in operational procedures. Initial attempts were made to normalize maximum sustained winds by correcting for known changes in operational procedures. The result was mixed, in that the adjustments removed some but not all of the systematic differences. This suggests that more details on operational procedures are needed and that a complete reanalysis of tropical cyclone intensities should be performed.

scholarly journals Culture Process and the Interpretation of Radiocarbon Data

Radiocarbon ◽  
2017 ◽  
Vol 60 (2) ◽  
pp. 453-467 ◽  
Author(s):  
Jacob Freeman ◽  
David A Byers ◽  
Erick Robinson ◽  
Robert L Kelly
Keyword(s):  
Time Series ◽  
Energy Consumption ◽  
Population Size ◽  
Population Change ◽  
Future Research ◽  
Human Populations ◽  
Data Sets ◽  
Economic Complexity ◽  
Using Data ◽  
Outstanding Question

AbstractOver the last decade, archaeologists have turned to large radiocarbon (14C) data sets to infer prehistoric population size and change. An outstanding question concerns just how direct of an estimate 14C dates are for human populations. In this paper we propose that 14C dates are a better estimate of energy consumption, rather than an unmediated, proportional estimate of population size. We use a parametric model to describe the relationship between population size, economic complexity and energy consumption in human societies, and then parametrize the model using data from modern contexts. Our results suggest that energy consumption scales sub-linearly with population size, which means that the analysis of a large 14C time-series has the potential to misestimate rates of population change and absolute population size. Energy consumption is also an exponential function of economic complexity. Thus, the 14C record could change semi-independent of population as complexity grows or declines. Scaling models are an important tool for stimulating future research to tease apart the different effects of population and social complexity on energy consumption, and explain variation in the forms of 14C date time-series in different regions.

Knowledge Discovery Using Data Stream Mining

2018 ◽  
pp. 231-258
Author(s):  
Prasanna Lakshmi Kompalli
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Relevant Information ◽  
Research Community ◽  
Data Stream Mining ◽  
Data Sets ◽  
Stream Mining ◽  
Real World Problem ◽  
Using Data ◽  
Over Time

In recent years, advancement in technologies has made it possible for most of the present-day organizations to store and record large streams of data. Such data sets, which continuously and rapidly grow over time, are referred to as data streams. Mining of such data streams is a unique opportunity and also a challenging task. Data stream mining is a process of gaining knowledge from continuous and rapid records of data. Due to increased streaming information, data stream mining has attracted the research community in the recent past. There is voluminous literature that has been published in this domain over the past few years. Due to this, isolating the correct study would be grueling task for researchers and practitioners. While addressing a real-world problem, it would be difficult to find relevant information as it would be hidden in data streams. This chapter tries to provide solution as it is an amalgamation of all techniques used for data stream mining.

scholarly journals A time series analysis of Prochilodus nigricans landings caught by small-scale fisheries in the lower stretch of the Amazon River

2013 ◽  
Vol 73 (1) ◽  
pp. 53-59 ◽  
Author(s):  
IF. Santana ◽  
CEC. Freitas
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Open Source Software ◽  
Age Groups ◽  
Small Scale ◽  
Amazon River ◽  
Small Scale Fisheries ◽  
Series Analysis ◽  
Using Data ◽  
The Right

We developed a time series analysis using data on curimatã (Prochilodus nigricans), which landed in Santarém, a small city located on the right banks of the Amazon River. A 10-year record of monthly average catches per day of P. nigricans was analyzed using forecasting procedures in the open-source software GRETL 1.7.8. We established two models from the identifications made with the correlograms of hyperparametrization and seasonal differences. The autoregressive terms of the model reach three years, indicating that individuals of the species are being caught around the age of three. This may indicate that the curimatãs in the landings at Santarém from 1992 to 2002 were more than two years old, potentially a sign of a lack of fishing pressure on the lower age groups.

scholarly journals A HARMONIZED DATASET ON GLOBAL EDUCATIONAL ATTAINMENT BETWEEN 1970 AND 2060 – AN ANALYTICAL WINDOW INTO RECENT TRENDS AND FUTURE PROSPECTS IN HUMAN CAPITAL DEVELOPMENT

2016 ◽  
Vol 82 (3) ◽  
pp. 315-363 ◽  
Author(s):  
Anne Goujon ◽  
Samir K.C. ◽  
Markus Speringer ◽  
Bilal Barakat ◽  
Michaela Potancoková ◽  
...  
Keyword(s):  
Time Series ◽  
Human Capital ◽  
Educational Attainment ◽  
Educational Development ◽  
Age Groups ◽  
Data Sets ◽  
Future Prospects ◽  
Human Capital Development ◽  
Recent Trends ◽  
Capital Development

Abstract:We hereby present a dataset produced at the Wittgenstein Centre (WIC) containing comprehensive time series on educational attainment and mean years of schooling (MYS). The dataset is split by 5-year age groups and sex for 171 countries and covers the period between 1970 and 2010. It also contains projections of educational attainment to 2060 based on several scenarios of demographic and educational development. The dataset is constructed around collected and harmonized empirical census and survey data sets for the projection base year. The paper presents the principles and methodology associated with the reconstruction and the projection, and how it differs from several previous exercises. It also proposes a closer look at the diffusion of education in world regions and how the existing gaps in terms of generation, gender, and geography have been evolving in the last 40 years.

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