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This paper investigates how ancillary geographic data – in particular, information on land or assessor parcels – might be used to improve estimates for small area populations and population characteristics. It seeks to determine whether parcel land use codes can be used to reliably replicate population and housing distributions within small (subcounty) areas and whether other parcel attributes – in addition to land use – exhibit any explanatory power in replicating population and housing characteristics within these same places. The basis for this paper was Professor Barbara Buttenfield's service on the Census Scientific Advisory Committee, in which her working group explored the utility of administrative source data as an alternative or complement to federal survey data. This analysis highlights some of the benefits and complications of the incorporation of parcel data into geodemographic estimation, and the findings demonstrate that such a use is problematic but encouraging. [ABSTRACT FROM AUTHOR]

The present article is the first empirical investigation of attitudes towards immigration in Europe via small area estimation providing reliable regional estimates across European regions. Four dimensions of regional attitudes are considered, i.e., restrictive attitudes towards specific groups, restrictive attitudes towards acquired criteria, threat, and restrictive attitudes towards ascribed criteria. We produce maps of these, as well as validation outputs, demonstrating that our estimates are reliable, hence, have a strong potential in informing policy makers. We show that, although there is a large between-country variation of these attitudes, there are also large spatial differences between-region in some countries. Overall, Swedish public attitudes tend to be quite homogeneous across regions, and located towards the positive side, whereas Eastern European countries tend to show negative attitudes across all the dimensions apart from the acquired criteria. However, in these countries, we can see larger spatial differences across regions, especially in the ascribed criteria and attitudes towards specific groups indicator. In general, the threat dimension does not show a large between-region variability, compared to the other three dimensions. [ABSTRACT FROM AUTHOR]

Abstract Background The COVID-19 pandemic has led to major shocks in mortality trends in many countries. Yet few studies have evaluated the heterogeneity of the mortality shocks at the sub-national level, rigorously accounting for the different sources of uncertainty. Methods Using death registration data from Belgium, we first assess change in the heterogeneity of districts’ standardized mortality ratios in 2020, when compared to previous years. We then measure the shock effect of the pandemic using district-level values of life expectancy, comparing districts’ observed and projected life expectancy, accounting for all sources of uncertainty (stemming from life-table construction at district level and from projection methods at country and district levels). Bayesian modelling makes it easy to combine the different sources of uncertainty in the assessment of the shock. This is of particular interest at a finer geographical scale characterized by high stochastic variation in annual death counts. Results The heterogeneity in the impact of the pandemic on all-cause mortality across districts is substantial: while some districts barely show any impact, the Bruxelles-Capitale and Mons districts experienced a decrease in life expectancy at birth of 2.24 (95% CI:1.33–3.05) and 2.10 (95% CI:0.86–3.30) years, respectively. The year 2020 was associated with an increase in the heterogeneity of mortality levels at a subnational scale in comparison to past years, measured in terms of both standardized mortality ratios and life expectancies at birth. Decisions on uncertainty thresholds have a large bearing on the interpretation of the results. Conclusion Developing sub-national mortality estimates taking careful account of uncertainty is key to identifying which areas have been disproportionately affected.

Abstract Background The increased availability of data on health outcomes and risk factors collected at fine geographical resolution is one of the main reasons for the rising popularity of epidemiological analyses conducted at small-area level. However, this rich data setting poses important methodological issues related to modelling complexities and computational demands, as well as the linkage and harmonisation of data collected at different geographical levels. Methods This tutorial illustrated the extension of the case time series design, originally proposed for individual-level analyses on short-term associations with time-varying exposures, for applications using data aggregated over small geographical areas. The case time series design embeds the longitudinal structure of time series data within the self-matched framework of case-only methods, offering a flexible and highly adaptable analytical tool. The methodology is well suited for modelling complex temporal relationships, and it provides an efficient computational scheme for large datasets including longitudinal measurements collected at a fine geographical level. Results The application of the case time series for small-area analyses is demonstrated using a real-data case study to assess the mortality risks associated with high temperature in the summers of 2006 and 2013 in London, UK. The example makes use of information on individual deaths, temperature, and socio-economic characteristics collected at different geographical levels. The tutorial describes the various steps of the analysis, namely the definition of the case time series structure and the linkage of the data, as well as the estimation of the risk associations and the assessment of vulnerability differences. R code and data are made available to fully reproduce the results and the graphical descriptions. Conclusions The extension of the case time series for small-area analysis offers a valuable analytical tool that combines modelling flexibility and computational efficiency. The increasing availability of data collected at fine geographical scales provides opportunities for its application to address a wide range of epidemiological questions.

Background: The increased availability of data on health outcomes and risk factors collected at fine geographical resolution is one of the main reasons for the rising popularity of epidemiological analyses conducted at small-area level. However, this rich data setting poses important methodological issues related to modelling complexities and computational demands, as well as the linkage and harmonisation of data collected at different geographical levels.Methods: This tutorial illustrated the extension of the case time series design, originally proposed for individual-level analyses on short-term associations with time-varying exposures, for applications using data aggregated over small geographical areas. The case time series design embeds the longitudinal structure of time series data within the self-matched framework of case-only methods, offering a flexible and highly adaptable analytical tool. The methodology is well suited for modelling complex temporal relationships, and it provides an efficient computational scheme for large datasets including longitudinal measurements collected at a fine geographical level.Results: The application of the case time series for small-area analyses is demonstrated using a real-data case study to assess the mortality risks associated with high temperature in the summers of 2006 and 2013 in London, UK. The example makes use of information on individual deaths, temperature, and socio-economic characteristics collected at different geographical levels. The tutorial describes the various steps of the analysis, namely the definition of the case time series structure and the linkage of the data, as well as the estimation of the risk associations and the assessment of vulnerability differences. R code and data are made available to fully reproduce the results and the graphical descriptions.Conclusions: The extension of the case time series for small-area analysis offers a valuable analytical tool that combines modelling flexibility and computational efficiency. The increasing availability of data collected at fine geographical scales provides opportunities for its application to address a wide range of epidemiological questions. [ABSTRACT FROM AUTHOR]

This brief presents a low-power asynchronous 10-bit 2-then-1b/cycle successive approximation register (SAR) analog-to-digital converter (ADC). With partial adoption of 2b/cycle mode only for higher-order bit conversions, additional area and power consumption could be minimized while maintaining fast conversion rate. Moreover, unlike the previous 2b/cycle conversion architecture, where two capacitive digital-to-analog converters (C-DACs) and multiple analog comparators are unavoidable, the proposed 2-then-1b/cycle conversion is implemented with only one C-DAC and one analog comparator by utilizing a digital-based time-domain reference. The speed of 1b/cycle conversion for lower-order bits is improved with a dual-mode comparator. With the aforementioned techniques, the prototype 10-bit SAR ADC is fabricated in 65 nm CMOS process. The ADC core occupies an active area of 0.0142 mm2 and consumes power of 3.8 $\boldsymbol{\mu }\text{W}$ at 0.5 V supply voltage and 3 MS/s conversion rate. The SNDR measured at a Nyquist-rate input frequency is 56.41 dB, resulting in a figure of merit (FoM) is 2.33 fJ/c.-s. [ABSTRACT FROM AUTHOR]

In this paper, a novel level shifter design with ultra-low leakage power and an extremely compact area is proposed. The presented structure is composed of an input inverter, a Wilson-like current mirror, and an output inverter. To minimize the leakage power, a super-cut-off pull-down network and a stacked pull-up network are proposed in our structure. Moreover, the novel architecture employs the multi-threshold CMOS (MTCMOS) and sizing technology to minimize the layout area. The prototype has been fabricated by the SMIC 55 nm MTCMOS technology with an area of 5.614 μ m 2 . Experimental results show that the average leakage of our level shifter is extremely 21.62 pW low power when converting a 0.3 V input to 1.2 V output. At the same time, the average propagation delay and the average energy per transition of the presented structure are 24.06 ns and 4.42 fJ, respectively. [ABSTRACT FROM AUTHOR]