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In this definitive reference, sophisticated software combines with comprehensive analysis of every aspect of life to represent the world as it really is. Digitally modified maps or cartograms depict the areas and countries of the world not by their physical size, but by their demographic importance on a vast range of subjects, from basic data on population, health, and occupation to how many toys we import and who is eating the most vegetables.

Street-level crime maps are publicly available online in England and Wales. However, there was initial resistance to the publication of such fine-grained crime statistics, which can lower house prices and increase insurance premiums in high crime neighbourhoods. Identifying the causal effect of public crime statistics is difficult since crime statistics generally mirror actual crime. To address this question empirically, we would ideally experiment and introduce a source of random variation in the crime statistics. For instance, we could randomly increase or decrease the number of offences displayed in crime statistics and measure their effects on local house prices. For obvious reasons, we cannot pursue this research design. However, street-level crime maps contain intentional errors, which are the product of a geomasking algorithm designed to mask the location of crimes and protect the identity of victims. This project leverages features associated with the geomasking algorithm to estimate the effect of public crime statistics on house prices.

The safe, highly effective measles vaccine has been recommended globally since 1974, yet in 2017 there were more than 17 million cases of measles and 83,400 deaths in children under 5 years old, and more than 99% of both occurred in low- and middle-income countries (LMICs) 1 , 2 , 3 – 4 . Globally comparable, annual, local estimates of routine first-dose measles-containing vaccine (MCV1) coverage are critical for understanding geographically precise immunity patterns, progress towards the targets of the Global Vaccine Action Plan (GVAP), and high-risk areas amid disruptions to vaccination programmes caused by coronavirus disease 2019 (COVID-19) 5 , 6 , 7 – 8 . Here we generated annual estimates of routine childhood MCV1 coverage at 5 × 5-km 2 pixel and second administrative levels from 2000 to 2019 in 101 LMICs, quantified geographical inequality and assessed vaccination status by geographical remoteness. After widespread MCV1 gains from 2000 to 2010, coverage regressed in more than half of the districts between 2010 and 2019, leaving many LMICs far from the GVAP goal of 80% coverage in all districts by 2019. MCV1 coverage was lower in rural than in urban locations, although a larger proportion of unvaccinated children overall lived in urban locations; strategies to provide essential vaccination services should address both geographical contexts. These results provide a tool for decision-makers to strengthen routine MCV1 immunization programmes and provide equitable disease protection for all children. Although progress in the coverage of routine measles vaccination in children in low- and middle-income countries was made during 2000–2019, many countries remain far from the goal of 80% coverage in all districts by 2019.

In this study, we evaluated the usefulness of six interpolation methods selected to model statistical surfaces using forest cover and population density. The three criteria of consistency of the mapped range of data values, preservation of the volume of the phenomenon, and visual perception of the maps by users were used. The first two criteria were verified experimentally, and the third by interviewing a group of advanced map users. The study results represent a compromise between the mathematical correctness of the maps and their graphical properties. Therefore, they indicate that the optimal interpolation is linear interpolation followed by natural neighbourhood and IDW. This was investigated by interviewing a group of advanced map users, who preferred maps and their graphical properties.

In 2018, 100 years had passed since the Central Statistical Office of Poland (since 2017: Statistics Poland – GUS) was established. This anniversary was considered an opportunity for preparation of a series of cartographic publications, i.e. 16 statistical atlases of Polish voivodships (first order administrative units) and the . Publication of such a series of atlases is a new undertaking in the history of Polish statistics – it involved both the employees of the head office of Statistics Poland in Warsaw and the staff of statistical offices in 16 voivodships. Until 2018 Polish public statistics did not have many such publications. The first atlas publication of Central Statistical Office was released in 1930. The next , covering all of Poland, was published only in 1970. Subsequent statistical atlases were published over 30 years later – the atlases of five voivodships, published in 2006−2016, and the published in 2017. Atlases for individual voivodships were prepared by the relevant regional statistical offices. The project was managed by the head office of Statistics Poland which prepared the guidelines and provided technical and substantive supervision. Due to different sizes of voivodships, the atlases were prepared in scales from 1:900,000 (Opolskie and Świętokrzyskie Voivodships) to 1:1,500,000 (Mazowieckie and Wielkopolskie Voivodships). A standard page contains a map of a voivodship divided into communes (gminas) or counties (powiats) and a map of Poland at the scale of 1:9,500,000 divided into voivodships. The number of pages of the voivodship atlas is 104 with 165 maps: 76 maps of voivodships, 76 maps of Poland, one administrative map of Poland at the scale of 1:3,800,000 and 12 maps of the European Union or Europe at the scale of 1:21,500,000. The was published in early July 2018. It consists of 216 pages, with 281 maps (full-page maps of Poland at the scale of 1:3,800,000, quarter-page maps of Poland at the scale of 1:9,000,000, full-page maps of Europe or the European Union at the scale of 1:21,500,000, and half-page world maps at the scale of 1:200,000,000) and 175 charts/graphs. Maps made by using quantitative cartographic presentation methods predominate in the atlas – choropleth and diagram methods are used most frequently (they are observed on 263 maps). Statistical atlases of voivodships and the count 1888 pages in total with 2934 maps, on which the development of the country is presented in relation to regional and local conditions. All atlases are bilingual, Polish-English. Publications printing was co-financed from EU funds within the Operational Programme Technical Assistance 2014–2020. Atlases are also available free of charge in the PDF format on the website of Statistics Poland:

Depression has been associated with functional alterations in several areas of the cingulate cortex. In this study we have taken a systematic approach to examining how alterations in functional connectivity vary across the functionally diverse subregions of the rostral cingulate cortex. Method Eighteen patients with major depressive disorder, aged 15 to 24 years, were matched with 20 healthy control participants. Using resting-state functional connectivity magnetic resonance imaging (fcMRI), we systematically investigated the functional connectivity of four subregions of the rostral cingulate cortex. Voxelwise statistical maps of each subregion's connectivity with other brain areas were compared between the patient and control groups. The depressed participants showed altered patterns of connectivity with ventral cingulate subregions. They showed increased connectivity between subgenual anterior cingulate cortex (ACC) and dorsomedial frontal cortex, with connectivity strength showing positive correlation with illness severity. Depressed participants also showed increased connectivity between pregenual ACC and left dorsolateral frontal cortex, and decreased connectivity between pregenual ACC and the caudate nucleus bilaterally. The results reinforce the importance of subgenual ACC for depression, and show a close link between brain regions that support self-related processes and affective visceromotor function. The pregenual ACC also has an important role, with its increased connectivity with dorsolateral frontal cortex suggesting heightened cognitive regulation of affect; and reduced connectivity with the caudate nucleus potentially underlying symptoms such as anhedonia, reduced motivation and psychomotor dysfunction.

Street-level crime maps are publicly available online in England and Wales. However, there was initial resistance to the publication of such fine-grained crime statistics, which can lower house prices and increase insurance premiums in high crime neighbourhoods. Identifying the causal effect of public crime statistics is difficult since crime statistics generally mirror actual crime. To address this question empirically, we would ideally experiment and introduce a source of random variation in the crime statistics. For instance, we could randomly increase or decrease the number of offences displayed in crime statistics and measure their effects on local house prices. For obvious reasons, we cannot pursue this research design. However, street-level crime maps contain intentional errors, which are the product of a geomasking algorithm designed to mask the location of crimes and protect the identity of victims. This project leverages features associated with the geomasking algorithm to estimate the effect of public crime statistics on house prices.

Precipitation estimation is one of the main inputs of hydrological applications, agriculture, and disaster management, but satellite-based precipitation datasets often present biases and discrepancies compared to ground measurements, particularly for data-scarce regions. The present work discusses the development of a novel methodology that merges quantile mapping with machine learning-based spatial clustering, aiming at enhancing the accuracy and reliability of satellite precipitation data. Results showed that quantile mapping, by aligning the distributional properties of satellite data with in situ measurements, reduced systematic biases. On the other hand, quantile mapping could not capture the extremes in precipitation merely by relying on a simple model complexity–performance trade-off. While increasing the number of clusters enhanced capturing spatial heterogeneity and extreme precipitation events, the benefit from using more clusters was really realized up to a point, as continued improvement in metrics beyond 10 clusters was marginal. Conversely, the extra clusters further did not provide any significant reductions in RMSE or Bias. This showed that the effect of further refinement in model performance showed diminishing returns. This hybrid quantile mapping and clustering framework provides a robust tool that can be adapted for enhancing satellite-based precipitation estimates and therefore has implications for data-poor areas where accurate precipitation information is key to sustainable water resource management, climate-resilient agricultural production, and proactive disaster preparedness that supports long-term environmental and socio-economic sustainability.

The author’s objective is to present the role of base contents for a statistical map and determine its desired features which would influence optimum perception of the main contents of the map. The article consists of two main parts. The first part discusses selection of cartographic projection. Presented recommendations could be summarized in a short conclusion that application of equal-area projections is the optimum solution. It results from the necessity of preserving correct spatial relations during the perception of the thematic map contents. It is recommended to show cartographic graticule on statistical maps, for it helps to recognize geometrical attributes of the map and plays the localizing role. The second part of the article concentrates on the issue of selection of base contents’ elements on statistical maps. Such selection depends on the method of presentation of the thematic contents of the map. Because of that the optimum contents of the map is discussed in the context of basic cartographic presentation methods, i.e. the dot method, the choropleth method, the diagram method and the isoline method. The role of base elements in the process of reading of the main contents is also underlined. It is shown that it is not only the localizing role. Including additional elements of base contents facilitates understanding of the spatial distribution of the phenomenon which is the main topic of the map. The article encourages extension of the contents of statistical maps by additional elements of contents, which, while keeping correct spatial relations, can improve the efficiency of map use.