![]() In the BBC’s map of deprivation across local authorities, for instance, sparsely populated rural areas dominate a disproportionately large area, while urban areas, such as London, containing millions of people, are rendered almost invisible.Īn alternative way of visualising the world’s population, using a variant on the cartogram. But using traditional boundaries can divert readers’ attention away from important information. Of course, many outlets used maps to share these findings with the public. ![]() The figures were widely reported, from the BBC to The Guardian and the Daily Mail, reigniting long-standing debates about persistent inequality in England. The government ranked 32,844 neighbourhoods, based on measures of deprivation such as income, employment, health and crime. To see how, consider the latest statistics on deprivation released by the UK government. But as a researcher focusing on data visualisation, I’m aware that even the most beautiful maps can introduce some degree of misrepresentation. Jahrhundert, Self-published by the Deutscher Wetterdienst, Offenbach, 2003 (Reports of the Deutscher Wetterdienst, No.From reporting election results to issuing weather forecasts, maps offer a powerful, accessible and visually appealing way to convey complex information. Müller-Westermeier: Klimatologische Auswertung von Zeitreihen des Monatsmittels der Lufttemperatur und der monatlichen Niederschlagshöhe im 20. Müller-Westermeier: Numerisches Verfahren zu Erstellung klimatologischer Karten, self-published by the Deutscher Wetterdienst, Offenbach, 1995 (Reports of the Deutscher Wetterdienst, No. 2: Uniform colour classes in the maps depicting anomaly values under "Current year - Present - Anomaly from 1961–1990 normal " and under "Climate scenarios - Simulations for the future"ġ. Maps which can be seen at the same time use the same class widths and are shown in the same colour.įig. The calculated values are visualised in the German Climate Atlas as maps. In this case, the anomaly from this climate normal is calculated for each raster grid point, mainly in absolute figures but in some cases in the form of a relative anomaly (e.g. They always relate to the climate normal for the current international 30-year reference time frame 1961–1990. Maps which can be seen at the same time use the same class widths and are shown in the same colour.Įach map's legend always contains just the colour classes which are contained in the respective map.Įxample 2: Uniform colour classes in maps showing anomalies from climate normalsĪnomalies from climate normals can be found under "Current year- Present - Anomalies with respect to climate normals 1961–1990" and under "Climate scenarios -Simulations for the future". They represent the raster data for recorded values or the parameters derived from them. Maps of absolute values can be found in the German Climate Atlas under "Climate normals - Past" and under "Current year - Present - Absolute values". 1: Example of uniform colour classes in the maps depicting absolute values under "Climate normals - Past" and under "Current year- Present - Absolute values" Example 1: Uniform colour classes in maps with absolute valuesįig. The colour shading for each of the parameters is constant over the different months and reference periods so that the maps of absolute values (see example 1) and the maps showing the anomalies from climate normals (see example 2) can be compared, in each case on the basis of the colouring. ![]() With the aid of a topographic grid field and the regression coefficient field the reduced field is finally converted into a field for the climatological parameter which corresponds to the actual local relief. The climatological data which have been reduced to sea level are then also interpolated across the entire range. The regression coefficients which are now available for the entire area are used to reduce the climatological recordings obtained at particular weather stations to sea level values and to assign them to specific grid units. The regression coefficients are assigned to the mid-points of single regions and spatially interpolated across the entire range. The grid fields were produced as follows: In some regions, the linear regression is calculated between the topographic altitude and the climatological parameters. The mean values are projected on to a 1 x 1 km grid structure to obtain the most uniform presentation for the entire area of Germany, a method, which can be explained as follows:Īll maps are based on a 1 km Gauss-Krüger grid for the meridian 9 degrees E. ![]() These maps are based on data recorded at DWD stations and collated into monthly, annual and annual means. ![]()
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