Phone: 012 538 52 24
Web Map Application for visualisation and dissemination of statistical data - v1.0
This web mapping application (WMA) is an interactive tool for visualization of regional statistical data through thematic maps. The users e.g. can change the geographical level, year, class ranges or colors of the map for the selected indicator. The application provides functionalities for a quick overview of the spatial distribution of a particular phenomenon and its trends observed by the official statistics. The output of the WMA makes it possible to combine it with other spatial data.
The first version of this application was developed between 2015 and 2017 by the State Statistical Committee in the framework of the EU funded Twinning project “Support to the State Statistical Committee in harmonization of the National Statistical System of the Republic of Azerbaijan in line with EU standards”.
The aim of developing this web mapping application is to widen the dissemination of regional statistics produced by the State Statistical Committee. The targeted users are social researchers, policy makers, educational institutes and last but not least the SSC employees.
The borders of the regions and cities of the Republic of Azerbaijan used in this WMA are taken from ArcGIS online. The borders of Economic regions are generated from the borders of Regions and cities. The official borders of regions and cities of Azerbaijan will be used in the next version of the application.
State Statistical Committee of the Republic of Azerbaijan
AZ 1136 Inshaatchilar avenue 81,
(+994 12) 377 10 70
(+994 12) 538 24 42
(+994 12) 377 10 70, 22-66
© 2017 State Statistical Committee of the Republic of Azerbaijan; All rights reserved.
Quantile: Each class contains an equal number of features. A quantile classification is well suited to linearly distributed data. Quantile assigns the same number of data values to each class. There are no empty classes or classes with too few or too many values. Because features are grouped in equal numbers in each class using Quantile classification, the resulting map can often be misleading. Similar features can be placed in adjacent classes, or features with widely different values can be put in the same class. To minimise this distortion the increasing the number of classes can be the solution. To use this method it is essential to specify the number of classes.
Equal interval divides the range of attribute values into equal-sized sub ranges. This allows you to specify the number of intervals, and the application should automatically determine the class breaks based on the value range. For example, if you specify three classes for a field whose values range from 0 to 300, the application should create three classes with ranges of 0–100, 101–200, and 201–300. The equal interval is best applied to familiar data ranges, such as percentages. This method emphasises the amount of an attribute value relative to other values. For example, it will show that a store is part of the group of stores that make up the top one-third of all sales. To use this method it is essential to specify the number of classes.
The Standard deviation method shows you how much a feature's attribute value varies from the mean. The application has to calculate the mean and standard deviation. Class breaks are created with equal value ranges that are a proportion of the standard deviation—usually at intervals of 1, ½, ⅓, or ¼ standard deviations using mean values and the standard deviations from the mean. A two-colour ramp helps emphasise values above the mean (shown in blue) and values below the mean (shown in red). To use this method, it is essential to be able to specify the proportion of a standard deviation to define each class range.
Natural Breaks (Jenks) classes are based on natural groupings inherent in the data. Class breaks are identified by minimizing the variation of values within the each class and maximises the differences between classes. Natural breaks are data-specific classifications and not useful for comparing multiple maps built from different underlying information.
For further information, see Univariate classification schemes in Geospatial Analysis—A Comprehensive Guide, 3rd edition; © 2006–2009; de Smith, Goodchild, Longley. To use this method it is essential to specify the number of classes.