In order to show the powiat’s position in the Voivodship, a comparative method of indicators was used. Selected indicators are marked with letters A to Y. Depending on the type of indicator, the data was ordered:

• descending, i.e. the first (1) position was given to the powiat with the highest value of the feature or
• ascending, i.e. the first (1) position was given to the powiat with the lowest value of the feature.

The powiats for whom the indicator took the same value were assigned the same position, another was left unused and the next one was assigned to the powiat with the next indicator value. For powiats in which the surveyed phenomenon did not occur, „.” was inserted instead of the position.

The summary list of positions is included in the MS Excel spreadsheet. To clarify the created ranking, the extreme positions are marked with the following colors:

• the first – in green,
• the last – in pink.

Pressing at the powiat name displays the chart

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Pressing at the indicator name displays the chart

Symbols

(–)	–	magnitude zero.
(0)	–	magnitude not zero, but less than 0.5 of a unit;
(0,0)	–	magnitude not zero, but less than 0.05 of a unit.
( . )	–	data not available, classified data (statistical confidentiality) or providing data impossible or purposeless.
"Of which"	–	indicates that not all elements of the sum are given.

Abbreviations

PLN	= Polish złoty		ha	= hectare
pcs.	= pieces		km2	= square kilometer
vol.	= volume		m3	= cubic metre
kg	= kilogram		dam3	= cubic decametre
t	= tonne		kWh	= kilowatt hour
km	= kilometre
m2	= square metre

In this publication, as a tool for presenting statistical data, an MS Excel spreadsheet was used, which contains information in the form of absolute and relative (indicators) numbers.

Statistics most often and most commonly uses numbers to describe the studied populations or phenomena. The description may be aimed at presenting the number (size), structure or indices of the population or phenomena.

The collective numerical characteristics of the statistical population include indicators.

The indicator is a quotient of two quantities corresponding to the components of a given population or representing the characteristics of two different populations, or relating to the same population, but in different periods or moments.

There are three main types of indicators:

• indicators of structure,
• indicators of intensity,
• indicators of dynamics.

Indicators of structure, also known as structural numbers, determine the ratio of a certain size concerning a part of the statistical population to that size concerning the entire population.

An indicator or measure of intensity determines the ratio of quantities characterising two different populations or statistical phenomena related to each other in some causal or logical way.

Indicators of dynamics express the ratio between the numbers characterising a certain quantity in two different periods or moments of time. They are used to study dynamic series, i.e. changes that occur in statistical populations over a period of time.

There are two basic measures of the dynamics of a statistical population: absolute increase and relative increase.

The absolute increase is the difference between the values of the variable in consecutive periods (e.g. in consecutive years, months or days).

The relative increase is the ratio of the absolute increase to the value of the variable from the previous period. If this relative increase is multiplied by 100, we will get a percentage increase.

Przyrost absolutny i przyrost względny mogą przybierać wartości dodatnie, ujemne, względnie mogą być równe zero.

Indexes are the most commonly used measure of dynamics. The index is the ratio of the variable value from different periods to the value of the same variable from the determined comparative period.

Relative numbers were usually calculated based on absolute data expressed with greater accuracy than those given in the spreadsheets.

When calculating data per capita (1000 population, etc.) as of the end of the year (e.g. employment, public library collection), the population was assumed as of 31 December, and when calculating data characterising the size of the phenomenon during the year (e.g. vital statistics, dwellings completed) – as of 30 June.

Automatic rounding of numbers in some cases could cause minor differences in sums of data at higher level of aggregation.

Methods for calculating the indicators included in the publication.