Iva Ritschelova
email: iva.ritschelova@czso.cz

Egor Sidorov
email: 2egor.sidorov@ujep.cz

Jiri Hrebicek
email: 3hrebicek@iba.muni.cz

Environmental Statistics at the CZSO: Past Development and Future Trends

Keywords: environment, statistics, history, development, Czech Republic

One of the very important aims of the Czech Statistical Office (CZSO) is to produce national statistical data on environmental development among others. During the last two decades one could trace the constantly increasing interest and attention to environmental statistics both at the international, national and regional level. Being a sustainability management tool, its content has been consequently improving and extending along with the changing political priorities and stakeholder interests. The paper describes the development path of the environmental statistics at the CZSO, makes an assessment of its historical development, current state, and presents the expected future development trends in the field.

Introduction

The Czech Statistical Office (CZSO) is an official national statistical institution of the Czech Republic. Being a coordinator of the State Statistical Service its main goal consists in acquisition of data and consequent production of statistical information on social, economic, demographic, and environmental development of the state. Based on acquired data CZSO yields a reliable and consistent image about the state of the arts and development of the society according to various user needs.

The recent stress laid on environmental protection stimulated by discussions and policies both at international and national levels leads to the stable demand for high quality environmentally related statistics from the variety of stakeholders. This interest is primarily motivated by the need to assess the environmental state as well as to define and describe environmental-economic interrelations that are highly influencing economic activity, employment, foreign trade, price level, etc. This is undoubtedly the reason for growing interest among politicians, as well as businessmen in the range of quality environmentally related statistics suitable for complex environmental-economic analyses.

In reaction to this demand the CZSO has been actively developing production of environmentally related statistics. The following paper maps the main factors that have influenced its recent development and present a bottom line of its current state. The special attention is devoted to key factors and trends that are expected to significantly influence future development in the field.

Development of the Environmental Statistics at the CZSO

Legislation development may be considered to be one of the most significant factors influencing the content of the official environmental statistics produced by the CZSO. Concerning the development of the Czech state environmental policy before 1989 one can mention several legislative acts focused on water, forests and general nature protection that appeared in 1950's. In 1960's the land and air legislation came into being. In 1970's the water law was accepted. As a result of foreign pressure the pact on distance trans-border pollution was signed up in 1980's. Furthermore act on protection and exploitation of mineral resources came into being. However, with a view to planned economy peculiarities, proprietorship model and prevailing political priorities environmental problems have been underestimated and mistreated for the long time (see e.g. Ritschelová et al., 2006). Since there was no political demand for relevant data, one can state that environmental statistics at that time was limited to data closely related to agriculture with no particular environmental purpose.

The development of the environmental policy in Europe followed the general trend of moving from end-of-pipe technologies and pollution abatement practices that were prevailing in the 60's-70's of the 20 th century up to the so-called integrated technologies and cleaner production of the last decades. Concerning the Czech Republic mainly administrative methods of environmental policy were used in the beginning of the 90's. The primary transformation of the Czech economy during that period was connected with significant activities aimed at improving the environmental state that belonged among the most problematic in the Central Europe of that time. In the first half of the 90's the important changes of environmental protection, especially in the legislative sphere, took place.

During that period legislation has consequently covered the variety of environmental domains, state institutions and environmental funding processes. In addition to acts establishing the Ministry for the Environment (1989), Czech Environmental Inspectorate and State Environmental Fund (1991) number of environmental regulations either newly appeared, or were updated. The most important legislative regulations included acts concerning waste (1990, 1991, and 1992), air protection (1991), general environment, protection of nature and the landscape, and environmental impact assessment (1992). These were often stimulated by the European legislative framework. In reaction to these processes the CZSO has started regular waste generation survey in 1992.

In the second half of the 90's the First State Environmental policy of the Czech Republic (1995-2001) was developed. It has clearly pointed out the priorities based on the generally accepted environmental protection principles and set up time frames for single aims. Environmental legislative process of that time was predominately concentrated on the enforcement of post 1989 acts. One should point out the act stipulating right for environmentally related information was adopted in 1998.

Number of the newly introduced measures enabled to achieve the required level of environmental quality through economic restructuring, decreasing the extent of the chosen economic activities, and implementing the new predominately end-of-pipe technologies. These measures placed very high requirements on number and volume of investment projects financed both form the public and private sources. Under these circumstances the demand for data on environmental expenditures naturally occurred within the variety of stakeholder groups. The CZSO had been collecting relevant statistics since 1986 and therefore could offer respective data.

The development of the legislation in the beginning of the new century was connected to entering the EU. The Second State Environmental Policy was accepted in 2001. It mainly reflected the requirements to the EU members and was predominately focused on the fastest implementation of the European environmental legislation. The policy promoted the sustainable development principles and put stress on incorporation of environmental aspects within the sectoral policies. During that time the Czech legal regulations transposed several tens of European environmental directives. The European accent on sustainability promotion demands complex interdisciplinary data from the so-called environmental accounts among others. At present environmental accounting is one of the most actively developing sphere of environmentally related statistics in the CZSO. According to the Recommendation of the Statistical Program Committee (EC) the regular, timely, production of the key environmental accounts modules is expected from national statistical offices. The CZSO is actively taking part in these activities.

Environmental accounts provide a link between economy and environment. The main guideline for implementation of environmental accounts is called System of Integrated Environmental and Economic Accounts [START_REF]Handbook of National Accounting -Integrated Environmental and Economic Accounting[END_REF]. It is based on the same framework as national accounts [START_REF]Greening the National Accounts[END_REF], [START_REF] Ritschelová | K metodice zjišťování výdajů na ochranu ţivotního prostředí[END_REF]. Environmental accounting seems to be the best platform for confronting environmental and economic information and serving as a basis for sustainable development assessment. Only such complex statistical overview of the problem area is capable of supporting sustainability-related decision making at regional and national levels.

The Third State Environmental Policy was accepted in 2004 along with the entering the EU and defined agenda until the year 2010. The contemporary legal system of the Czech Republic responds to all new legal environmental regulations adopted by the European Community. Apart from the so-called administrative tools, one can also see the increasing role of the economic tools represented by fees, environmental taxes, emission permits, etc along with the Czech environmental policy development. Several projects performed by the CZSO were related to these issues as well.

As one can see all the above mentioned institutional conditions during two last decades were actively supporting the interest in high quality environmental statistics. These requirements have also been consequently finding respective reflections in the activities of the Czech Statistical Office. The CZSO environmental department was established in 1993. Its personnel capacity have been fluctuating around 8-9 employees during the whole period of its existence. Today 6 persons are involved into preparation of environmental statistics: 1.5 persons (full time equivalent) are engaged into environmental protection expenditure surveying, 2 persons deal with waste survey, 1 person prepares water statistics, and 1.5 persons perform activities in the sphere of environmental accounting.

It is clear that not all environmental domains are covered by statistical surveys. However one should also mention that a significant part of data is being collected from other administrative sources (i.e. ministries and other bodies of the State Statistical Service). In this case CZSO additionally plays an active integrative role that consists in collecting, aggregating and confronting of available environmentally related statistical data. This enables providing a more complete picture of environmental-economic situation in the country.

Own Statistical Surveys and Overtaken Data

Statistical Survey on Environmental Expenditure

Environmental protection expenditure may be divided into investment and noninvestment (i.e. current) expenditure. For a long period of time environmental protection investment was measured as a part of statistical surveys that weren't primarily focused at environmental issues. For example, during 1986-2001 respective questions were incorporated within the investment survey of the CZSO. One should mention that only investment into fixed environmentally related assets was covered at that time. [START_REF] Ritschelova | Environmental Policy. Selected Chapters. Jan Evangelista Purkyně University in Ústí nad Labem[END_REF][START_REF] Ritschelová | K metodice zjišťování výdajů na ochranu ţivotního prostředí[END_REF] During 1986-1990 environmental investment activities were surveyed both at the macroeconomic level (as total environmental fixed assets investment), as well as at the level of particular investment projects, defined as single constructions and other investment activities leading to improvement of the current environmental state. The latter included all environmentally related constructions with granted construction permission and with the budget over CZK 5 million. Those eventually included constructions individually subsidized from the state budget. Data were currently monitored starting from construction permission grant till final building approval.

All investment activities were broken down into four categories: 1. water quality control, 2. protection of ambient air, 3. waste management, and 4. soil reclamation related to mining and waste dumping activities. During 1992-1995 those were additionally extended by protection activities against physical factors influencing environment, i.e. activities reducing noise level, electromagnetic field intensity, or radioactivity.

The structure of financial sources has been surveyed since 1992. For instance in 1993 the following five categories of financial sources were used within environmental investment activities survey:

1. own sources, 2. loans, 3. support from public budgets, 4. sources from abroad, and 5. other sources. However, one of the shortcomings of this survey was that its results covered only a limited number of constructions. Furthermore single types of financial sources were not unambiguously defined. For example, in case of financial sources obtained from the State Environmental Fund one could classify them as state subsidies, as well as other sources.

Within the framework of harmonizing the survey with the European standards in 1996 the CZSO has changed the environmental investment definition and classification. The new classification became to a large extent similar to the so-called Classification of Environmental Protection Activities (CEPA) adopted by the Conference of European Statisticians in 1994. In this respect environmental investment activities were grouped into 6 categories:

1. water pollution control (excl. groundwater), 2. air pollution control and climate protection, 3. waste management, 4. landscape and nature conservation, 5. reduction of the impact of physical factors, and 6. soil and groundwater protection.

Another important change was connected with the extension of the financial limit of the covered environmental investment types. Since then investment activities with budgets less CZK 5 million were also included. State environmental fund was introduced as an additional financial source, i.e. since 1996 the total of 6 financial source types had been surveyed. National Property Fund was included among other sources.

After the new budget structure was adopted by the Ministry of Finance of the Czech Republic in 1997, its specification of environmental expenditures became very close to the one specified by the CZSO. Particular investment projects survey was cancelled. Through the noticeable progress in the sphere of environmental investment survey methodology, its information capacity was still very limited. In particular due to the fact that it still didn't cover non-investment expenditure.

In 2002 environmental investment survey was performed on the one hand as a part of the annual business survey covering selected production industries and, on the other hand, as a part of the annual survey for government institutions. That year CZSO introduced a new environmental activity classification compatible with the 2000 version of CEPA that has been used up to now. It contains the total of 9 categories, including the following:

1. protection of ambient air and climate, 2. wastewater management, 3. waste management, 4. protection and remediation of soil, groundwater and surface water, 5. noise and vibration abatement (excluding workplace protection), 6. protection of biodiversity and landscapes, 7. protection against radiation (excluding external safety), 8. research and development, and 9. other environmental protection activities. Classification of financial resources was also updated in 2002 reflecting the changes in model of economic relations in the country. Such finance sources as loans and financial assistance, and issue of securities, free transfers, nonmonetary deposits, etc. were newly introduced.

One of the most significant changes took place in 2003 when the first noninvestment expenditure data were introduced for the first time. Non-investment data introduction led to adoption of the targeted Annual survey on environmental protection expenditure. These updates enabled more complex presentation of national environmental protection expenditure. One should mention that in the beginning it covered only business sector. Separate Annual survey covering only investment into long-term fixed assets for government institutions was used until 2005. Since 2006 both business and public sector data have been collected by a Single environmental protection expenditure survey (ZP 1-01 survey). [START_REF] Czso | Indicators on Environmental Protection Expenditure[END_REF] A year later ZP 1-01 survey was additionally extended by environmental fees and charges data. At that time it covered non-financial enterprises in the following fields: agriculture, forestry, mining of mineral resources, manufacturing industry, production and distribution of electricity, gas, water, surface and underground transport, aircraft transport with the number of staff higher than 20 and more (NACE rev.1 codes 01, 02, 10-40, 41, 60, 62). Furthermore the survey covered all non-financial enterprises in the field recycling of secondary raw material and waste disposal (NACE rev.1 codes 37, 90) regardless their size. Finally it included all governmental institutions and municipalities with more than 500 citizens (NACE rev. 1 code 75). The main domestic data users are MfE and Czech Environmental Information Agency (CENIA). Information is also widely used within co-operation with international organizations. For instance based on gentlemen agreement the CZSO regularly fills out OECD/Eurostat Environmental Protection Expenditure 5 and Revenue Joint Questionnaire 6 . One should mention however, that only about 5 percent of required data is being filled out at present. One of the strengths of the survey is that information on environmental revenues and taxes is being collected by its means. Both of them belong to highly discussed topics with a view to political situation in the EU. The first is closely related to Europe 2020 strategy supporting environmental technologies and eco-innovations. The second one corresponds to the so-called environmental tax reform (activities in this field in the Czech Republic have been slowed down lately before all with a view to economic recession and restrictive fiscal policy of the current government).

Concerning the future development in the area of environmental expenditure statistics one should state that under the current circumstances the policy of the CZSO is neither to introduce new surveys, nor to support the extension of existing ones. The aim is not to increase administrative burden as well as not to increase budget requirements. In this respect one should rather expect reduction of survey activities in future. However on the other hand legislative activity at the EU level has a potential to motivate extension and development of the survey under review. This would be the case if Environmental Protection Expenditure Account (EPEA) module would be introduced among others within the environmental economic accounts regulation. National activities aimed at reducing public budgets and international policy supporting environmental activities are the two contradictive factors that would influence the future development in the area of environmental expenditure statistics. One can expect that along with the overcoming of the economic recession impacts interest in relevant statistical data would consequently increase.

Waste Production Survey

CZSO has been making statistical surveys in the field of waste generation and waste treatment since the 80's. After two single-standing one-time surveys of waste dumps and waste production performed by the former Federal Statistical Office in 1980 and 1987 waste generation surveys have been performed on the regular annual basis since 1992. [START_REF] Ritschelová | Determinantion of Waste Production in CR. Eurostat -Work session on environmental statistics, Lysebu -Norway[END_REF] Through the number of existing critiques (e.g. coverage only of special and hazardous waste, or publishing only aggregated statistics due to individual data issues, etc.) statistical questionnaire on wastes in 1993-1996 period established a relatively good basis for further time series creation. This enabled tracing development in the field of production and management of the certain types of waste in the Czech Republic.

Before 1994 surveys were solely focused at disposal and utilization of waste by establishments in order to eliminate double counting possibility connected with transfer of waste between several economic agents. However, strong demand for flow data related to waste transfers between units resulted in extension of survey in 1995 by introducing the name and identification number of the waste acceptor. Based on confronting acceptor organizations with the registers of dump operators and waste incineration facilities one could identify the way of disposal of up to 60 % of the transferred waste.

The survey covered companies classified according to their principle economic activity by such CZ NACE rev. 1 codes and 01, 10-41, 45, 55, 601, 602, 62, 747, 7481, 851, 852, 90, 9211, and 93 with 25+ employees.

The 1998 statistical survey of waste production and disposal was maximally adapted to the requirements of the new act on waste and its regulations for implementation. The main changes included switching to a new waste catalogue, new coding system of waste disposal and utilization ways. These changes were regarded to be the most complex, e.g. due to the fact that the new coding system was regarded to be relatively complicated especially for smaller economic agents. Additional changes consisted in inclusion of other waste category into survey and extension of number of respondents.

In 2002 a new act on waste entered into force. The newly adopted act fully complies with waste management laws and regulations of the EU. The surveyed data is classified according to groups of the official waste catalogue among others. The survey enables production data on waste generated by enterprises (broken down by hazardous and other waste), waste generated by municipalities (including separately collected categories), waste management (recovery, disposal, etc.). This information is supplemented by data on consumption of waste as secondary raw material for producing selected products.

The 2002 survey population included enterprises and sampled municipalities (CZ-NACE code 751). The enterprises group covered units with 20+ employees with the principal economic activity in CZ-NACE codes 01, 02, 10-36, 38-41, 45, 502, 55, 601-602, 62, 642, 747, 7481, 851, 852, 9211, and 93. Furthermore, units classified to CZ-NACE 37 (Recycling) and 5157 (Wholesale of waste and scrap) and units classified to CZ-NACE 90 (Sewage and refuse disposal, sanitation and similar activities) with no limit on the number of employees, were also included in the survey.

In order to reduce reporting burden the so-called "rotating model" approach to respondent selection is used since 2009. It consists in regular surveying of the most significant business entities (according to generated waste, business branch, or pursuant to the number of employees). The remaining "rotational" part of respondents is inquired once every three years with the rest of the data being mathematically imputed. This approach enabled reducing of respondent number by almost a half. Among international users one can name e.g. Eurostat and its data users, as well as national statistical institutions of the EU member states. For more than a decade the CZSO has been transferring waste related data to Eurostat. During 1995-2004 the national data reporting was performed according to the so-called Eurostat/OECD Joint Questionnaire on Waste. In 2006 the environmental statistics department changed the report structure in accordance with EC waste directive for which the CZSO is a national responsible institution. One should expect that the range of possible data users is far more extensive. Since the CZSO publishes the majority of data on its web pages, significant amount of regular data users remain anonymous, since they obtain them indirectly via internet.

Concerning the future development of waste production survey one should mention, that act on waste has been updated recently. The most important changes concern the definition and specification of secondary raw materials. Under the current political and economic conditions producers are getting more and more motivated to transform waste into secondary raw materials and to actively use them as production inputs. In reaction to these changes the CZSO has prepared an update of waste production survey. As a result the new survey would support control and development of national Raw Materials policy due to more precise and complete tracing of material flows of waste as well as secondary raw materials e.g. by covering export and import flows. Under institutional conditions of the Czech Republic the waste statistics is also being currently surveyed by the MfE. However, since its actions are limited by the waste-related legislation, and novelization processes involving number of stakeholders are difficult and time consuming, the MfE has no power to follow the new material flow trends so operatively. This also highlights flexibility of national statistical office in reflecting current socio-economic reality.

Issues of raw materials safety are paid an exceptional attention at the European level at present. Secondary raw materials are looked at as a strategically important input into production process. Under these circumstances one can expect that extended waste (and secondary raw materials) production survey would be consequently improved and developed in future according to various stakeholder needs.

Environmental Accounting

Key modules of environmental accounting in the CZSO include Air Emissions Accounts, Economy-Wide Material Flow Accounts (EW-MFA), and Module for Environmental Protection Expenditure and Revenue. Pilot projects focused at Environmental Taxes by Economic Activity and Environmental Goods and Service Sector were completed as well. One should state that all these modules are still more or less a work in progress. As it was already mentioned, even environmental expenditure survey that has been developing since 1986 is not complete enough to prepare Environmental Protection Expenditure Account (EPEA) according to the requirements described in [START_REF]Handbook of National Accounting -Integrated Environmental and Economic Accounting[END_REF].

However, wide range of methodological work and pilot applications of respective results is currently performed in all key environmental accounts modules. These activities are actively supported by the Recommendation of the Statistical Program Committee (EC) suggesting to provide regular and timely production of the key environmental accounts modules. Eurostat in its turn provides active financial and methodological support to member countries in order to intensify research and development activities in this area. Since no legal base exists in this problem field, at present the majority of data is based upon gentlemen agreements between Eurostat and national statistical institutions. At present some of them transfer data on chosen modules on the basis of other already existing data sources. For instance those include e.g. data on Economy-Wide Material Flows Accounts (based on data from agricultural, industrial and trade statistics), on air emissions accounts (based on the data of air emission inventories), or data on environmental taxes (based on of tax and government finance statistics).

In the meantime proposal for a Regulation on European environmental economic accounts is discussed. The current proposal concerns only the collection of data on emissions, environmental taxes, and material flow accounting. One can expect however, that other modules would be added in future upon the progress in the field of methodology and pilot application (e.g. Environmental Goods and Services Sector (EGSS), EPEA, energy accounts, Resource Use and Management Expenditure Accounts (RUMEA), water, waste, forest accounts, Environmentally Related Transfers (Subsidies) accounts or probably the most problematic ecosystem services accounts).

The first attempt to compile Air Emissions Accounts in the Czech Republic was performed with the support of project of the Ministry for the Environment in 1998. National Accounting Matrix including Environmental Accounts (NAMEA) integrated framework was used for these purposes. The pilot project covered such pollutants as SO2, NH3, NOx, and HCl. As a result of the project time series for time period 1992 and 1995 occurred.

The next project took place in 2004-2005 and was focused on data of 1998, 1999, and 2003. Its aim was to make a step forward and to find a way for regular annual compilation of NAMEA for air emissions in the Czech Republic. In this respect a close cooperation with the Czech Hydro-meteorological Institute (CHMI) was established. This project significantly extended the amount of pollutants covered by the NAMEA framework. In addition to previous results such pollutants as CO2, N2O, CH4, NMVOC, CO, PM10, Hg, Pb, Cd, As, Zn, Cr, Se, Cu, Ni were covered completely, and CO2 bio, HFCs, PFCs, SF6 were covered partly. No data were identified for CFCs and HCFCs. Consequently in 2008 another project was completed. At that time NAMEA Air reporting tables containing 1995-2006 time series were reported to Eurostat. All project results were also used at national level e.g. in reports on the environment of the Czech Republic, or for State environmental policy development and assessment. In 2010 NAMEA Air data collection project continued based on a new compilation guide.

The first attempts of Material Flow Accounts (MFA) preparation in the Czech Republic for years 1990-2003 were performed by the Charles University Environment Centre as a part of its research and development activities. In the following years the CZSO in cooperation with Environment Center carried out a common project aimed at routine MFA preparation process development. As a result of this project the Czech MFA time series for the years 1993-2004 were compiled. Data were collected in close cooperation with Ministry of Industry and Trade and Czech Geological Survey among others. At present CZSO disposes of quite extensive economy-wide material flow time series: domestic extraction is covered for the period 1990-2009, import is covered for 1999-2009, and export is covered for 1999-2009 period. In the close future it is also planned to map outputs of the economy.

As it was mentioned, Environmental Taxes by Economic Activity and Environmental Goods and Service Sector statistics are currently in the phase of development. [START_REF]The Environmental Goods and Services Sector[END_REF] In 2009-2010 the CZSO has got two respective Eurostat grants in order to develop methodology for routine data collection. The aim of the pilot Environmental Taxes by Economic Activity compilation project was to determine data sources for sectoral breakdown of environmental taxes and fees in the Czech Republic additionally broken down into four categories: energy taxes, pollution taxes, resources taxes, and transport taxes. Totally 12 taxes and fees matching the environmental tax definition were specified for these purposes in cooperation with Charles University Environment Center and chosen tax recipients. As a result of the project time series of energy, resources and transport taxes from 1995 to 2008 and pollution taxes from 2001 to 2008 were created in a demanded structure.

Methodology for Environmental Goods and Services Sector data collection was developed in cooperation with Jan Evangelista Purkyne University. The project was focused at possibilities of environmental goods and services related data from yearly industry and production and environmental protection expenditure surveys, core industries conventional statistics as well as government expenditure statistics. As a result of the project time series of environmental goods and services related data for 2005-2009(2008) were developed. Research related to these and other environmental accounting modules in the CZSO would continue in future.

Discussion

Even through the fact that the extent of voluntary business environmental reporting activities is constantly increasing, the role of the state statistical service in the field of producing environmental statistics still holds its exceptional position. The main reason for that is that only centralized state authority is capable of providing objective, standardized, quality and complete environmental statistics. These parameters make it a suitable tool for supporting of decision making at different levels. Furthermore only official statistics can be sufficiently specific and take into account the local conditions as well as particular interests of different policy makers. Being a producer of wide range of other economic and social statistics national statistical office is capable of ensuring the compatibility of different data for the purposes of multidimensional analyses. In this respect official statistics is the most appropriate or even the only data source for promoting sustainable development principles.

One of the critical issues in promoting sustainability consists in analysis of interaction between the economy and the environment. It is widely accepted that the best available approach to the economy description and analysis is based on using national accounting framework. The contemporary System of National Accounts (SNA) is an internationally acknowledged accounting standard at the macro-economic level. The SNA has historically acquired a good reputation of a tool supporting policy-making at different levels. The majority of countries today widely use such SNA-generated indicators for defining objectives and evaluating results of different policies.

The development of the national accounting system has gone a long way from narrow focus at purely economic activities to the wide range of social, economic, and environmental issues. With a view to the constantly changing needs of decision and policy makers as well as other stakeholders, the conventional national accounting system is being constantly transformed and updated in order to meet them.

Significant part of changes is motivated by growing environmental concerns. They have stimulated numerous critiques of the conventional System of National Accounts and brought an idea of linking economic and environmental data. Speaking about the national accounting, the most up-to-date approaches to this linking are discussed in the System of Integrated Environmental and Economic Accounting (SEEA). [START_REF]Greening the National Accounts[END_REF] It comprises several categories of accounts, among which one can find environmentally adjusted accounts. These accounts consider how the existing SNA might be adjusted to reflect defensive expenditure, depletion, and degradation and consequently calculate the environmentally adjusted NDP among others.

Through the common agreement about linking the conventional economic and newly developed environmental accounting systems there is a wide debate about the form of their integration. Some experts argue for the complete greening of national accounts, i.e. integrating environmental concerns into conventional SNA that would consequently adjust main economic indicators. The main argument is that conventional SNA does not provide comprehensive welfare measures and therefore policy-and decision-makers do not get indicators they are actually interested in. Greening of accounts is expected to eliminate this problem.

The other group of experts argues for maintaining explicit distinction between the two accounting systems. The rationale for that is connected with the following two reasons. First, it is the need to maintain consistency with already available historical time series of indicators produced by the SNA. Second, is the conservative principle of accounting preventing from direct incorporation of data retrieved by various nonmarket evaluation techniques into the conventional national accounting.

According to our opinion, the most sensible way is to choose the second conservative approach to linking economic and environmental accounting systems and continue using satellite accounts that were firstly introduced in the SNA 1993. The SEEA is still a work in progress containing number of unsolved methodological issues. In number of cases it provides only discussion and/or overview of possible approaches rather than an action plan. Currently the third version of the SEEA is being prepared. However, one can expect that complete consensus on all arguable points would not be reached in this version either. This conservative approach would enable the development of environmental accounting, provide comparable environmental-economic data, and preserve the consistency of conventional national accounts. One should admit that the SNA can not be (and probably should not be) flexible enough to follow all the newly borne concepts and theories. [START_REF] Leinen | Report on the proposal for a regulation of the European Parliament and of the Council on European environmental economic accounts[END_REF] Additional argument is that environmental accounts are closely related to environmental statistics. In case environmental accounts become part of national accounts the link between accounts in physical and money units may be damaged or become less straightforward. It is a matter of vital importance to preserve direct relation between monetary statistics and physical environmental data. Not only it enables the control of data quality, but it also helps to explain economic processes and provides logical interpretations of different environmental-economic phenomena. In this respect the satellite accounts approach is the right solution for making the whole system more suitable for satisfying the growing information needs of both decisionmakers and other stakeholders involved into variety of issues.

Conclusion

As one can see political interest in environmentally related statistics has been rapidly increasing during the last two decades. The main reasons for that are twofold. First, the alarming environmental problems required legislative actions. These processes created demand for environmental statistics enabling prioritization of respective problems. On the other hand, environmental actions significantly influence economic activities, motivate structural changes and involve number of other related socio-economic consequences. These factors created demand for quality environmental accounting information. As one can see, all these institutional conditions were and are still actively supporting the interest in complex environmentally related statistics.

As key integrative part of the national statistics service the Czech Statistical Office has been making active steps to meet these rapidly changing institutional requirements. One should also note that significant number of environmentally related statistics is also collected due to various command and control purposes. Even through the fact that not all environmental domains are covered by statistical surveys, CZSO data accompanied by these data cover the most important spheres of environmental issues.

Regarding the political demand for environmental data and increasing pressure from variety of other stakeholders, the future of environmentally related statistics is determined by setting up a legal background on the compilation of environmental accounts. One can expect that these activities would support development of environmental satellite accounts and provide complex and comparable data for environmental-economic analyses at the European level. These processes, however, would be obviously slowed-down in reaction to the overall economic situation and consequent reduction of budgets of national statistical institutions. Furthermore, in conditions of overall administrative burden optimization, actions increasing the reporting burden become less and less politically acceptable. In addition to the current stage of methodological development and preparedness of single member countries, this is one of the main reasons why the currently proposed regulation reduces field of action to the three core modules only, with the rest to be consequently added in future. These contradictory factors would determine development in the field in the upcoming years.

Table 1 .

 1 Survey of Environmental Expenditure in 2003-2009. [Source: CZSO] As one can see environmental protection expenditure survey has underwent many phases and experienced multiple methodological changes. Today the CZSO has a relatively extensive time series on environmental investment expenditure (1986-2009), accompanied by a shorter time series of non-investment expenditure (2003-2009 for business and 2006-2009 for public sector), as well as revenues from byproducts and services (2003-2009 for business and 2006-2009 for public sector). The survey data is used before all to fulfill the reporting duty within the Structural Business Statistics regulation. It is widely used for preparation of printed publications and variety of electronic outputs produced either by the CZSO itself or in cooperation with the Ministry for the Environment (MfE) of the Czech Republic. One should mention among others such publications as Environmental Protection Expenditure in the Czech Republic 1 , Report on the Environment in the Czech Republic 2 , Statistical Yearbook of the Czech Republic 3 , or e.g. Regional Statistical Yearbooks 4 .

	year	2003	2004	2005	2006	2007	2008	2009
	Number of respondents	8 039 11 648 11 463 15 288 15 143 11 467 11 705
	Response, %	74	57	62	66	82	82	84
	Upon introduction of CZ-NACE rev. 2 in 2008 the current ZP 1-01 survey covers
	chosen units from CZ-NACE 01, 02, 03, 05-33, 35, 36, 49, 51, 52, 58 with 50+
	employees and CZ-NACE 37, 38, and 39 regardless number of employees,
	municipalities with 500+ inhabitants, budgetary organizations and state funds.

Table 2 .

 2 Waste Production Survey in 1998-2009 [Source: CZSO] Among the main data users directly using data on waste are e.g. such national institutions as Ministry for the Environment and its affiliate Czech Environment Information Agency, Ministry of Industry and Trade, variety of research and development institutions and, of course, media. The collected data is a part of the following regular publications: Statistical Yearbook of the Czech Republic, Generation, Recovery and Disposal of Waste in the Czech Republic, Regional Statistical Yearbooks, Statistical Environmental Yearbook of the Czech Republic, and Report on the Environment of the Czech Republic among others. The last two publications are joint outcomes of CZSO and the MfE.

	year	1998	2002	2004	2006	2008	2009
	Number of respondents 14 461 17 741 19 085 17 875 17 363	9 787
	Response, %	64	69	76	81	79	84

http://www.czso.cz/csu/2010edicniplan.nsf/engp/2005-10

http://rocenka.cenia.cz/stat_rocenka_2009/index.htm

http://www.czso.cz/eng/redakce.nsf/i/statistical_yearbooks_of_the_czech_republic

http://www.czso.cz/eng/edicniplan.nsf/aktual/ep-1#10

http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/Environmental_protection_expe nditure

http://epp.eurostat.ec.europa.eu/portal/page/portal/product_details/publication?p_product_code =KS-EC-05-001


Florian Bauer
email: florian.bauer@reeep.org

Denise Recheis
email: denise.recheis@reeep.org

CMC Martin Kaltenböck
email: m.kaltenboeck@semantic-web.at

Operations Director REEEP Mag Florian Bauer

Junior Researcher

data.reegle.info -A New Key Portal for Open Energy Data

Keywords: semantic web, open government data, energy data, linked data

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Several governments and official agencies have pledged to make all their collected data publicly available, a move that makes sense considering it is public money that is being spent on gaining this data. These days, such data is published on the internet, meaning there is more and more of it available online. Sharing data is expected to deliver more transparency, efficient public service and better use and re-use of data in the public and commercial domains. Especially in the field of (clean) energy, it is crucial to have access to latest and accurate datasets and to be able to link, compare and mash-up this information to provide decision makers, financiers and project developers with the necessary set of information to accelerate the marketplace of clean energy. The Renewable Energy and Energy Efficiency Partnership (REEEP) has taken up the challenge of developing and running a web portal (http://www.reegle.info), which acts as a single point of access to these datasets, offering its datasets according to the Linked Open Data W3C Standards and enriching its own information with relevant data from other open data providers to offer the full benefit of information on clean energy to the user. Linked Data best practise will allow the extension of the worldwide web to a global data space that can connect data from various domains like scientific data, businesses but also film and radio programmes. New applications using Linked Open Data can incorporate new data appearing on the web automatically by recognizing its relevance, and thus the user will receive more holistic results. reegle is joining this movement by providing and using Open Data from renewable energy related themes, and by offering its own applications as well as making its data available for applications of external sites. By using only high-quality sources, concerns about the trustworthiness of data are appeased for users as well as developers working with data retrieved through reegle.

Linked Open Data W3C Standards

When great amounts of data are published, it is important to structure, catalogue and document it in a way that makes it retrievable -not only by humans but also by machines. In order to make data actually re-useable, the distribution of raw data is crucial. This raw data has to be in a well defined machine-readable, structured format.

The Linked Data standards, techniques and technologies allow information to be published in an open and standard format with common web based access methods. This standard web-based data format allows data of various kinds, vocabularies, sources, semantics and authors to be federated, queried and analyzed. It is based on the "Resource Description Framework" (RDF) -a very open and general way to publish and consume data1 .

SKOS and RDF Format

SKOS (Standard Knowledge Organisation System)2 is a formal and controlled vocabulary used in the context of the semantic web for creating taxonomies, thesauri and ontologies. Knowledge organized in such a way uses, for example, the Resource Description Framework (RDF) which encodes the information in a way that it can be used in decentralized metadata applications. RDF triples consist of a subject, a predicate and an object. The subject and object are labelled as a skos:Concept. The predicate describes the "properties" between subject and object, which could be skos:broader if the subject is a child of the object, like for example if "jeans" were the subject then the predicate to its object "trousers" would be skos:broader. So RDF is built from classes/concepts and the properties between them, and each class and property has its own unique identifier URI. This "third dimension" added when converting data to RDF triples allows mash-ups to be created with data from various sources to give it new meaning. This adds lots of value and results in highly interesting and informative applications, especially in the context of resource and energy data.

Thesauri

Thesauri 3 use controlled vocabulary in the sense of a closed list of named subjects as a means to index subjects/concepts. SKOS thesauri use a concept/subject-based classification. Controlled vocabulary has the advantage over uncontrolled vocabulary in that it avoids the use of different terms for identical subjects/concepts, like misspellings, old-fashioned names or simply singular forms. Information, data can be retrieved by its metadata which describes objects which are connected to the subjects/concepts they are about and which are used to classify the objects. An improvement over old-fashioned term-based thesauri is the fact that a concept/subject-based thesaurus allows poly-hierarchies, meaning that a subject/concept can have more than one broader relation. Each concept will always have the same narrow relations, whatever its broader concept may be. Thesauri are a powerful way to classify objects and term subjects/concepts and to provide a way of searching for and browsing data. Furthermore thesauri enable very powerful search based applications as for instance similarity mechanisms et al. To handle this amount of data becoming available now as open data, filter mechanisms and categorization are more crucial than ever.

The reegle Thesauri

In fact, the reegle thesaurus consists of several thesauri covering the subjects of renewable energy technologies as well as climate compatible development. Having been built with the software PoolParty [START_REF] Garsho | Metadata? Thesauri? Taxonomies? Topic Maps! Making sense of it all[END_REF] , it boasts full Linked Data capabilities and it is based on W3C principles which allow linking with other LOD5 sources. Having developed a controlled vocabulary for the fields it covers, the reegle thesaurus is capable of structuring and categorizing the many concepts of clean energy. As a handcrafted piece of work, it can be adjusted when required but operates and powers some of reegle's most important features without the need for constant man power.

The fact that several domain experts from engineering as well as international development have contributed to the reegle thesaurus ensures a high quality and relevance for reegle users. They have the benefit of vast knowledge combined with the comfort and ease of a modern web-tool.

reegle.info as a Linked Open Data Publisher/Consumer

"reegle.info", the gateway for high quality information on renewable energy and energy efficiency acts as a main information source for more than 90,000 users per month in the clean energy sector. It is an offshoot from The Renewable Energy & Energy Efficiency Partnership (REEEP, http://www.reeep.org), a non-profit, specialist change agent aiming to catalyze the market for renewable energy and energy efficiency, with a primary focus on emerging markets and developing countries. Originally launched in 2006, reegle was designed as a specialist search engine for renewable energy and efficiency issues. It didn't publish any content of its own, but eventually it developed further and an actors catalogue was produced for reegle. Two years later reegle underwent its first total make-over when a regular energy blog and a small glossary were introduced, and outreach could be expanded. Still, reegle was promoted foremost as a search engine and the website's design strongly focused on this feature. Through experience and feedback, it became more apparent that user's tended to benefit more from reegle's secondary features, and more features were added. A reegle map visualizing energy statistics and potentials was introduced and again wellperceived by the public. As an ever evolving dissemination tool, it was decided in 2010 to add reegle to the LOD cloud as a user and producer of Open Data. The new data portal (data.reegle.info), launched in 2011, has established reegle as a publisher and consumer of Linked Open Data in the energy sector in an innovative design. This means that data provided on reegle can be located and re-used by 3 rd parties for free, and content is enriched by all relevant data from the LOD (Linked Open Data) cloud. It now provides key datasets free for re-use using Linked Open Data W3C standards. These datasets include extensive energy facts, a set of more than 1700 key stakeholders (actors catalogue) and a comprehensive thesaurus/glossary describing the clean energy sector. On www.reegle.info all available information is enriched by data from other data providers, resulting in comprehensive information dossiers, like the country energy profiles which summarize the most important energy related information (e.g. Regulations, Statistics, Organizations …) for all countries worldwide. reegle data is already being used by external developers to enrich their own information systems as openei.org or the Solar-Med-Atlas (http://dev.geomodel.eu/solar-med-atlas/ ) and thus proves its relevance. This new development once again reinforces reegle's position as a one-stop-shop in the field of renewable energy, energy efficiency and climate compatible development. The energy sector is a vivid example of how Open (Government) Data can be utilized and benefits development and implementation of energy policies, targets and helps decision makers and project developers alike not only by offering the latest relevant data but also by offering new ways of using it.

The data.reegle.info SPARQL Endpoint

The SPARQL6 endpoint7 at reegle.info offers developers to retrieve data on energy statistics, key stakeholders and individual country energy profiles as well as the whole reegle.info thesaurus on clean energy for free and unlimited re-use.

As an example, a user could send a query regarding reegle stakeholders in energy and efficiency NGOs active in the Czech Republic. The query would look like this:

PREFIX foaf: <http://xmlns.com/foaf/0.1/> PREFIX dcterms: <http://purl.org/dc/terms/> PREFIX reegle: <http://reegle.info/schema#> SELECT ?actor ?name WHERE { ?actor a foaf:Organization. ?actor dcterms:subject <http://reegle.info/categories/11>. ?actor foaf:name ?name. FILTER (lang(?name) = "en" ) ?actor reegle:activeIn <http://reegle.info/countries/CZ>. } (Example from data.reegle.info/developers/sparql-intro)

The user will receive a list of all relevant stakeholders, each with its own unique identifier, for example LaGuardia Foundation with its URI (Unified Resource Identifier) http://reegle.info/actors/2423. This information can be used by developers for any application they can think of, free and without strings attached.

3 Benefits for reegle users

Glossary, Web search

Both glossary and intelligent web search are powered by the thesaurus. The glossary makes use of linked data principles and displays Wikipedia definitions beside reegle' own definition and informs the reader of synonyms, abbreviations or other terms that describe an identical "unit of thought"; they are included in the thesaurus skos:prefLabel and skos:altLabel which allows for one preferred label and several alternative labels. Other sources like suitable glossaries or thesauri can easily be integrated to contribute more information when need arises. The web search can be refined through choosing related terms from the thesaurus in a box which can be ticked and which then will be added to the original terms. Related searches are also offered on basis of narrower terms at the bottom of the page. Auto-complete is another convenient measure that is offered, and common spelling errors are accounted for be including them as skos:hiddenLabel in the thesaurus. reegle users can thus find what they are looking for by refining their search and because the reegle web search operates on carefully pre-selected websites, reliability of the retrieved information is guaranteed.

Visualizing knowledge

The reegle thesauri don't only power the intelligent web search and clean energy glossary in the ways just described, but PoolParty also allows a visual display of all the hierarchical and associative relations of a concept. This can really benefit understanding complex and abstract concepts, and by clicking onto the relations the user can dig deeper into a subject and get the full graphic display for each unit of thought.

Country Energy Profiles -a reegle Mash-Up

Following reegle's re-launch in 2011, it now boasts comprehensive and well-arranged energy-related information on all of the world's countries and regions. These profiles are not researched and written by a person, but they are an example of making use of open data that is freely available for re-use. Valuable documents from REEEP's own database such as the regulatory overview and the reegle stakeholders (actors) are presented in the brand-new reegle design and this information is now for the first time enriched with easy-to-understand reegle graphs showing energy demand, production and emissions amongst other key facts retrieved from trusted sources, such as UN data and the World Bank. A short general description and the flag of each country is contributed by DBpedia8 and traditional "links" to relevant web pages documents or other relevant information round up these comprehensive dossiers. This new asset is attracting a lot of project developers, decision makers and other interested parties because they can now gain a complete overview in user-friendly format, getting the relevant facts from the major sources in one place as well as re-use them for free. Another advantage is that new sources that suit the high quality requirements are regularly reviewed and can be added very easy to improve reegle's country energy profiles by such information.

Outlook

The ever increasing information flow can bring about many chances and opportunities, but now is the time to make some considerations and choose the right formats and create the right applications to show the public what can be done with Linked Open Data. One of the recent projects are the reegle.info country energy profiles which link and mash up data from different sources into one comprehensive package and offer machine-readable links to country information in other relevant data sources worldwide. reegle is determined to partake in this exiting development and be at the forefront of open energy data and to underline this progression it has just been re-launched in a new, modern and user-friendly design highlighting its most important features.

W3C eGov Wiki (http://www.w3.org/RDF/)

SKOS (http://www.w3.org/2004/02/skos/)

Thesaurus (http://en.wikipedia.org/wiki/Thesaurus)

http://poolparty.punkt.at

Linked Open Data

SPARQL as the query language of the semantic web offers a powerful API to retrieve data and manage complex queries over several data sources

http://semanticweb.org/wiki/SPARQL_endpoint

Wikipedia datasets (http://dbpedia.org/About)


Jitka Machalová
email: jitka.machalova@mendelu.cz

Capturing of Uncertainty by Geographical IS in Decision Support for Selected Aspects of Quality of Life

Keywords: Fuzzy Sets, Geographical IS, Quality of Life

Quality of life has currently been intensively studied and assessed by using a variety of indicators. Information and communication technologies, or more precisely geographical information technologies, prove invaluable in this process. It is clearly difficult to quantify and spatially localize the quality of life. Fuzzy sets allow the capturing of uncertainty. This article deals with the integration of fuzzy sets into a geographical IS to capture, analyse and evaluate selected aspects of the quality of life.

Introduction

Information systems are used for decision support at all levels of management. Information systems can be viewed as models of specific parts of reality (storage, human resources etc.). They save information about the modelled reality in the form of data; the data are then restructured, analysed and presented to decision-makers by the systems at appropriate times, in requested quality, form, volume and content. Information systems capable of modelling landscapes and the processes running in landscapes are known as geographic information systems, or geo-information systems (GIS).

There are certain setbacks in using models generated by such systems. A typical disadvantage of every model is its simplification of the modelled reality. One of the major simplifications in models based on GIS, is the use of binary logic, for example, if we need to mark an area as waterlogged or dry. Any decision maker, however, who implements these models uses natural language working with uncertainty, for example: the area is "slightly" or "substantially" waterlogged. The tool to work with such uncertainty is the theory of fuzzy sets. Implementation of fuzzy sets in geographic information systems allows us to model, analyse and evaluate the modelled processes better.

A good example of the application of fuzzy sets could be a request to evaluate the quality of life in a specified area. In scientific literature, the term quality of life (QOL) has been discussed by classic psychologists, such as C. G. Jung. First attempts to measure the quality of life were made in treatment of patients. The findings were published in the field of medicine, especially in psychiatry, oncology and geriatrics. Later, the term quality of life and attempts to measure it can be seen in social sciences. One approach seeks to specify objective conditions for quality of life. The other approach monitors subjective human perception as satisfaction with one´s own life. In the last ten years we can see attempts to measure the quality of life from the perspective of marketing and management. The need for sustainable development forces us to compromise between the need for protecting the environment and its maximal use. The number of tools for measuring the quality of life is growing, as they become more specialized in several domains of this kind of assessment.

This work deals with the integration of fuzzy sets into geographic information systems in order to capture, analyse and evaluate selected aspects of the quality of life. The main ideas on the aspects of the quality of life, collectively known as the "well-being factor" will be presented and applied to a representative area in South Moravia in the Czech Republic.

Modelling of Landscape Phenomena

Information about landscape phenomena (elements with a relation to a location on Earth) is saved in the form of geographical data in geographic information systems. Spatial data or geographical data are data which are related to specific places in space with known location at the required level of detail [START_REF] Longley | Geographic Information Systems and Science[END_REF].

Geodata and Map Algebra

Storage of high volume data sets is now managed by geodatabases [START_REF] Prochazka | On Development of Search Engine for Geodata[END_REF]. The data is usually saved in a vector or raster format as thematic layers. Data in vector format has two parts: a spatial and descriptive i.e. attribute part. The spatial part typically uses the rules of vector graphics; the descriptive part uses rules of relational database systems. In the case of geodata storage in the raster format, the layer is represented by a square network of cells or pixels, where a specific value, a number from a specific domain, is assigned to each cell.

The choice of geodata storage depends on many factors. One of the important factors is the need of spatial analysis, performed on the data. Spatial analysis is a process we use to obtain information from geodata. We can use a map algebra apparatus for data saved in the raster format. The map algebra makes it possible to combine raster layers using different mathematical operations and a map algebra language. The map algebra is a tool which allows us to process the raster representation of reality by using the map algebra language. We can carry out map algebra operations with one or more layers. The operations or functions are divided into local, zonal, focal and global ones [START_REF] Tomlin | Geographic Information Systems and Cartographic Modeling[END_REF] Functions are built from operators and operands. Operands can be: arithmetic (plus, minus, times etc.), Boolean (true, false), relational (greater than etc.) as well as specialized, such as bit shifts, accumulative, combinatorial assignment operators. The operands are values stored in a cell or cells of layers used in the spatial analysis (Fig. 1).

Fig. 1. Example of map algebra used with an arithmetic operator on one layer

Fuzzy logic

The basic advantage of fuzzy logic is its ability to mathematically describe information expressed in words. In other words, it enables us to work with ambiguous terms often used in human language. Fuzzy logic permits us to use ICT resources to discern the meanings of "less" or "significantly" based on mutual comparison. The possibility to search for information based on ambiguous or incomplete values with an option to find incorrectly saved information [START_REF] Klir | Fuzzy Sets and Fuzzy Logic[END_REF] is also an advantage.

Suppose there is an element a. We have to decide, whether it belongs to a given set or not. Each element a in a domain D will be assigned an integer z, z ∈ <0;1>, which expresses the grade of membership in the set. Value 0 means: completely not included in the set; value 1 means: fully included in the set. If we have a set of elements, where each element is characterized by a grade of membership, we call this a fuzzy set [START_REF] Zadeh | Fuzzy sets[END_REF].

The traditional set can be understood as a special case of the fuzzy set, z ∈ Z, Z ≡ {O;1}. Numbers z define a membership function. The traditional sets with exact borders h 1 , h 2 can be defined as follows:

MF(a) = 1, if a ∈ <h 1 ;h 2 > (1) MF(a) = 0, if a ∉ <h 1 ;h 2 >
(2) Fuzzy sets can be expressed as follows:

A = (a, MF(a)), if ∀a ∈ D, ( 3
)
where MF(a) is a membership function of element a in domain A with values in closed interval <0;1>. Range of the values of the function MF can be either the whole interval <0;1> or only few selected values.

The MF function and its range can be defined by an expert in the given field or by using operations research methods. The former is used when we model the semantics of words of a natural language. The meaning of words is a subjective matter. When possible, we can ask a number of people to make a statement. There are two ways of doing this. The first option is to let the subjects to express themselves using a number or a graphic scale in order to determine whether a given element satisfies the researched expression. Subsequently, the resulting grade of membership is found using a statistical evaluation e.g. calculating the average of the individual observations. The second option is to let the people express whether the given element satisfies the researched expression or not and the resulting grade of membership will be determined as the percentage of positive answers to the total count of answers. In a real situation, a single person (expert) defines a point A which is not included in the studied set, a point D which is another extreme value not belonging to the set, an interval of values <B;C> with full membership (grade of membership equals 1), and intervals <A;B> and <C;D> to be spanned by a suitable function which approaches the extreme grades of membership at the endpoints of these intervals (Fig. 2).

Fig. 2. Linear and parabolic membership function of a fuzzy set

To a great extent, a fuzzy set is determined by the membership function it is associated with. The selection of a membership function influences the borders of the fuzzy set.

Principles of Fuzzy Sets Integration into Geographic IS

As mentioned above, GIS are able to model the landscape around us. Some elements of landscapes for example plots, have exact borders. However, it is difficult to delimit the borders of some phenomena, such as waterlogging. It is often unclear, how to correctly define for example the possible limits of land slopes. Fuzzy sets are particularly suitable and useful because of their ability to capture the uncertainty when modeling the aspects of the quality of life. In these cases it appears necessary to introduce uncertainty into the GIS in the form of fuzzy geoelements.

Fuzzy geoelement is a geoelement which in addition to a geometry, topology, attributes and time also has its own membership function. The basic fuzzy geoelements are a fuzzy point, fuzzy line and fuzzy polygon [START_REF] Machalova | Definition of fuzzygeoelements in raster representation for decision-making[END_REF].

Each type of the fuzzy geoelement depends on a membership function. Its modeling is based on the concrete membership function. In raster information storage, values of each cell are calculated on the basis of the membership function and a distance of the cell from cells with the value of a membership function equal to one.

Fuzzy point is a fuzzy geoelement given by the ordered triad F, A, M, where F is a membership function, A (with coordinates x, y) is a cell with a value of the membership function equal to one, and M is a set of description (attribute) values of the fuzzy point [START_REF] Machalova | Definition of fuzzygeoelements in raster representation for decision-making[END_REF] (Fig. 3).

Fig. 3. Fuzzy point in raster representation with different membership function

Fuzzy line is a fuzzy geoelement given by the ordered n+2-tuple F, A 1 , A 2 , …, A n , M, where F is a membership function and A 1 to A n are neighbouring cells with the membership function equal to one. A i only has neighbours A i-1 and A i+1 , A 1 only has a neighbour A 2 , and A n has only A n-1 . M is a set of description values of the fuzzy line [START_REF] Machalova | Definition of fuzzygeoelements in raster representation for decision-making[END_REF] (Fig. 4).

Fuzzy polygon is a fuzzy geoelement given by the ordered n+2-tuple F, A 1 , A 2 , …, A n , M, where F is a membership function and A 1 to A n are neighbouring cells with the membership function equal to one, which fill contiguous space. M is a set of describing values of the fuzzy polygon. Fuzzy polygon, which meets requirements of the previous definition, is a fuzzy line [START_REF] Machalova | Definition of fuzzygeoelements in raster representation for decision-making[END_REF] (Fig. 5).

The cell A is a cell of fuzzy geoelement P just if it has the membership function of the fuzzy geoelement P greater than 0.

To make fuzzy geoelements applicable in spatial analyses, it is necessary to define geometrical and topological relations. When Euclidean metrics is used, a position of each cell is defined as the position of its centre, and the distance between two cells is defined as the distance between their centres.

. Fuzzy polygon in raster representation

There are three types of distances between fuzzy points:

1. minimal -minimum of distances of the fuzzy geoelement cells with the grade of membership greater than 0, 2. mean -minimum of distances of the fuzzy geoelement cells with the grade of membership equal to 0.5, 3. simple -minimum of distances of the fuzzy geoelement cells with the grade of membership equal to 1. Two fuzzy lines are continuous only if there is a cell with the membership function value equal to 1 with both lines.

Two fuzzy polygons are neighbouring only if there is at least one cell which has the membership function value greater than 0 with the first fuzzy polygon, and at the same time, there is a neighbouring cell (with a common border) having the membership function value greater than 0 with the second fuzzy polygon.

Two fuzzy polygons are simple neighbours if there is at least one cell having the membership function value equal to 1 with the first fuzzy polygon, and at the same time, there is a neighbouring cell (with a common border) having the membership function value equal to 1 with the second fuzzy polygon.

Fuzzy polygon P contains a second fuzzy polygon Q if each cell of the fuzzy polygon Q with non-zero membership function value has also non-zero membership function value with the second polygon P [START_REF] Machalova | Definition of fuzzygeoelements in raster representation for decision-making[END_REF].

As described above, fuzzy sets provide an option to capture the uncertainty, or vagueness typical for human language. However, the necessity to describe the reality using binary logic causes inaccuracies, which can consequently lead to poor decisions. For example, how can we exactly specify low level of traffic? Fuzzy sets make it possible to capture, analyse and present such "fuzzy" information. The use of fuzzy sets in similar situations is contingent on the following three steps:

1. Correct definition of types of membership functions and the values A, B, C and D (Fig. 2) 2. Assurance of good-quality input data, where the quality is given by precision, authenticity and recency [START_REF] Lake | Virtual Globes as Essential Services?[END_REF]. 3. Modelling fuzzy sets defined by experts and execution of spatial analyses over these sets. Solution of the first two steps is not the main contribution of this article. As noted above, the correct definition of fuzzy sets is up to experts in the given research area. The second step can be critical for the success of the project, because especially with raster data, it requires a compromise between the resolution (number of cells per inch, called DPI) and the size of the file storing the information (in MB, or TB). We usually encounter performance limits of computers at this stage of the analysis, or cluster computers where the analyses are processed.

The article is innovative especially by the approach used in solving the third step. Because the full use of the mentioned methods to provide a comprehensive methodology for assessing the quality of life would be beyond the scope of this article, the article will be focused only on the specific aspects of the well-being factor. The methodology described above will be applied to one of the quality of life indicators -the well-being factor. The study has been carried out on a sample region in South Moravia in the Czech Republic. The area is 150 km 2 . It is relatively densely and evenly populated. There are several small municipalities present, including a regional centre -a town. The landscape is flat with undulating parts and hills. It is used for agriculture; forests are present as well. We can find factories here and also an area unaffected by industry.

Fig. 6. Fuzzy evaluation of the density of buildings in the monitored area

The well-being factor is a summary criterion which means a low density of buildings, traffic, noise, light and smell pollution [START_REF] Swanwick | Land use consultants. Landscape Character Assessment -Guidance for England and Scotland. The Countryside Agency -Scottish Natural Heritage[END_REF]. In the Czech Republic, the well-being factor is understood as a landscape characteristic comprised of the following factors: visual (the visual contamination of scenery), acoustic (the noise level not reaching or exceeding threshold limits), odor (not reaching hygienic limits, but dominant in a negative sense) and emotional disturbance of well-being factors [START_REF] Sklenička | Protection of Landscape Character in Process EIA[END_REF].

The density of buildings differs from population density1. The density of buildings is reported as the percentage of the built-up area to the total area. It is possible to use vector layers of buildings and utility buildings as input data. These layers have to be rasterized, reclassified and merged to a binary raster layer of buildings using map algebra (1 -built-up, 0 -un-built). Using cadastral maps and land plans we will define the built-up area of a municipality and the built-up areas such as country house areas outside the built-up area of the municipality. In these areas, the percentage of the built-up area will be calculated in reference to the total area and expressed using fuzzy sets (Fig. 6) with a linear membership function. Point A = 1 %, B = C = 42 %the maximal density (Fig. 2). The following geographic representation is not expressed as fuzzy sets, because the monitored areas have fixed boundaries also defined in the land plans.

Fig. 7. Fuzzy evaluation of the sources of odor in the monitored area

Odor is a very subjective matter. For the purpose of standardization, relevant legislation regarding odor and its acceptable limits was enacted (Act No. 86/2002 Coll., Decree No. 362/2006). It sets limits for each substance in appropriate units which represent acceptable odor pollution. The well-being factor however, is often affected by values below the legal threshold. There are three sources of odor in the monitored area: a glass factory (arsenic, chromium, cadmium and airborne dust), a sewage water treatment facility (phosphorus) and a cattle and poultry farm (ammonia). The given scenario represents a fuzzy set with a parabolic membership function. Two points, A and D were set, both 1.500 m far from the farm, 500 m far from the sewage water treatment facility and 1.000 m far from the glass factory. The point B = C was set in each source of odor. The figure 7 visualizes the influence on the well-being factor in reference to the character of each source of odor.

There is a personal and public transportation in the monitored area. The transportation takes place on public roads. The quality of life in the area is affected by noise caused by the traffic on the roads and by the quality of roads during their usage. In addition the distance from stops and the time distance from the regional centers while using the public transportation also affects the quality of life.

Fig. 8. Fuzzy evaluation of the source of noise in the monitored area

According to the data published by the World Health Organization, a long-term noise burden above 55 dB is harmful and can cause a variety of illnesses. Noise above 65 dB is reported by physicians as in the long-term unbearable and demonstrably harmful to health [START_REF] Bernard | Analysis of results of the strategic noise maps[END_REF]. Strategic noise maps have been published on the basis of the obligation to create and disclose noise maps. This obligation was stipulated by the European Directive No. 2002/49/EC relating to the assessment and management of environmental noise,. However this is done only for those urban areas and road systems, where more than 6 million cars pass annually. Road traffic has a 95 percent share in the proven annoyance by noise [START_REF] Bernard | Analysis of results of the strategic noise maps[END_REF].

Fig. 9. Fuzzy evaluation of the light pollution in the monitored area

The noise map of the Czech Republic classifies areas into 7 zones according to the noise level. This classification however, was not made with fuzzy sets. Our monitored area is not included in the noise map. In 2005, the Road and Motorway Directorate of the Czech Republic prepared a summary map of the number of cars passing measure points in 24 hours. According to the results, the intensity of traffic is relatively low and stable on the public roads, with values from 1 500 to 3 000 cars in 24 hours [START_REF]Directorate of Roads and Highways: Trafic intensity in 2005 in the Czech Republic[END_REF].There were also local measurements carried out in the streets under the auspices of the municipal authorities. The result is that most streets are used for private transportation of the locals. Another source of noise is a non-electrified state-owned railway. There are no other important sources of noise caused by industry present in the monitored area.

Buildings, vegetation and terrain relief are important in the elimination of the spread of noise. The landscape is relatively open and flat with one major hill. The input data for analyses are the layers of buildings, utility buildings, forests, greenery with scrubs and slopes. In order to get a qualified analysis, the layers must be rasterized first and then reclassified in accordance with the influence on the noise elimination.

Furthermore, a summary layer of the elements eliminating noise must be created using map algebra. The layer then inputs together with the membership function of the spread of noise into the function of spreading. The result is a pair of fuzzy layers presenting the noise caused by the railway and road (public roads + streets) traffic (Fig. 8).

Fig. 10. Fuzzy evaluation of the well-being factor in the monitored area

Light pollution is caused by artificial light, operated without a reason in places, which do not need to be illuminated. This way it disturbs the natural darkness and often glares dangerously. It also disturbs the natural night and biorhythms of the living organisms. The Institute of Science and Technology of Light Pollution (Insituto di Scienza e Tecnologia dell'Inquinamento Luminoso) created a map of Europe classifying the area in Europe into 4 categories,in the range from 0.33 to 27, according to the proportion of the natural and artificially illuminated sky. In our monitored area, the town of Kyjov has the range of values from 3 to 9 and the other areas between 1 and 3. The light well-being is relative present. The light pollution is mainly caused by unsuitable designs of street lamps, illuminated window displays and advertising lasers. In the monitored area, light pollution is caused by passing cars, street lamps and illuminated window displays. The layers of streets and roads with the rating of light intensity are inputs of the analysis. The result are presented in Figure 9.

A marketing research aimed at finding the importance of the researched factors for local inhabitants was recently conducted in the monitored area. 506 respondents living in the Kyjov area took part in the research. 300 respondents were women of age between 22-66 years with the prevailing secondary education. In addition, controlled interviews were conducted with all mayors of surrounding villages. The survey showed clearly that respondents attached the lowest importance which influences the subjective well-being to light pollution and the highest importance to traffic noise. The respondents found the density of buildings as fair; an excessive density of buildings would negatively affect their well-being factor. Odor pollution was found as important as the noise pollution by the respondents of areas affected by odor.

The resulting well-being factor was created using map algebra where the input operands were the layers of the density of buildings, smell, noise and light pollution; the operator is addition. The operands are multiplied by a coefficient which emerged from the marketing research (Fig. 10).

Well-being factor = 2 × the density of buildings + 4 × the smell pollution + 5 × the noise pollution + the light pollution

Conclusions

The result of the analyses is the spatial presentation of the well-being factor. The presentation assigns a well-being factor value of the resulting fuzzy layer to each place of the monitored area. According to the known facts that the well-being factor is mostly influenced by the built-up area, noise, odor and light pollution, the lowest value of the well-being factor can be found in the built-up area and the areas in the neighbourhood of roads or the railway. The highest values of the well-being factor in built-up areas are in the village of Věteřov which is off the main roads. The lowest value of the well-being factor can be found in the regional centre -the town of Kyjov. This can be credited to railway transport, several main streets, two sources of odor and the highest values of the light pollution. The well-being factor is only one of the factors influencing the quality of life. It is completely in accordance with current trends of people moving from the urban areas to rural calm, but within the grasp of the city. Comprehensive modelling of the spatial aspects of the quality of life is a subject of further research. The use of fuzzy layers and map algebra can better capture the uncertainty natural to human thinking expressed in language. Unlike the traditional geoelements, the fuzzy geoelements are able to model selected spatial phenomena more accurately. Therefore the results reflect the modelled reality better.

  . The result of a local function is a change of value of one layer cell. A focal function changes cell values based on values of the neighbouring cells. These functions are mostly used in flow analyses. Zonal functions change values of cells based on values in another layer. A global function concerns the whole layer and is used for example, in distance analyses such as costs or timing.

Fig. 4 .

 4 Fig. 4. Fuzzy line in raster representation with a different membership function

Fig. 5

 5 Fig. 5. Fuzzy polygon in raster representation

Population density is measured as the number of inhabitants per square kilometer. In the Czech Republic, it represents 130 inhabitants/km

, in the monitored area 150-200 inhabitants/km 2 .