Ladislav Dušek

Jan Mužík

Matěj Karolyi

Michal Šalko

Denisa Malúšková

Martin Komenda
email: komenda@iba.muni.cz

A Pilot Interactive Data Viewer for Cancer Screening

Keywords: cancer care, epidemiology, data analysis, data visualisation, CRISP-DM, Czech Republic

The paper introduces processing, modelling, analysis and visualisation of data on cancer epidemiology and cancer care in compliance with a proven and validated methodology. We aim to provide online access to unique data on cancer care and cancer epidemiology, including an interactive visualisation of various analytical reports in order to provide relevant information to the general public as well as to experts, such as health care managers, environmental experts and risk assessors. The data viewer has been developed and implemented as a webbased application, making a very time-consuming process of data analysis fully automatic. The presented data contain dozens of validated epidemiological trends in the form of tables, graphs and maps.

Introduction

Health care reporting and overviews nowadays involve not only the distribution and availability of health care, but also the standardisation of diagnostic and treatment approaches [START_REF] Azevedo | KDD, SEMMA and CRISP-DM: a parallel overview[END_REF]. The European health systems underwent a great deal of reorganisation in the last decade. There has been a tendency to facilitate the expanding involvement of the private and public health care sector, a process which has occurred mainly in the countries of Central and Eastern Europe [START_REF] Cancon | [END_REF]. Cancer care is a prime example of multidisciplinary medical service which requires integration and a certain degree of centralisation in order to ensure an optimal use of resources available and to achieve optimal treatment outcomes. Cancer surveillance holds a privileged position, compared to other diseases, in terms of sources for collecting data, rich experience and availability of data [START_REF] Azevedo | KDD, SEMMA and CRISP-DM: a parallel overview[END_REF]. Various cancer monitoring systems aim to collect data on cancer occurrence and provide much more detailed information on cancer, including diagnostic criteria and therapeutic procedures at the level of individual patients. The Comprehensive Cancer Control (CanCon) initiative aims to improve the quality of cancer care in the European Union. Involved cancer experts from across Europe have joined forces to advance cancer care and reduce cancer incidence by: (i) identifying key elements and quality standards for comprehensive cancer control in Europe and preparing an evidence-based European guide on quality improvement in comprehensive cancer control; (ii) facilitating cooperation and exchange of best practice between EU countries, to identify and define key elements to ensure optimal, comprehensive cancer care [START_REF]Comprehensive Cancer Care Network: A pilot model[END_REF]. CanCon has been divided into nine work packages, of which three are horizontal (dealing with coordination, dissemination and evaluation), and six are core packages (focusing on developing the content of cancer control). The concept of Comprehensive Cancer Care Network (CCCN) has been introduced in the work package 6, providing synergy with all institutions that have complementary expertise. CCCN aims to: (i) promote the optimal use of advanced technologies; (ii) make innovative clinical trials accessible to the entire population in a certain area; (iii) identify the most suitable unit within the CCCN for the management of rare and complex cancers; (iv) promote common infrastructures within the CCCN; and (v) provide a forum for regular consultation among professionals. A pilot model of such CCCN has been set up in the Czech Republic, namely in the Vysočina Region and South Moravian Region. This model covers all components of cancer care: from cancer prevention and organised screening programmes through standard diagnostic and treatment procedures to follow-up plans; specialised care focused on rare cancers as well as palliative care are also included. This CCCN involves one highly specialised national comprehensive cancer centre, three regional cancer centres and four general hospitals. This consortium of core centres was at the very start of the pilot CCCN and took responsibility for the development of binding cancer care protocols, rules of multidisciplinary teams and quality assessment standards [START_REF] Dušek | Czech cancer care in numbers 2008-2009[END_REF]. Beside the required organisation of cancer services in two specific regions (in order to provide the best possible care for their population), collecting data on cancer epidemiology, their processing, analysis, and visualisation is very important in order to summarise long-term trends in cancer burden and to provide up-to-date incidence and mortality overviews.

Problem definition

The paper introduces the domain of epidemiological, clinical and demographic data aggregation, analysis and interactive visualisation. We started by research question formulation, which helped us clarify what exactly we wanted to achieve. We have defined the following issue focusing on a comprehensive and fully representative overview of analytical reports. Specifically, we aimed to investigate how to perform efficient modelling, storage and visualisation of data on cancer epidemiology from an area of Czech hospital network (the CCCN pilot model).

Methodological background

The healthcare industry has continuously generated large amounts of data stored in various locations (e.g. national information systems and specialised registries) [START_REF] Dušek | Cancer incidence and mortality in the Czech Republic[END_REF]. In general, processing, modelling and analysis of these data need to be in compliance with proven and validated methodologies. It allows the discovery of new knowledge and potential useful information based on data describing the particular domain of human interest [START_REF] Dušek | The national web portal for cancer epidemiology in the Czech Republic[END_REF]. In fact, analytical reports, overviews and visualisations should help plan, understand, work through and reduce cost by detailing procedures to be performed in each of the steps. For our purposes, we decided to use the CRoss-Industry Standard Process for Data Mining (CRISP-DM) reference model, which provides a life cycle overview of a given research question [START_REF] Edge | The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM[END_REF]. The CRISP-DM model serves mainly as the methodological standardised guideline in practice (Fig. 1).

Business and data understanding

The Czech Republic is ranked among those countries with the highest cancer burden in Europe and worldwide [START_REF] Micheli | European health systems and cancer care[END_REF]. Based on a thorough knowledge of the domain of cancer epidemiology, we needed to define the problem in the form of a research question corresponding to local regional estimates of incidence and mortality rates, which helps to choose the proper statistical and analytical methods and software tools selection. The main objective of our study entitled "CCCN pilot model: Interactive data views" was to design, to develop and to implement a web-based tool supporting scientific analytical reports on cancer data aggregated with demographic data from the Vysočina Region and the South Moravian Region. This stage also involved a more detailed fact-finding about all data sources. The Czech National Cancer Registry1 provides fully representative long-term trends and consists of cases according to main risk factors and diagnostic descriptors including TNM classification of tumours in the following diagnostic groups [START_REF] Organization | International statistical classification of diseases and related health problems[END_REF]. These groups are presented in accordance with the 10th edition of the International Classification of Diseases (ICD-10) terminology [START_REF] Raghupathi | Big data analytics in healthcare: promise and potential[END_REF], which is the standard diagnostic tool for epidemiology, health management and clinical purposes. We used only Chapter 2 (C00-D48 diagnoses), which classifies all neoplasms:

• I.

Data preparation and modelling

We decided to use the data warehousing concept, which makes it possible to integrate information from heterogeneous databases and to query very large databases efficiently [START_REF] Romero | Knowledge discovery with genetic programming for providing feedback to courseware authors[END_REF]. We aimed to synthesise available data on cancer care and to store them together in a single repository. First of all, a four-step process -extraction, transformation and load (ETL) -was performed.

Step 1: Stage. The first step includes data retrieval from the Czech National Cancer Registry and the Czech Statistical Office. Raw data are mined from both databases and further processing (data cleaning and transformation) is needed due to data format unification. Moreover, we discovered key dataset features and characteristics, which also include the tables, records (rows), and attributes (columns) selection. In case of a wrong format or syntax of the uploaded CSV file (syntactical and semantical check is performed), the transaction is aborted and the entire step has to be repeated. The output of the step is one table called <<import_nador>>.

Step 2: Operational data store. The second step is represented by the database table <<fact_primar>>. Data are syntactically and semantically checked by function f_re-start_fact_primar() right before being stored to this table. If the check is not successful, the table <<import_nador>> has to be fixed and an attempt to transform it into a table <<fact_primar>> has to be made again.

Step 3: Primary Data Warehouse (PWD). The third step consists of fact tables and dimension tables. The fact tables are not directly connected to each other, but there are relational links between them, depend-ing on their dimensions contents. Dimension tables are simple tables which contain an abstract identifier as the primary key and columns with descriptive data; these are displayed on the screens of the data viewer. Based on the selection of one or more descriptive attributes, clustering of identifiers in the structure suitable for data selec-tion from the dimension tables is performed. The Data Mart includes one table entitled <<fact_agr_clinic_stage>>. The data for endpoint visualisations can be selected from all fact tables, but there are significant performance differences (e.g. between the selection of the same information from the <<fact_primar>> and the <<fact_agr_clinic_stage>> tables). The fact table <<fact_agr_clinic_stage>> (Fig. 3) is part of the CCCN Data Mart. This table is derived from fact tables included in the PDW and has the following dimensions, which correspond to the visualisation filters: dregion, dyear, dsex, dage_group, dage_group2 and ddiagnose_stage. The last dimension is a set of diagnoses and groups of diagnoses in accordance with ICD-10.

Fig. 3. Fact table of clinical stages

where all relations between <<fact_agr_clinic_stage>> entity (white), parametric lists (grey) are shown.

Evaluation and deployment

The evaluation phase assesses the degree to which the analytical reports meet the given objectives and seeks to determine if there are any imperfections or inaccuracies in terms of graphs, table validity and general user understanding. We made a detailed computational validation of the outputs presented by tables and graphs using standard epidemiological statistical methods [13]. This statistical validation was computed using the SPSS 24.0.0.0 software. The CCCN pilot interactive data viewer is a web-based application written in the Symfony framework3 version 3.1. The data warehouse is implemented as a PostgreSQL4 9.5 database, which is hosted on the database server. The application core is divided into two parts:

• AppBundle (Symfony project) -the main application bundle which provides data access layer, PHP entity mapping and all processes connected to the Model-View-Controller (MVC) architecture. • VisualisationBundle -this bundle contains a functionality connected strictly with graphs, data tables and filters shown on all screens. The Git5 system was used for an efficient work with the code. All instances can be independently tested by a set of acceptance tests written in the integrated development environment for Selenium IDE 6 Mozilla Firefox plugin, which allows tests recording, editing, and debugging.

Results

The developed data viewer 7 allows the user to investigate general epidemiological trends for a particular diagnostic group. The viewer is divided into three individual modules, each of them containing a set of specific analytical reports visualised by interactive graphs and maps (Table 1). The user can access the analyses of each module through a module navigation homepage (Fig. 4). The selection of individual analyses is at the top of the screen. The user has an overview of all provided data views in the current module, and can choose the one that interests him/her. The remaining part of the navigation page contains a selection of a particular diagnosis. The diagnoses are divided into twelve major cancer groups, which were defined in the domain and data understanding section. The groups can be freely expanded and browsed directly in the tree list (on the right) or by using the schematic picture of a human silhouette with tooltip elements and information about the part of human body being hovered over (on the left). After selecting a specific diagnosis -or a subgroup of diagnoses -the screen with the required analytical report appears again. For illustrative purposes, four epidemiological views and analytical reports of the "Cancer epidemiology" viewer module are described as following. When a user accesses the above-mentioned analyses in a standard way (there is also the option of proceeding through an URL with the already pre-filtered content), the graph and the table displays the complete information about all records in the registry. Additionally, we have provided a set of filters that correspond to fact table dimensions in the data warehouse. If the filter is disabled, the user is informed by the button's grey colour and the value is not included in the list of applied filters. The complete list of filters with analysis settings is on the right side of the screen (Fig. 5) and contains the following types of filters:

• Sex filter -men, women, both sexes.

• Age filter -five-year groups from 0 to 85+.

• Region filter -selection from 14 regions of Czech Republic plus the CCCN pilot model. • Period filter -years from 1977 to 2014.

• TNM filter -extension of the primary tumour (T), regional lymph nodes (N), and distant metastases (M) [START_REF] Sastry | Implementation of CRISP Methodology for ERP Systems[END_REF]. • Clinical stage filter -combination of the TNM staging system, which determines the stage of cancer for each person (four stages: stages I to stage IV).

Conclusion

We have designed, developed and implemented a prototype of interactive viewer of data on cancer care and cancer epidemiology, which shows the contemporary analytical overview of cancer incidence, prevalence and mortality, including the evidence on cancer care and cancer epidemiology in the CCCN pilot region and on the national level.

In future, we might be able to extend the viewer in order to ensure regional and also international benchmarking analyses. Generally, the visual representation of cancer screening data is very heterogeneous (free text, parametric text, numerical and graphical format). The trend aims to show available data sources in a form of interactive visualisations, specifically graphs, maps and diagrams. Modern users want to easily understand aggregated information in a comprehensive and validated shape for further decision-making activities. The presented data visualisation concept heads towards data-driven approach, which is quite useful for health professionals in situations where a large amount of data must be presented in the most comprehensible way.

Fig. 1 .

 1 Fig. 1. CRISP-DM schema showing the relations between the different phases of the process.

Fig. 2 .

 2 Fig. 2. Complete data flow in CCCN data warehouse subset.

Fig. 4 .

 4 Fig. 4. Diagnosis selection page provides a human body interactive silhouette including tooltips (on the right) in combination with a complete list of all available diagnoses (on the left).

Fig. 5 .

 5 Fig. 5. Visualisation of incidence and morbidity trends over time.

  Data describing the main demographic characteristics of the Czech population (such as the total population, age structure or life expectancy) were provided by the Czech Statistical Office 2 .

	Head and neck cancers;
	• II. Digestive cancers;
	• III. Cancers of the respiratory tract and intrathoracic organs;
	• IV. Bone and soft tissue cancers;
	• V. Skin cancers;
	• VI. Breast cancers;
	• VII. Gynaecological cancers;
	• VIII. Genitourinary cancers;
	• IX. Cancers of the central nervous system and eye;
	• X. Malignant neoplasms of lymphoid, haematopoietic and related tissue;
	• XI. Endocrine cancers;
	• XII. Other malignant neoplasms;

  It is implemented through the re-start_dimensions_function() function, which firstly erases data from allthe dimension tables and secondly inserts data to each dimension table by accumulation (imple-mented through GROUP BY clause) of <<fact_primar>>. In cases when the <<fact_primar>> table does not contain any descriptive attributes, data for the di-mension table are inserted to it through SQL commands or manually from external sources. In contrast to the table <<fact_primar>>, fact tables do not contain atomic data providing the base for all data transformations, but contain aggregated data created by the count(*) function over all the appropriate dimensions. The data are cumulated in them. This means that the index over all dimensions in each fact table is unique and the measuring columns contain the numbers of specific cases that oc-cur.

Data selection is performed faster in this way. The only exception is the <<fact_demog-raphy>> table, which already contains aggregated data, and is import-ed directly from CSV file.

Step 4: Data Mart. This phase involves tables derived from the PDW data. These are so far represented only by the <<fact_agr_clinic_stage>> table. Data in the tables are derived from the fact tables <<fact_demography>> and <<fact_agr_patient_diag-nose>>. Again, the data in the fact table are arranged similarly to PWD based on aggregation over existing dimensions. The whole ETL process of selected entities is shown below (see Figure

2

). The external source (CSV file) is imported to the database entity <<import_nador>>, validated and transformed to the table <<fact_primar>>. At this point, several fact tables of PWD (e.g. table <<fact_agr_patient_diagnose>>) are created. The table <<fact_demography>> is created directly from the external source.

Table 1 .

 1

	Viewer module	Analytical report
		Incidence and mortality trends over time
	Cancer epidemiology	Prevalence trend over time Age structure of incidence and mortality
		Age-specific incidence and mortality rate
	Regional benchmarking	Incidence rates in regions Mortality rates in regions
		Distribution of clinical stages
	Cancer diagnostics	Incidence trends by clinical stages Distribution of clinical stages by age
		Comparison of incidence of clinical stages by age

•

  Incidence and mortality trends over time show annual incidence (newly diagnosed cases of a selected diagnosis) since 1977, and annual mortality (deaths caused by a selected diagnosis) since 1977 -absolute numbers or rate per 100,000 population. • Prevalence trend over time shows annual prevalence (alive persons with disease or its history) since 1990 (point prevalence at 31 December of each year or interval prevalence during years) -absolute numbers or rate per 100,000 population. • Age structure of incidence and mortality, and age-specific incidence and mortality rates show the numbers of new cases (incidence) or deaths caused by diagnosis (mortality) according to five-years age groups (absolute numbers, percentages or rate per 100,000 population).

http://www.uzis.cz/en/registers/national-health-registers/czech-national-cancer-registry

https://www.czso.cz/csu/czso/home

http://symfony.com/what-is-symfony

https://www.postgresql.org/about

https://git-scm.com

http://www.seleniumhq.org/projects/ide

http://cccn-viz.onconet.cz


Nicholas J Car
email: nicholas.car@ga.gov.au

Alex Ip
email: alex.ip@ga.gov.au

Kelsey Druken
email: kelsey.druken@anu.edu.au

netCDF-LD SKOS: Demonstrating Linked Data Vocabulary Use Within netCDF-Compliant Files

Keywords: netCDF, netCDF-LD, Linked Data, inference, vocabularies, SKOS

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

Introduction

NetCDF files [START_REF]Network Common Data Form (NetCDF)[END_REF] are containers that include both data -usually array-oriented scientific data -and metadata. The intention of the metadata inclusion is that netCDF files should be 'self-describing' meaning the metadata (usually referred to as being in a 'header') describes the rest of the file (the data), as well as an arbitrary number of name/value attributes which together allow for accurate interpretations of the data and thus sensible use of it. Some communities, such as the Climate and Forecasting (CF) Conventions [START_REF] Eaton | NetCDF Climate and Forecast (CF) Metadata Conventions[END_REF], use standardized name/value attributes to enhance metadata understanding between different parties leading towards interoperability of netCDF data. In most cases, those communities achieve interoperability by constraining the metadata values, potentially reducing its richness. The Semantic Web [START_REF] Berners-Lee | The Semantic Web[END_REF] is a set of extensions to Web standards that allow for data exchange and, ultimately, knowledge representation. Collections of terms (vocabularies) and knowledge graphs (ontologies) can be codified and, through Linked Data principles [START_REF] Berners-Lee | Linked Data[END_REF], be published and accessed. The ability for Linked Data links in any Internetconnected system to give live access to rich vocabulary and ontology information published on Web is potentially very useful for netCDF data. Such links in file metadata allow associations with further, more detailed, metadata, meaning that not all of the metadata relevant to a netCDF file needs to be contained within the netCDF container itself. While this may seem to break with the netCDF aim of the format being selfdescribing, we believe that conventions that maintain any information externally to netCDF files, such as the CF Convention's relations between terms given in a website hierarchy, have already broken with this (what we believe to be impossible) aim.

Agreed systems of knowledge representation (ontologies) can be used to define the type (classes) of knowledge within a domain. One such ontology is the Simple Knowledge Organization System (SKOS) [START_REF]SKOS Simple Knowledge Organization System Reference[END_REF] which has been made to represent items within vocabularies and thesauri. Rather than constraining the metadata values of netCDF files to achieve interoperability, Semantic Web and Linked Data methodologies can be used to constrain the mechanisms for describing and accessing metadata without constraining the content itself. In addition to achieving interoperability via the use of SKOS, since it is a standardized and a well-known ontology, SKOS use also instantly provides netCDF file makers with access to a large number of already published SKOS vocabularies and their content, such as those employed by Geoscience Australia1 and several versions of the CF Conventions' terms which have recently been published 2 .

Sophisticated methods for the inclusion of Linked Data within netCDF files have been demonstrated at a previous ISESS conference called 'netCDF-LD' [START_REF] Yu | Towards Linked Data Conventions for Delivery of Environmental Data Using netCDF[END_REF] and an example of netCDF-LD using Linked Data reasoning has also been given recently [START_REF] Biard | Linking netCDF Data with the Semantic Web -Enhancing Data Discovery Across Domain[END_REF]. This paper is both a 'next step' to those bodies of work and also a step back with respect to the complexity of implementation. We present a simplified mechanism for referencing SKOS data within a netCDF file (Section 2) which, we believe, is easily understood by users of regular netCDF data, and yet which delivers at least some the benefits that a fully-fledged combination of netCDF and Linked Data offer. Due to the simple nature of our encoding mechanism, we can demonstrate a simple wrapper for the well-known ncdump command line tool, which we call ncskosdump, which prints out netCDF data and metadata. Details of this tool are given in Section 3. In Section 4, we describe a test deployment of SKOS data to netCDF files that references a demonstration SKOS vocabulary that we have built. Vocabularies like this are to be used in place of community-agreed conventions for the classification of those files, using features of the vocabulary, using ncskos tools. In Section 5, we discuss how the netCDF combined with the Linked Data and Semantic Web methodologies and ncskos tools we have presented here might be made more efficient when used at scale. Finally, in Section 6, we indicate a few areas of future work regarding more comprehensive linked data representation in netCDF files.

2

Simple Linked Data encoding While Yu et al. [START_REF]SKOS Simple Knowledge Organization System Reference[END_REF] proposed and Baird et al. [START_REF] Yu | Towards Linked Data Conventions for Delivery of Environmental Data Using netCDF[END_REF] have demonstrated sophisticated integration of Linked Data into netCDF files, we have tested simples of integrations only whereby we place Universal Resource Indicator (URI) links [START_REF]URIs, URLs, and URNs: Clarifications and Recommendations 1.0[END_REF] to SKOS concepts within attribute metadata in netCDF files. Our reason for taking this very simple approach is to enable the demonstration, and ultimately the adoption, of some limited Linked Data and Semantic Web functionality with the lowest possible barrier to uptake. This link inclusion approach delivers files that very closely resemble 'normal' netCDF files that conform with the netCDF4 specification. Figure 1 shows some basic netCDF metadata taken from a file used by the Unidata community for the demonstration 3 of metadata extraction using the ncdump tool.

Fig. 1.

A sample of normal netCDF metadata for the time variable within an example file 2 . float tos(time, lat, lon) ; tos:standard_name = "sea_surface_temperature" ; tos:long_name = "Sea Surface Temperature" ; tos:units = "K" ; tos:cell_methods = "time: mean (interval: 30 minutes)" ; tos:_FillValue = 1.e+20f ; tos:missing_value = 1.e+20f ; tos:original_name = "sosstsst" ; tos:original_units = "degC" ; float tos(time, lat, lon) ; tos:skos__concept_uri = "http://pid.geoscience.gov.au/def/voc/netCDF-LD/sea_surface_temperature"; tos:units = "K" ; tos:cell_methods = "time: mean (interval: 30 minutes)" ; tos:_FillValue = 1.e+20f ; tos:missing_value = 1.e+20f ; tos:original_units = "degC" ; Fig. 2. Similar netCDF metadata to Fig. 1 with key/value pairs removed and a skos_con-cept_uri key/value pair added with the URI of a concept in a demonstration vocabulary 4 .

Figure 2 shows this same header metadata with the standard_name key/value pair replaced with a URI link (key: skos_concept_uri) to a segment of a SKOS vocabulary; the entry for the concept of "sea surface temperature". This linking allows a whole set of SKOS data -the entire vocabulary the concept is drawn from -to be associated with the netCDF file containing the link via link look-up ("dereferencing"), rather than just the single textual value of "sea_surface_temperature" which may have further information associated with it which is not discoverable in any standardized way. The link in this example is to data in a demonstration vocabulary that we have built to replicate a small portion of the CF Conventions' [START_REF] Eaton | NetCDF Climate and Forecast (CF) Metadata Conventions[END_REF] terms, with some of our own additions for testing purposes, relating to surface temperature using the SKOS ontology.

The ncskosdump tool

The netCDF specification includes a set of software tools used to manipulate netCDF files [START_REF] Yu | Towards Linked Data Conventions for Delivery of Environmental Data Using netCDF[END_REF]. These tools allow people to view netCDF metadata and data, create netCDF files and view visual representations of netCDF's gridded arrays. Some examples of tools, their developers and their usages are given in Table 1.

Table 1. Several netCDF tools

Name Creators Purpose

ncdump 5Unidata, the makers of netCDF Command-line utility converts netCDF data to human-readable text form (CDL or XML) ncgen 6Unidata A program that creates a netCDF dataset fro CDL input ncview 7Scripps Institution of Oceanography A netCDF visual browser The netCDF Operators toolkit 8A community of developers Command-line programs that take netCDF, HDF, and/or DAP files and derive new data, compute statistics, print or otherwise manipulate them In order to make our simple SKOS-only deployment of netCDF-LD usable by people without detailed knowledge of Linked Data, we have created a prototype Python 9 utility called ncskosdump which wraps the ncdump tool (see Table 1) and adds a series of options for the retrieval and display of Linked Data that the tool is able to extract from a netCDF file, including links to vocabulary terms as described in Section 2.

ncskosdump finds and dereferences SKOS concept URIs to access vocabulary metadata and uses a series of SPARQL queries [START_REF]SPARQL Query Language for RDF[END_REF] on that data to present requested subsets of it to users.

The tool is presented as a Git10 -based code repository online which is catalogued at http://pid.geoscience.gov.au/dataset/103620 (repository version 1.0). That repository contains comprehensive documentation and test code & data for the tool, including all examples used in this paper. The vocabulary used for testing is online at http://pid.geoscience.gov.au/def/voc/netCDF-LD-eg-ToS (version 1.1) and a copy of its content is stored within the repository in the file examples/tos.ttl.

A deployment scenario

A series of small netCDF test files (included in the data directory of the code repository) has been created to test and demonstrate the Linked Data functionality of the ncskos tools, including ncskosdump. One file was created for each valid concept in the test vocabulary, and a "skos__concept_uri" variable attribute in the file set to the URI of the Concept. In addition, one file was created with its "skos__concept_uri" variable attribute value set to an un-resolveable ("non-dereferenceable", in Linked Data jargon), dummy URI; another with a valid global "skos__concept_uri" attribute (as opposed to an attribute of a variable); and yet another with no "skos__concept_uri" global or variable attribute value defined for error case testing.

The ncskosdump command, like its ancestor, ncdump, could be invoked within a script for each netCDF file, and its Common Data Language (CDL) or eXtensible Markup Language (XML) output parsed to infer relationships between concepts. This approach would, however, be extremely inefficient, so Python classes (ConceptHierarchy and NCConceptHierarchy) have been implemented in order to facilitate efficient programmatic handling of large numbers of files and the resolution of their URIs using the same Linked Data mechanisms implemented for ncskosdump.

The script skos_inferencing_demo.py inspects each of the specified netCDF files, resolves its URI(s), where possible, and then displays the file paths and variable names grouped hierarchically by Concept as indented lists, as shown in Figure 3. The Concept hierarchies are inferred from the broader/narrower results of the SKOS queries. The Concepts are cached in memory within a session, so each Concept is resolved only once across multiple files, and cached on persistent storage between sessions by using YAML 11 files of concept retrieval results. By default, the concept hierarchies are populated recursively from each linked Concept all the way up to the Top Concept(s) of the vocabulary to provide the full context of the Concepts in the files. It is also possible to recursively build the full Concept hierarchy downward to the narrowest Concepts by specifying the "--narrower" command line option. Fig. 3. Sample output from skos_inferencing_demo.py showing netCDF files and variables in hierarchical groupings with Polish language labels. Note the fallback to English where Polish prefLabels are not defined.

In order to cater for another practical application of SKOS, the script skos_inferenc-ing_demo.py also takes an optional command line argument "--altlabels=<altLabels>", where <altLabels> is a comma-separated list of case-sensitive altLabels (alternative names for a label) for which netCDF files and variables matching a corresponding or narrower Concept are listed. Sample output is shown in Figure 4.

Current command line options for skos_inferencing_demo.py are as follows:

--verbose to enable verbose output --lang=<lang_code> where <lang_code> is a two-character ISO 639-1:200 code for the language in which the results are sought --narrower to recursively create complete tree of narrower concepts, not just ones resolved directly from URIs --altLabels=<altLabel_list> where <altLabel_list> is a comma-separated list of altLabels to match in order to list their associated datasets --retries=<max_retries> where <max_retries> is the maximum number of retries to attempt for unresolved URIs. Default retries = 0 --delay=<retry_delay_seconds> where <retry_delay_seconds> is the number of seconds to wait before each retry. Default delay = 2s --refresh to discard current file cache and repopulate the cache from scratch Fig. 4. Sample output from skos_inferencing_demo.py showing search results using two altLabels "SST" & "sst". NetCDF files which match the corresponding concept, or its narrower concepts, are shown in hierarchical groupings with Polish language labels (where available).

Consideration of scaling and retrieval strategies

The ncskos Python classes used in the current version of skos_inferencing_demo.py utilize in-memory caching of Concepts retrieved via SKOS queries within a single session and caches whole Concept hierarchies and their information on disk in YAML for use between equivalent sessions (i.e. sessions with the same SKOS options). This means that for most operations, the ncskos Python classes need only resolve (dereference) each Concept URI once which, for large numbers of files with the same URIs, greatly reduces network overheads and thus improves program performance. As with any client-side caching system, care needs to be taken to ensure that cached data is kept consistent with the point of truth (i.e. the source vocabulary online), hence the command matching and the ability for users to refresh the file cache. The current version of skos_inferencing_demo.py also resolves concept URIs recursively as required.

Future Work

Resolution strategy changes are likely with a better understanding of the tool's usage patterns, as indicated above. An alternative approach for efficient URI resolution might be to read all URIs within netCDF files and then resolve the multiple URIs in bulk using a much smaller number of queries. Where a complete concept hierarchy is required and the source(s) is/are resolvable, it may also be better to retrieve the entire concept hierarchy from each relevant vocabulary before matching individual URIs. The decision as to which approach is best for given scenarios cannot be determined until a larger number of community uses of this tool are available for analysis.

Additionally, extensions to the tool's ability to handle more forms of Linked Data encoded in netCDF files will surely be considered. Already the potential for handling multiple URIs per global or variable attribute (as a comma-separated list) has been implemented and tested during development, but was deemed to be problematic at this stage because of the potential for inconsistent or nonsensical user-defined combinations of URIs. This remains an area open to further investigation, since it may be very useful to be able to reference multiple, independent concept URIs if appropriate validation can be undertaken.

Current work being undertaken by the authors and members of a larger "netCDF-LD working group" are looking in to methods of full Linked Data representation within netCDF files, as indicated in [START_REF] Biard | Linking netCDF Data with the Semantic Web -Enhancing Data Discovery Across Domain[END_REF]. Results from this work make prompt changes to SKOS Concept URI representation within netCDF files, perhaps through the use of URI and URI prefix (the "skos__concept_uri" key) aliasing that is more akin to Linked Data conventions.

Conclusions

Metadata "bloat" is a very real issue for many of today's scientific datasets, and it can be difficult to maintain consistency where metadata is replicated within data files. For example, complex hierarchical relationships are difficult to represent in metadata encapsulated within netCDF files and where such information is stored externally to the files, such as the CF Conventions community has done, that information may not be either easily discoverable or machine-readable. Linked Data provides a mechanism which can address these issues.

The simple Linked Data functionality demonstrated in our ncskos implementation can reference much richer metadata for netCDF files in standardized ways, including alternate language representations and the complete broader/narrower context of concepts as required. The attribute containing URIs to SKOS Concepts can coexist with existing netCDF metadata conventions such as the Climate and Forecasting (CF) [START_REF] Eaton | NetCDF Climate and Forecast (CF) Metadata Conventions[END_REF] or Attribute Convention for Data Discovery (ACDD) [START_REF]Category:Attribute Conventions Dataset Discovery[END_REF] so that backwards-compatibility with 'normal' netCDF files and file use can be maintained. In addition, at least some of the well-known conventions (CF) have already been represented in SKOS vocabularies thus we expect that many of those initiatives will come to publish their term lists in ways compatible with this approach.

Our code has been written expressly to handle large numbers of netCDF files with multiple Linked Data concepts, and we are looking to move to real-world implementation in the very near future.

http://pid.geoscience.gov.au/def/voc/

http://vocab.nerc.ac.uk/collection/P07/current/. The same SKOS vocabulary is republished at http://auscope-services-test.arrc.csiro.au/elda-demo/nerc/resource?_view=skos&uri=http://vocab.nerc.ac.uk/collection/P07/current/

Demo vocabulary online at http://pid.geoscience.gov.au/def/voc/netCDF-LD-st-demo

http://www.unidata.ucar.edu/software/netcdf/netcdf-4/newdocs/ncdump-man-1.html

http://www.unidata.ucar.edu/software/netcdf/netcdf-4/newdocs/ncgen-man-1.html

http://meteora.ucsd.edu/~pierce/ncview_home_page.html

http://nco.sourceforge.net/

The Python programming language, v2.7: https://www.python.org/download/releases/2.7/

Git is a distributed version control system: https://git-scm.com/

YAML is a human-readable data serialization language: https://en.wikipedia.org/wiki/YAML

Acknowledgements

The authors thank the members of the netCDF-LD Working Group for their stimulation of work in this area and their thoughts around Linked Data representation in netCDF.

This paper is published with the permission of the CEO, Geoscience Australia.


Lukáš Herman
email: herman.lu@mail.muni.cz

Jan Russnák
email: russnak@mail.muni.cz

Tomáš Řezník
email: tomas.reznik@sci.muni.cz

Flood Modelling and Visualizations of Floods Through 3D Open Data

Keywords: 3D modelling, 3D visualizations, floods, open data

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

Introduction

Nowadays, 3D geospatial data and technologies for 3D cartographic visualization are being used with increasing frequency in a variety of human activities. Crisis management is no different. Applications of 3D geospatial data and technologies can increase, for example, the awareness and understanding of flood-related issues [START_REF] Kubicek | Flood Management and Geoinformation Support within the Emergency Cycle (EU Example)[END_REF][START_REF] Roy | 3D web-based GIS for flood visualization and emergency response[END_REF][START_REF] Van Ackere | Development of a 3D dynamic flood WEB GIS visualisation tool[END_REF]. 3D visualization may be used by experts and laymen alike in the use case described below. 3D visualization also has the ability to assist in dealing with the problem of flooding on different scales -from the global and regional levels to the local level involving individual towns and cities [START_REF] Konecny | Cartography: Challenges and Potentials in Virtual Geographic Environments Era[END_REF]. Feasibility of a 3D visualization of floods purely based on open technologies and data was is the primary aim of this paper. Solely open data and technologies were used for this purpose; based on an analysis of available sources of open data for the case study area (a part of city of Prague, The Czech Republic).

Open data and technologies

"Open Data and content can be freely used, modified, and shared by anyone for any purpose" [START_REF]The Open Definition[END_REF]. This definition was initially derived from the Open Source Definition.

Open data are available without access restrictions, licenses, copyright, patents, or charges for access or re-use. The main reasons, why data should be open, are:

• transparency,

• the release of social and commercial value,

• the release of participation,

• the release of engagement.

Because of the variability in open data, when, for example, one organization deploys PDF files while another deploys raw data, Tim Berners-Lee, the inventor of the Web and Open Data supporter, suggested a 5-star deployment scheme for Open Data. The scheme -from scanned tables in PDF, through XLS and CSV, to data that could be linked to other data (e.g. [START_REF] Palma | An INSPIRE-Based Vocabulary for the Publication of Agricultural Linked Data[END_REF]) -is shown in Fig. 1.

Open geospatial data

There are various projects creating open geospatial data. Precise and detailed geospatial data are not usually open or distributed for free. National mapping agencies or commercial companies usually offer their geospatial data for a fee. Therefore because of such fees, open or free geospatial data are used whenever they are suitable enough. Free data could be licensed (e.g. for limited usage in educational context) unlike Open Data. Open geospatial data have potential uses and applications in various fields such as urban planning, urban studies or 3D city modelling [START_REF] Goetz | Towards generating highly detailed 3D CityGML models from OpenStreetMap[END_REF][START_REF] Long | Transformations of urban studies and planning in the big/open data era: a review[END_REF], transport analysis and navigation [START_REF] Keler | Safety-aware routing for motorised tourists based on open data and VGI[END_REF], precision farming [START_REF] Palma | An INSPIRE-Based Vocabulary for the Publication of Agricultural Linked Data[END_REF][START_REF] Reznik | Monitoring of In-Field Variability for Site Specific Crop Management Through Open Geospatial Information[END_REF], environmental protection [START_REF] Kubasek | Mapping of Illegal Dumps in the Czech Republic -Using a Crowd-Sourcing Approach[END_REF], sociology and socio-economic analysis [START_REF] O'brien | Interactive mapping for large, open demographic data sets using familiar geographical features[END_REF], crime analysis [START_REF] Russnak | Visualization and Spatial Analysis of Police Open Data as a Part of Community Policing in the City of Pardubice (Czech Republic)[END_REF], and also crisis management, which is the topic of this article. Meanwhile, the currently most popular open geospatial data source, Open-StreetMap (OSM), is in the form of a Volunteered Geographic Information (VGI) project created by thousands of contributors, which are not responsible for the content or which cannot be held accountable for the content. OSM data can be both transformed from official sources and manually created by voluntary users. For this reason, OSM data should be used with caution, especially if the issue needs detailed (and precise) input data such as the 3D modelling of cities. Some 3D visualizations or 3D models have been created on the basis of OSM data. A basic extension for 3D modeling is Simple 3D Buildings based on the 3D attributes of buildings. On the basis of the heights of building outlines and building parts, 3D buildings are modeled or, for example, roof types are described. Another 3D application based on OSM data is the open source project Open Earth View. Its aim is full 3D web browsing of a world map. The base ground is built by OSM 2D tiles, which are extended by 3D tiles created essentially from SRTM and other accessible elevation data, or from OSM 2D data attributes like building elevation, number of floors, or roof elevation. For conversion from OSM data into a 3D model, OSM2World can also be used. The OSM-3D project combines OSM data and the SRTM DEM on a virtual globe. OSM-3D is a part of the development of a 3D Geodata Infrastructure for the entire World based on open web service standards of the Open Geospatial Consortium (OGC). The project aims to provide a Web-based interactive 3D view of OSM data. OSM Buildings and F4map are two quite similar projects concerning also the 3D visualization of OSM data. [START_REF]3D development[END_REF][START_REF] Mooney | Has OpenStreetMap a Role in Digital Earth Applications?[END_REF]23,[START_REF]Simple 3D buildings[END_REF]

3D data formats

Both technologies and standards primarily designed for computer graphics and GIS formats are used to save 3D geospatial data. Probably the oldest most widespread format for interactive 3D computer graphics is VRML (Virtual Reality Modelling Language). VRML is a text file format in which 3D geometry can be specified along with surface colour, texture, transparency, and other properties. X3D ( Extensible 3D) is an open standard file format based on XML for the description of spatial scenes. Essentially, it is recognized as the successor to VRML. COLLADA is designed to store 3D graphics; it has similar capabilities to X3D and also the XML structure [START_REF] Behr | X3DOM -A DOM-based HTML5/ X3D Integration Model[END_REF]. There are many other 3D graphics formats besides the above mentioned technologies, and extensions of more conventional (2D) formats, such as 3D PDF, can also be used.

3D spatial objects are also stored through GIS technologies. For example, Multipatch is geometry used in ESRI geodatabases and in Shapefiles to represent 3D features. GML (Geography Markup Language) is an XML grammar defined by the OGC to express geographical features. GML was originally only 2D; however, since version 3.0, it has also been possible to manage 3D data. CityGML is an application schema for GML version 3.1.1 for the storage and exchange of virtual 3D city models. KML (Keyhole Markup Language) is primarily intended for the publication and distribution of geographic data. KML 2.2 was adopted in 2008 as an OGC standard [START_REF] Kolbe | Representing and exchanging 3D city models with CityGML[END_REF].

3D formats can also be classified according to a 5-star scale of data openness. A 3D PDF or Shapefile (Multipatch geometry) can be classified under the 2-star class, other formats like COLLADA, KML, VRML, X3D, GML and CityGML under the 3star class. An important issue for open data formats is their standardization. The most important standardization authority in geoinformatics is OGC. From the above mentioned, technologies standardized by OGC include CityGML, GML and KML. Formats from 3D computer graphics are standardized, for example, by ISO (International Organization for Standardization) or the Khronos Group. ISO standards are VRML and X3D. The Khronos group uses a standardized COLLADA format.

2.3

Freeware and open source software for 3D modelling Software designed primarily for computer graphics, computer-aided drawing (CAD), photogrammetry, or GIS can be used when processing 3D geospatial data. Freely available 3D GIS software include the tools GRASS, LandSerf, 3DEM, and gvSIG with 3D extension. These software packages usually employ simple 3D visualization or 3D spatial analysis (in particular, GRASS). The 3D extension to gvSIG enables, for example, the extrusion of 2D geometry, the symbolisation of point layers, or the setting of stereo visualization. These tools also quite often allow the conversion of selected formats. Certain possibilities of 3D data processing are also provided through plug-ins (e.g. DEMto3D, Shp2D3, and Qgis2threejs) in the widespread open source software QGIS.

Trimble SketchUp, which is used regularly for the 3D modelling of buildings and other objects, is a CAD program working with 3D data. Regarding software for 3D computer graphics, commercial programs are often used. However, there are also the freeware applications MeshLab and View3DScene as well as the open source tools Blender and Wings 3D. The individual aforementioned applications are often combined in the process of creating 3D models. The key factor is then support for formats that are used to transfer data between applications. In these connections, it is appropriate to use standards (all formats specified in the previous section are standards). Price is also an important aspect when selecting 3D software, in addition to its functionality. In this study, we focus on non-commercially distributed applications (freeware and open source).

3D rendering in a web browser

A broad spectrum of technologies is also available for rendering on the Web. In addition to thick clients, which are standalone applications such as Google Earth, there are a number of techniques for displaying 3D data using a simple web browser. Many of them use plug-ins. 3D models can be displayed in a web environment using Flash plug-in, version 11.0 or higher, or Microsoft Silverlight technology, version 3.0 or higher [START_REF] Behr | X3DOM -A DOM-based HTML5/ X3D Integration Model[END_REF]. Plug-ins are also used for the implementation of virtual globes, for example NASA World Wind. Today, preference is given to technologies built on HTML5 and JavaScript library WebGL. These are, for example, SpiderGL, Three.js, XML3D and X3DOM. SpiderGL library is based on the COLLADA file structure [START_REF] Di Benedetto | SpiderGL: A Graphics Library for 3D Web Applications[END_REF]. Three.js was used for the implementation of 3D city model visualization by [START_REF] Wendel | Development of a Web-Browser Based Interface for 3D Data -A Case Study of a Plug-in Free Approach for Visualizing Energy Modelling Results[END_REF]. Practical aspects of the use of the X3DOM library for the 3D visualization of detailed models of cities are described, for example, by [START_REF] Herman | 3D Web Visualization of Environmental Information -Integration of Heterogeneous Data Sources when Providing Navigation and Interaction[END_REF]. In addition to general 3D libraries, there are also a number of 3D virtual globes, e.g. OpenWebGlobe [START_REF] Netek | OpenWebGlobe -Virtual Globe in Web Browser[END_REF].

Pilot study

The data and technologies described in this paper were tested in the area around the Vltava River in Prague, the Czech Republic. The floods in 2002 were the most destructive floods in the history of Prague. The flow of the Vltava River culminated at 5300 m³/s. About 40 000 people were evacuated from immediate vicinity. The area around the Vltava was flooded again in 2013 (culmination at 3 200 m 3 /s).

Open geospatial data in the Czech Republic

The Registry of Territorial Identification, Addresses and Real Estate (RUIAN) in the Czech Republic became a freely available online resource for geospatial data in 2012. Among other data and attributes, this registry also contains information about number of floors per each building. On the basis of this information, 3D models can be created from building footprints. The dataset is downloadable and includes also address points, streets, parcels, administrative units, and electoral districts. [27] There are also detail open data for city areas provided in the Czech Republic, e.g. Děčín, Plzeň and, above all, Prague. The Prague Institute of Planning and Develop-ment (IPR) manages and provides open data -specifically, open data for various purposes and analyses connected, for example, with power distribution, noise mapping, the climate, the environment, and flood protection. In addition to this kind of data, the datasets include 3D buildings, 3D towers, absolute and relative building heights, digital terrain models (DTM), or digital surface models (DSM). Similar data for 3D city models are also provided for cities around the world, e.g. Berlin, Hamburg, Montreal, Rotterdam and New York. [START_REF]IPR Praha -Institut plánování a rozvoje hlavního města Prahy[END_REF][START_REF]Open Data Initiatives[END_REF]

Input data

The following were used as input data for the creation of 3D models:

• The digital terrain model of the city Prague provided by IPR in TIFF format. This DTM was created from aerial photography in 2010 and designed for the level of detail commensurate with a map scale of 1:5 000. • Building footprints from RUIAN that are available on-line as a Web Feature Service or as off-the-shelf GML files (data are compressed by the GZIP algorithm). • Flood areas for various water levels (for 5-, 20-and 100-year floods) provided by IPR as Shapefiles with Polygon geometry. Alternatively, Shapefiles for identical water levels from the DIBAVOD (Digital Base of Water Management Data) database can also be used, but these do not have a clearly defined license. • Flood walls provided by IPR as Shapefiles with Polyline geometry.

• Orthophoto Prague 2015 with a pixel resolution of 50 cm and in true colors. Data acquisition took place in August 2015. Orthophoto is stored in TIFF format. • POI (Points of Interest) from OSM downloaded from Geofabrik Tools. We used POI as a Shapefile with Point geometry.

Processing of open data

These input data were processed in QGIS software (version 2.12) with the same plugins which are intended for the processing and visualization of 3D data. The QGIS program enables the use of functions important for 3D data pre-processing (interpolation and other techniques of virtual surfaces creation). Available 3D plug-ins for QGIS are: Shp2D3, CZML Generator, and Qgis2threejs. Shp2D3 transforms 2D vector data to 3D by the sampling of an elevation raster map. It works actually only with polylines. CZML Generator serves to export to form ready for virtual globe (for Cesium library). Qgis2threejs exports terrain data, map canvas images, and vector data to a web browser which supports WebGL. This plug-in uses the Three.js library; thus, in this study, final visualization was implemented through this library.

In this pilot study (see Fig. 2), we used mainly Qgis2threejs for visualization in a web browser. All data are loaded into QGIS as GML, Shapefiles, and TIFF files. Additional layer settings (e. g. color scales for terrain) can be set through QGIS. Other parameters of final visualization, like template of final web page, controls, and other parameters of individual layers (e.g. exaggeration, display of labels, transparency, background color, resampling of raster) are defined in the scope of the Qgis2threejs plug-in.

Discussions

3D visualization in general has significant application potential, but it is affected by its properties, e.g. the UI (User Interface) or cartographic visualization methods. Regarding cartographic visualization, our approach is not directly dependent on the visualization(s) of underlying data. Cartographic visualization should be developed separately for each application and is not directly re-distributable since it is written directly in the Three.js code. We may define, for example, a standardized cartographic visualization for flood areas, flood walls, as well as POI using the developed software as shown in this paper. [START_REF] Konecny | The usability of selected base maps for crises management -Users' perspectives[END_REF][START_REF] Van Ackere | Development of a 3D dynamic flood WEB GIS visualisation tool[END_REF] have focused on this issue. The usability aspects of concrete UI are also important topics for future research. Some authors, e.g. [START_REF] Vozenilek | Cartography for GIS: Geovisualization and Map Communication[END_REF], argue that 3D visualization is able to present geospatial data to wider audiences, including those with little or no cartographical or GIS experience. On the other hand, results of other previous studies [START_REF] Herman | Comparison of User Performance with Interactive and Static 3D Visualization -Pilot Study[END_REF][START_REF] Jurik | When the display matters: A multifaceted perspective on 3D geovisualizations[END_REF][START_REF] Sprinarova | Human-computer Interaction in Real 3D and Pseudo-3D Cartographic Visualization: A Comparative Study[END_REF] suggest that interactive 3D visualization will be more useful for users with previous experience with 3D visualization and for complex tasks in particular. It would be appropriate to validate created application through user testing.

When we focused on technological issues, we found that using Multipatch geometry for storing 3D open geodata limits the possibilities of 3D modeling in open source software which does not support this geometry (unlike Shapefiles with Point, Polyline or Polygon geometries). Multipatch geometry is supported only in commercial software (ESRI ArcGIS and FME), but not in QGIS or gvSIG. Buildings at the level of detail 1 (LoD 1) are modeled in the resulting application only. Web applications built with Three.js could be further tested in terms of capacity, availability and support in different Web browsers. Deployment in a form of daily service should be made after validations of above mentioned technological parameters.

Conclusion

The presented proof-of-concept application was established in order to demonstrate the possibility of open web-based 3D visualization. It should be emphasized that the openness relies on the data and software used, as well as on the final application. The developed proof-of -concept application is freely available to interested persons under a BSD license.

To sum up, the following major advantages of 3D visualization based on open data and Three.js library were identified:

• it represents (in this pilot study) an example of an open data application usable in flood impact analysis and crisis management; • it enables user-friendly interactive 3D visualization, which is accessible to a broad spectrum of users (from the general public to experts); • it does not require any new software or plug-ins to be installed on the client or server sides;

We also identified some limitations of available open source tools. The noncommercial software functionality needed for the creation of 3D visualizations is generally sufficient because, inter alia, programs primarily used in computer graphics can also be used for this purpose. Less commonly available is non-commercial software support for 3D analytical tools (3D equivalents of overlay algebra functions, calculations of surfaces or volumes of 3D elements). We must also mention recommendations for the formats in which open ends are provided for 3D geospatial data. Multipatch geometry, which is used in the case of Prague open data for 3D models of buildings at the level of detail 2 (LoD 2), is not supported in open source tools such as QGIS. It is more suitable to transform these data into a standardized format like CityGML or into GML files formatted according to INSPIRE (INfrastructure for SPatial InfoRmation in Europe) data theme Buildings.

Fig. 1 .

 1 Fig. 1. 5-star schema of Open Data openness (source: [2])

Fig. 2 .

 2 Fig. 2. Example of a web application for the 3D visualization of floods.

Fig. 3 .

 3 Fig. 3. Interactive functionality of developed application (left -setting parameters of layers, right -"cut of plane")

  In the following paragraphs we present examples of various sources of open geospatial data, with an emphasis on data suitable for 3D modelling Open geospatial data, or open data in general, are often collected, managed, and then made available by an institution, organization or government. These providers should be responsible for the content. According to the provider, these open data can be termed open government data. The same should also hold for open geospatial data. The most commonly used freely available elevation data sets are derived from the SRTM (Shuttle Radar Topography Mission) and the ASTER GDEM (Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model). SRTM DEM (Digital Elevation Model) is the result of a project spearheaded by NASA and NGA (National Geospatial-Intelligence Agency).The pixel size of the raw image represents 30m over the USA and Australia. For the rest of the world, the ground resolution is 90m. The elevation models obtained from SRTM data are freely available from the Internet. Up to 2009, SRTM DEM was the most complete highresolution digital topographic database of the Earth. In 2009, the ASTER GDEM was released. Compared to SRTM DEM, ASTER GDEM covers a larger area of the Earth and the resolution is 30m worldwide. By the end of 2015, all of the highest-resolution data generated from SRTM were also released.[3,[START_REF]Shuttle Radar Topography Mission[END_REF][START_REF]SRTM DEM[END_REF][START_REF] Vijith | Comparison and Suitability of SRTM and ASTER Digital Elevation Data for Terrain Analysis and Geomorphometric Parameters: Case Study of Sungai Patah Subwatershed (Baram River, Sarawak, Malaysia)[END_REF]] EU-DEM is another similar dataset for the pan European region, which is provided by the European Environmental Agency. For the European level, EU-DEM provides downloadable colorshaded relief data with values relating to actual elevations. The EU-DEM is a hybrid product based on SRTM and ASTER GDEM data fused by a weighted averaging approach. The resolution of EU-DEM is 25m.[5]

Acknowledgements. This research was funded by Grant No. MUNI/A/1419/2016, "Integrated research on environmental changes in the landscape sphere of Earth II" and Grant No. MUNI/M/0846/2015, "Influence of cartographic visualization methods on the success of solving practical and educational spatial tasks", both awarded by Masaryk University, Czech Republic.


Jiří Hřebíček
email: hrebicek@iba.muni.cz

Jiří Kalina
email: kalina@mail

Jana Soukopová
email: soukopova@econ.muni.cz

Eva Horáková

Jan Prášek

Jiří Valta
email: jiri.valta@cenia.cz

Modelling and forecasting waste generation -DECWASTE information system

Keywords: waste generation, waste categories, forecasting, modelling, DPSIR, multi-linear regression models

The DECWASTE forecasting waste generation information system is presented. It is based on Annexes I, II and III of Regulation (EC) No 849/2010 amending Regulation (EC) No 2150/2002 of the European Parliament and of the European Council on waste (WSR). DECWASTE forecasts the quantity of waste generated for each waste category listed in Section 2(1) of Annex I of the WSR at the Czech national level. Its multi-linear regression forecasting model is based on environmental as well as economic and social predictors. These models use historical data of the waste information system (ISOH) of the Czech Environmental Information Agency and sets of indicators (predictors) integrated into forecasting models. The methodology consisted in adjusting predictors of the forecasting models into a Driving Force-Pressure-State-Impact-Response (DPSIR) framework and their sensitivity analysis enables their choice into forecasting models and their verification using appropriate data. DECWASTE supports decisions made by the Ministry of the Environment to improve the implementation of the national Waste Management Plan.

Introduction

Making decisions in waste management is not only very capital-intensive, but also difficult from the environmental, economic and social points of view. There is a need to develop, master and implement simple but reliable models and application software that will help decision-makers in public administration (PA) to analyse waste manage-ment processes, follow national and European Union (EU) legislation, the implementation of the Waste Management Plan (WMP) for the respective PA level [START_REF]Preparing a Waste Management Plan[END_REF] and to consider also EU Circular Economy tasks [START_REF]Closing the loop -An EU action plan for the Circular Economy[END_REF]. Therefore, we have developed an improved version of the information system, DECWASTE, forecasting the quantity of waste generated for each waste category listed in Section 2(1) of Annex I of Regulation (EC) No 849/2010 amending Regulation (EC) No 2150/2002 of the European Parliament and of the Council on waste (WSR). We have updated the previous information system, DECWASTE [START_REF] Hřebíček | Decision support system for waste management[END_REF], forecasting the quantity of waste generated for each category of waste on the European list of waste (LoW) established by the Commission Decision 2000/532/EC.

The new DECWASTE can forecast these quantities for the prescribed years 2016-2025. It uses linked open data of the Czech eGovernment system, i.e. historical waste data (2009)(2010)(2011)(2012)(2013)(2014)(2015) of the waste information system (ISOH) [START_REF]Waste Management Information System[END_REF] of the Czech Environmental Information Agency (CENIA) at the national level of Czech Republic and transforms them into quantities of waste categories of the WSR. It downloads and parses linked open data to populate appropriate sets of indicators (predictors) for the forecasting models. The predictors are then used in the Driving Force-Pressure-State-Impact-Response (DPSIR) framework [START_REF] Liao | Identification of the driving force of waste generation using a high-resolution waste input-output table[END_REF] which also integrates linked open data from the Czech eGovernment system [START_REF] Soukopová | National Environmental Data Facilities and Services of the Czech Republic and Their Use in Environmental Economics[END_REF] and from the Czech Statistical Office (CSO) [7].

DPSIR analysis was carried out by Delphi method [START_REF] Seker | Computerized Argument Delphi Technique[END_REF] undertaken by the partnership on the basis of an assessment of the underlying data and searching for causal links of waste category production to the management of "driving force" (economic sectors of human activity), through the "pressures" (emissions, waste), on the "states" (physical, chemical and biological) and "impacts" on ecosystems, human health and function, that eventually lead to political "responses" (the setting of priorities of waste management, setting goals of the WMP and its indicators [START_REF]Preparing a Waste Management Plan[END_REF]), see [START_REF] Kalina | Case study: Prognostic model of Czech municipal waste production and treatment[END_REF][START_REF] Hřebíček | The Forecasting Waste Generation Model based on Linked Open Data and the DPSIR Framework. Case study concerning municipal waste in the Czech Republic[END_REF].

The Delphi method was carried out with the assistance of a chosen expert group of finding solutions to the survey group experts' opinions. The group of experts carried out, independently of each other, predictors designated mediator were summarized, also distributed for the next round. Use of standardized questionnaires and the procedure was repeated until the approximate match.

Each group was assessed from the perspective of the individual parts of the DPSIR cycles. From the perspective of the production of waste category and the environment impacts we understood the individual parts of the DPSIR cycle, the following [START_REF] Liao | Identification of the driving force of waste generation using a high-resolution waste input-output table[END_REF]:

• Driving forces are human activities or activities caused by the lifestyle of society.

They lead to pressures on natural resources and the environment, which disrupt the ecological stability and impair the quality of the environment (e.g., emissions and waste).

• State is what is usually measured in the environment: waste category generation, the quality of water, soil, air and the environment (nature), energy and material flows. • Pressures and states effect impacts: health problems, change in ecosystems, e invasion of alien species etc. • Responses are responses of society to the identified problems in the form of specific measures (e.g. legislation).

The result of the implementation of DPSIR analysis was the description of the predictors of the individual parts of the DPSIR cycle, including identification of driving forces of the 51 waste categories of the WSR, expressed as the time series input data for calculating the forecast model [START_REF] Horáková | Vyhodnocení DPSIR rámce pro vybrané skupiny odpadů stanovené výzvou TB940MZP003[END_REF]. The most relevant predictors were chosen through a selection process that included experts' opinions and literature reviews based on the relevance and applicability to different waste categories of WSR settings [START_REF] Hřebíček | The Forecasting Waste Generation Model based on Linked Open Data and the DPSIR Framework. Case study concerning municipal waste in the Czech Republic[END_REF].

A verified multilinear regression model is presented. The construction of the forecasting model consisted in the construction and definition of the predictors based on the DPSIR framework; a sensitivity analysis of the models; implementation of the forecasting models. The presented results follow the authors' results from the iEMSS 2014 [START_REF] Kalina | Case study: Prognostic model of Czech municipal waste production and treatment[END_REF] and iEMSS 2016 [START_REF] Hřebíček | Decision support system for waste management[END_REF] conferences.

Material and Methods

The development of the new information system DECWASTE includes the following consequent steps:

1. Identification of each waste category listed in Section 2(1) (51 items) and waste generating activity listed in Section 8(1) (19 items) of Annex I of the WSR and development of computation formulae for their amounts of using the predictors from the DPSIR framework analyses. 5. Forecasting of predictors from the DPSIR framework and the calculation of waste category forecasts. 6. Processing of sensitivity analyses of predictors of waste category generation models. 7. Visualization scenarios forecasting the quantity of waste categories for the prescribed period 2016-2025.

We briefly describe the above steps. Firstly, we implemented simple meta-computation formulae in DECWASTE using the Table of Equivalence of Annex III of the WSR between the EWC-Stat Rev 4 and the LoW, which enabled us to calculate the amount of every item of waste category of the WSR from items in the LoW.

Data sets of the ISOH keep the quantity of waste generation and treatment by generators and data from facilities to treat, recover and dispose of waste [START_REF] Hřebíček | Decision support system for waste management[END_REF][START_REF] Kalina | Case study: Prognostic model of Czech municipal waste production and treatment[END_REF] from 2009-2015. Every year, the ISOH records more than 70,000 different generators' reports in all 6,500 municipalities of the Czech Republic and more than 3,000 facilities' reports. The annual ISOH database contains more than 50,000 records of municipal waste generation and 10,000 records concerning their treatment. The database is available to the public for the years 2009-2015 and we calculated all appropriate waste categories for these years.

The Czech eGovernment systems provide sources of linked open data of necessary input data for the DPSIR framework predictors for different waste categories and activities [START_REF] Liao | Identification of the driving force of waste generation using a high-resolution waste input-output table[END_REF][START_REF] Soukopová | National Environmental Data Facilities and Services of the Czech Republic and Their Use in Environmental Economics[END_REF][7][START_REF] Kalina | Case study: Prognostic model of Czech municipal waste production and treatment[END_REF]. Parsing of the linked open data of eGovernment systems is divided in the DECWASTE information system into five phases [START_REF] Kalina | Case study: Prognostic model of Czech municipal waste production and treatment[END_REF]: definition of the appropriate data; data export; data processing; data import and optimization.

Identification and development of socioeconomic and demographic predictors based on the DPSIR framework [START_REF] Liao | Identification of the driving force of waste generation using a high-resolution waste input-output table[END_REF] of the third step have been described [START_REF] Hřebíček | Decision support system for waste management[END_REF][START_REF] Hřebíček | The Forecasting Waste Generation Model based on Linked Open Data and the DPSIR Framework. Case study concerning municipal waste in the Czech Republic[END_REF]. In the next section, we discuss the fourth step.

Development of a multi-linear regression model of waste generation based on DPSIR

We need to predict the amount of waste generated on the national level of the Czech Republic for the given year t and the chosen waste category 𝑤𝑤 𝑓𝑓 (𝑡𝑡) of the WSR f = 1,…,51. We have developed mathematical forecast models based on the DPSIR framework [START_REF] Liao | Identification of the driving force of waste generation using a high-resolution waste input-output table[END_REF] predictors according to the work of [START_REF] Kalina | Case study: Prognostic model of Czech municipal waste production and treatment[END_REF][START_REF] Hřebíček | The Forecasting Waste Generation Model based on Linked Open Data and the DPSIR Framework. Case study concerning municipal waste in the Czech Republic[END_REF]. They use available linked open data on DPSIR predictors from eGovernment systems [START_REF]Waste Management Information System[END_REF] and data of past waste generation from the ISOH (2009 -2015).

Let us assume that for a given waste category f, the amount of waste ŵ 𝑡𝑡 𝑓𝑓 and predictors  𝑖𝑖,𝑡𝑡 𝑓𝑓 , i=1,…, K f ; in years t=2009,…,2015 are known, where K f is the number of predictors for the waste category f of the WSR. Let waste category generation 𝑤𝑤 𝑓𝑓 (𝑡𝑡) at the given year t fulfil the equation

log(𝑤𝑤 𝑓𝑓 (𝑡𝑡)) = 𝑎𝑎 0 𝑓𝑓 + ∑ 𝑎𝑎 𝑖𝑖 𝑓𝑓 𝐾𝐾 𝑓𝑓 𝑖𝑖=1 • log�𝐴𝐴 𝑖𝑖 𝑓𝑓 (𝑡𝑡)� + 𝜀𝜀 𝑡𝑡 𝑓𝑓 , (1)
where will be more accurate and the approximation error 𝜀𝜀 𝑡𝑡 𝑓𝑓,𝑝𝑝𝑝𝑝 will be smaller.

• 𝐴𝐴 𝑖𝑖 𝑓𝑓 (𝑡𝑡), i=1,…,K f are
Furthermore, we assume that the predictors 𝐴𝐴 𝑖𝑖 𝑓𝑓 (𝑡𝑡), i=1,…,K f , for t=2016,…,2025

have either known values (e.g. GDP, population, household consumption, etc.) from the eGovernment systems [START_REF] Soukopová | National Environmental Data Facilities and Services of the Czech Republic and Their Use in Environmental Economics[END_REF]7] or are determined by an appropriate extrapolation method or are chosen by decision makers using DECWASTE. These models are implemented in DECWASTE, written in language R and they use predefined predictors  𝑖𝑖,𝑡𝑡 𝑓𝑓 , which were parsed from linked open data [START_REF] Soukopová | National Environmental Data Facilities and Services of the Czech Republic and Their Use in Environmental Economics[END_REF]7]. The outputs 𝑤𝑤 𝑓𝑓 (𝑡𝑡) are time series of the amount of waste generated for the years t=2016,…,2025 of waste category f. An estimate of the waste category generation 𝑤𝑤 𝑓𝑓 (𝑡𝑡) can be expressed after treatment (1) as

𝑤𝑤 𝑓𝑓 (𝑡𝑡) = 𝐴𝐴 0 𝑓𝑓 • ∏ 𝐴𝐴 𝑖𝑖 𝑓𝑓 (𝑡𝑡) 𝑎𝑎 𝑖𝑖 𝑓𝑓 𝐾𝐾 𝑓𝑓 𝑖𝑖=1 , (2)
where 𝐴𝐴 0 𝑓𝑓 = exp( 𝑎𝑎 0 𝑓𝑓 ), while we have neglected the error 𝜀𝜀 𝑡𝑡 𝑓𝑓 .

2.2

The sensitivity of estimate of waste generation with respect to the statistical significance of predictors.

The local sensitivity of the model ( 1), ( 2) for the i th predictor 𝐴𝐴 𝑖𝑖 𝑓𝑓 (𝑡𝑡), in the year t and waste category f can be estimated using the partial derivatives of the waste category 𝑤𝑤 𝑓𝑓 (𝑡𝑡) in (2) according to the i th predictor 𝐴𝐴 𝑖𝑖 𝑓𝑓 (𝑡𝑡):

𝑎𝑎 𝑖𝑖 𝑓𝑓 • 𝑤𝑤 𝑓𝑓 (𝑡𝑡) 𝐴𝐴 𝑖𝑖 𝑓𝑓 (𝑡𝑡) . ( 3
)
It follows from (3) that if the value of predictor 𝐴𝐴 𝑖𝑖 𝑓𝑓 (𝑡𝑡) is increased by 1 percent then the amount of waste 𝑤𝑤 𝑓𝑓 (𝑡𝑡) in waste category f will increase or decrease by 𝑎𝑎 𝑖𝑖 𝑓𝑓 percent, if 𝑎𝑎 𝑖𝑖 𝑓𝑓 > 0 or 𝑎𝑎 𝑖𝑖 𝑓𝑓 < 0, for i=1,…,K f . This knowledge is important for users of the model. We continue in the further analysis of the developed model, i.e. assessment of the statistical significance of predictors. The predictors 𝐴𝐴 𝑖𝑖 𝑓𝑓 (𝑡𝑡), i=1,…,K f in (1), (2) were determined for each waste category f on the basis of the analysis of the DPSIR framework and experts' assessment. Therefore, we will conduct an assessment of their statistical significance in the multiple linear model (1) depending on the input values of the predictors in the years 2009 to 2015. Firstly, we consider in the model (1) all predictors 𝐴𝐴 𝑗𝑗 𝑓𝑓 (𝑡𝑡), j=1,…,K f in (1), in the waste category f where we calculate its sample variance, i.e.:

𝑠𝑠 2 = (∑ (𝜀𝜀 2015 𝑡𝑡=2009 𝑡𝑡 𝑓𝑓 ) 2 )/(𝐾𝐾 𝑓𝑓 -1). (4)
The statistical significance of each predictor 𝐴𝐴 𝑖𝑖 𝑓𝑓 (𝑡𝑡) is calculated using the F-test of the difference of two sample variances, i.e. the sample variance s of the model ( 1) with all predictors and sample variance si of the model [START_REF]Preparing a Waste Management Plan[END_REF] where the i th predictor 𝐴𝐴 𝑖𝑖 𝑓𝑓 (𝑡𝑡) is excluded and the rest of the predictors, i.e. K f -1, are recalculated and its sample variance is calculated

𝑠𝑠 𝑖𝑖 2 = (∑ (𝜀𝜀 2015 𝑡𝑡=2009 𝑡𝑡,𝑖𝑖 𝑓𝑓 ) 2 )/(𝐾𝐾 𝑓𝑓 -2) , (5)
Where 𝜀𝜀 𝑡𝑡,𝑖𝑖 𝑓𝑓 is the approximation error of multiple linear regression of the model [START_REF]Preparing a Waste Management Plan[END_REF],

where the i th predictor was excluded.

Then we set the number of degrees of freedom for both samples: n1 = K f -1 (for s 2 ) and n2 = K f -2 (for si 2 ) and compute the value of the test criteria (statistics) Fi: Fi = the greater of (s 2 , si 2 ) / the smaller of the (s 2 , si 2 ), ( 6) that has the Fischer probability density distribution

𝐻𝐻(𝑥𝑥) = 𝛤𝛤( 𝑛𝑛 1 +𝑛𝑛 2 2 ) 𝛤𝛤( 𝑛𝑛 1
2

)•𝛤𝛤( 𝑛𝑛 2 2
)

• 𝑛𝑛 1 𝑛𝑛 1 2 • 𝑛𝑛 2 𝑛𝑛 2 2 • 𝑥𝑥 𝑛𝑛 1 -2 2
(𝑛𝑛 2 +𝑛𝑛 1 •𝑥𝑥) - 𝑛𝑛 1 +𝑛𝑛 2 2 •, (7)
for x> 0 and is equal to 0 for x ≤ 0.

We use statistical software where it is more common to calculate the test p-value, which we denote as pi. This is the smallest level of the F-test in which we would reject the hypothesis H0: {s 2 = si 2 }. We set this value as pi = 1-H (Fi).

This procedure is repeated gradually for other predictors 𝐴𝐴 𝑖𝑖 𝑓𝑓 (𝑡𝑡), for which we calculate Fi statistics and the test p-values pi, i = 2, ..., K f . Denote V = {𝐹𝐹 𝑖𝑖 , 𝑝𝑝 𝑖𝑖 , 𝑠𝑠 𝑖𝑖 } 𝑖𝑖=1 𝐾𝐾 𝑓𝑓 the set of predictor significance. We can now simplify the model ( 1) and exclude non-significant predictors.

Let us choose the level of significance α (values 0.05 or 0.1 are usually selected) of the predictors. We calculate p-values pi ,i = 1,…,K f and compare them with this level of significance α:

• If pi> α => the null hypothesis H0: s 2 = si 2 is rejected. Conclusion: the variances of different models are statistically significant and the i th predictor 𝐴𝐴 𝑖𝑖 𝑓𝑓 (𝑡𝑡)is significant. • If pi< α => we cannot reject the hypothesis H0. Conclusion: the variances of both models are not statistically significantly different (i.e., the selections originated from the same basic model with the common variance s 2 ) and the i th predictor is not significant. In this way we can simplify the model ( 1), (2) if we exclude insignificant predictors 𝐴𝐴 𝑖𝑖 𝑓𝑓 (𝑡𝑡), which we originally selected on the basis of the DPSIR analysis. In the developed model, only the statistically significant predictors 𝐴𝐴 𝑖𝑖 𝑓𝑓 (𝑡𝑡) then remain. For some waste category f, however, it may happen that the original proposed predictors based on the DPSIR analysis do not remain in any model [START_REF]Preparing a Waste Management Plan[END_REF]. The resulting forecast p of waste category generation is then constant and independent of the predicted values of the predictors of the future. In this case we can only reduce the statistical significance level α, i.e. reliability of the developed model and accept a greater risk of erroneous predictions.

Extrapolation of the predictors

Let us suppose that for each waste category f the amount of waste category generation ŵ 𝑡𝑡 𝑓𝑓 and predictors  𝑖𝑖,𝑡𝑡 𝑓𝑓 , i=1,…,K f ; t=2009,…,2015 are known and we have calculated for each waste category f the coefficients a0 f ,…, aK f in the model (1) by using multiple regression.

For the calculation of the forecast waste category generation 𝑤𝑤 𝑓𝑓 (𝑡𝑡) in the years t=2016,…,2025 it is necessary to know the values of predictors 𝐴𝐴 𝑖𝑖 𝑓𝑓 (𝑡𝑡), i=1,…, K f , t=2016,…,2025. These values, however, may not always be listed in the sources (linked open data in the eGovernment systems) from which we draw the data predictors  𝑖𝑖,𝑡𝑡 𝑓𝑓 , i=1,…,K f ; t=2009,…,2015. In this case, the procedure is as follows:

• Enter the values of the predictors based on experts' estimates or other appropriate sources; • On the basis of the values of the predictors  𝑖𝑖,𝑡𝑡 𝑓𝑓 , i=1,…,K f ; t=2009,…,2015 the values of predictors 𝐴𝐴 𝑖𝑖 𝑓𝑓 (𝑡𝑡),i=1,…, K f , t=2016,…,2025 are calculated using either linear or exponential extrapolation.

Modelling measures in waste management and scenarios

The developed models ( 1), ( 2) of forecast waste generation 𝑤𝑤 𝑓𝑓 (𝑡𝑡) for the waste category f should also reflect the trends in changes in this waste category as a result of the anticipated N measures oj, j = 1,…,N (WMP, EU and national legislative changes etc.), which will have an impact on the given waste category f in the year t.

Therefore, we introduce the function P f (t,o1,o2,…,oN) in the form:

𝑃𝑃 𝑓𝑓 (𝑡𝑡, 𝑜𝑜 1 , 𝑜𝑜 2 , … , 𝑜𝑜 𝑁𝑁 ) = ∏ (1 - 𝑃𝑃 𝑗𝑗 𝑓𝑓 (𝑡𝑡) 100 ) 𝑁𝑁 𝑗𝑗=1 , (8)
where functions 𝑃𝑃 𝑗𝑗 𝑓𝑓 (𝑡𝑡) are time-dependent functions of the impact of given measures (e.g. prevention, kind of collection, permitted treatment, delivery distance to waste facilities, etc.) on the waste category f in the given year t (generally in the whole waste management of the Czech Republic) and for simplicity, we assume that 0 ≤ 𝑃𝑃 𝑗𝑗 𝑓𝑓 (𝑡𝑡) < 100, j = 1,…,N; t = 2016, …, 2025.

The functions𝑃𝑃 𝑗𝑗 𝑓𝑓 (𝑡𝑡) for the year t are set as a percentage which should be achieved in years t = 2015, …,2025, as a result of the measures oj (on the basis of the WMP, WPP or other strategic documents). For the period t = 2009,…,2015, zero values of these functions are considered. In most of the waste categories f simple function values 𝑃𝑃 𝑗𝑗 𝑓𝑓 (𝑡𝑡) for the given year t = 2016,…,2025 will not be provided, therefore, they will be estimated on the basis of expert assessment. If it is not possible to set them realistically, we consider them to be equal to 0. The forecast of the waste generation 𝑤𝑤 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑛𝑛𝑝𝑝𝑝𝑝𝑖𝑖𝑝𝑝 𝑓𝑓 (𝑡𝑡) for the given waste category flet us consider now as the product of the function 𝑃𝑃 𝑓𝑓 (𝑡𝑡, 𝑜𝑜 1 , 𝑜𝑜 2 , … , 𝑜𝑜 𝑁𝑁 ) and estimated waste category generation 𝑤𝑤 𝑓𝑓 (𝑡𝑡) of (2):

𝑤𝑤 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑛𝑛𝑝𝑝𝑝𝑝𝑖𝑖𝑝𝑝 𝑓𝑓 (𝑡𝑡) = ∏ (1 - 𝑃𝑃 𝑗𝑗 𝑓𝑓 (𝑡𝑡) 100 ) 𝑁𝑁 𝑗𝑗=1 • 𝐴𝐴 0 𝑓𝑓 • ∏ 𝐴𝐴 𝑖𝑖 𝑓𝑓 (𝑡𝑡) 𝑎𝑎 𝑖𝑖 𝑓𝑓 𝐾𝐾 𝑓𝑓 𝑖𝑖=1
(10) To compute logarithms [START_REF] Seker | Computerized Argument Delphi Technique[END_REF] we obtain the resulting equation of our developed model for the forecast of the waste category generation f in the year t = 2016, …, 2025:

log (𝑤𝑤 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑛𝑛𝑝𝑝𝑝𝑝𝑖𝑖𝑝𝑝 𝑓𝑓 (𝑡𝑡)) =𝑎𝑎 0 𝑓𝑓 + ∑ 𝑎𝑎 𝑖𝑖 𝑓𝑓 𝐾𝐾 𝑓𝑓 𝑖𝑖=1 log�𝐴𝐴 𝑖𝑖 𝑓𝑓 (𝑡𝑡)� + ∑ log(1 - 𝑃𝑃 𝑗𝑗 𝑓𝑓 (𝑡𝑡) 100 ) 𝑁𝑁 𝑗𝑗=1 . ( 11
)
The

Results and Discussion

The DECWASTE information system enables the outputs of the above forecasting models for 51 waste categories listed in Section 2(1) of Annex I of Regulation (EC) No 849/2010. We present this for forecasting the household waste category.

Household waste forecast generation analysis outputs

Firstly, we describe the process of development of DPSIR analysis and the choice of predictors for the household waste category [START_REF] Horáková | Vyhodnocení DPSIR rámce pro vybrané skupiny odpadů stanovené výzvou TB940MZP003[END_REF].

The basic driving force behind the production of household waste is the size of the population, which is also significantly affected by urbanization of the population, when residents with higher incomes and stronger consumer behaviour live in cities and their surroundings. High population density together with higher purchasing power also limits their own waste management options (e.g. composting) and creates requirements for faster replacement of goods, which affects household consumption and composition of the consumer basket. The driving force for the production of municipal waste is also the age structure of the population and the amount of the unemployment benefit, when families with small children, students, pensioners and partially unemployed remain throughout the day near the residence where their activities produce waste.

The main pressure influencing the amount of household waste generation is environmental education and enlightenment in the prevention of and management of household waste.

The state of household waste generation is annually evaluated by indicators of the WMP [START_REF]Preparing a Waste Management Plan[END_REF], data from information systems of the MoE relating to the amount of emissions/imissions from energy recovery waste, the landfilling of waste, the status of surface water and groundwater etc.

The impacts of the generation and treatment of household waste on the environment have not yet manifested themselves to distort the landscape; mainly landfills, incinerators and industrial areas with waste treatment plants, the potential threat to groundwater and surface water and air pollution, etc. Furthermore, collection and transport of household waste and pollution in the case of collection containers, etc.

The response to generated household waste is increasing the number of collection places for sorting waste and streamlining the system of collection and treatment of household waste. There is, along with improving municipal waste management systems, the education of the population in the context of environmental education. Together with education legislative measures are used, which are operated both by the municipality as the originator of household waste, so the whole system for the collection and processing of waste, i.e. throughout the life cycle of the household waste. Legislative instruments are accompanied by economic instruments, in the form of charges (waste disposal in a landfill), and the amount of the tax rates and subsidies directed towards the development of systems for the municipal waste management.

The expert group choose the most significant four predictors for household waste for which past time series are available or can be estimated [START_REF] Horáková | Vyhodnocení DPSIR rámce pro vybrané skupiny odpadů stanovené výzvou TB940MZP003[END_REF]:

• Driving forces: population; number of retired; unemployed rate; household expenditure on food, footwear and clothing. • Press: prevention percentage according to environmental awareness (sorting rate, influence of ecological education etc.) which decreases the amount of household waste. • State: household waste generation.

The outputs of every forecasting model consist of four basic output steps, which are presented for the household waste category forecasting, in the following figures.

Conclusion

The construction of the DECWASTE waste information system is presented. It uses historical waste data of the ISOH [START_REF]Waste Management Information System[END_REF] and linked open data [START_REF] Soukopová | National Environmental Data Facilities and Services of the Czech Republic and Their Use in Environmental Economics[END_REF]7] of the chosen predictors [START_REF] Kalina | Case study: Prognostic model of Czech municipal waste production and treatment[END_REF][START_REF] Hřebíček | The Forecasting Waste Generation Model based on Linked Open Data and the DPSIR Framework. Case study concerning municipal waste in the Czech Republic[END_REF] based on the DPSIR framework [START_REF] Liao | Identification of the driving force of waste generation using a high-resolution waste input-output table[END_REF][START_REF] Hřebíček | The Forecasting Waste Generation Model based on Linked Open Data and the DPSIR Framework. Case study concerning municipal waste in the Czech Republic[END_REF]. Integration of the appropriate predictors into the forecasting model is discussed. The forecasting model was implemented as open source software DECWASTE and was verified using appropriate household waste data, whose outputs are presented.

The new DECWASTE information system allows decision makers at the national level of the Czech Republic to make sustainable environmental decisions, focusing on waste management data requirements, national strategies for waste data acquisition, management and processing in a similar way as was done previously. They assisted in identifying alternative waste management strategies of the Czech Republic and support the national WMP that meet the objectives of EU legislation and Circular Economy principles [START_REF]Closing the loop -An EU action plan for the Circular Economy[END_REF].

  values of the waste category generation ŵ 𝑡𝑡 𝑓𝑓 and the values of predictors  𝑖𝑖,𝑡𝑡 𝑓𝑓 are known for each waste category f and 𝑃𝑃 𝑓𝑓 (𝑡𝑡, 𝑜𝑜 1 , 𝑜𝑜 2 , … , 𝑜𝑜 𝑁𝑁 ) = 0 in the years t = 2009, …,2015 and the coefficients a0 f ,…,aK f from (1), (2) are calculated using the method of multiple regression. The forecast of the amount of waste category generation 𝑤𝑤 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑛𝑛𝑝𝑝𝑝𝑝𝑖𝑖𝑝𝑝 𝑓𝑓 (𝑡𝑡) in the years t = 2016,…,2025 depends on the values of the predictors 𝐴𝐴 𝑖𝑖 𝑓𝑓 (𝑡𝑡) and functions 𝑃𝑃 𝑗𝑗 𝑓𝑓 (𝑡𝑡), i=1,…,K f ; j = 1,…, N for each waste category f. Using the functions 𝑃𝑃 𝑗𝑗 𝑓𝑓 (𝑡𝑡), the decision makers at the PA could simulate different scenarios for the anticipated impact of the individual measures oj, j = 1,…,N on the total waste generation in the given waste category f and year t. They could choose, for example, the value of 𝑃𝑃 𝑗𝑗 𝑓𝑓 (𝑡𝑡), which was achieved for the year 2025 and the values of the function 𝑃𝑃 𝑗𝑗 𝑓𝑓 (𝑡𝑡) in the previous years from 2016 to 2024, until the year 2015, where they choose this equal to zero and examine what the impact of the forecast value on the waste category generation 𝑤𝑤 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑛𝑛𝑝𝑝𝑝𝑝𝑖𝑖𝑝𝑝 𝑓𝑓 (𝑡𝑡)is.

Fig. 1 .

 1 Fig. 1. Step 1. Input data for MSW generation. Source: authors. 1. In the first step, Fig. 1, basic data inputs over the time t=2009,…,2015 for modelling are provided: previous household waste generation and time series of the known values of the above DPSIR predictors (implicit values are parsed from linked open data) and forecasting model outputs. 2. In the second step, Fig. 2, users can specify the values of the predictors (pre-filled values are available with hyperlinks to the linked open data sources) expected in the model (1), (2) or choose their possible linear/exponential extrapolation. Users

Fig. 2 .

 2 Fig. 2. Step 2. Scenario of forecasting future development in household waste generation in 2016-2025 with expected prevention measures. Source: authors. 3. In the third step, we conducted the assessment of predictor' statistical significance α=0.05 in the model (1) depending on the input values of the predictors in the years 2009 to 2015 and we excluded insignificant predictors: number of retired; unemployed rate; household expenditure on food, footwear and clothing. We obtained a linear model with the predictor amount of population. In Fig. 3, results are shown in the form of a table, mathematical expression of (1) and a time-plot, showing the development of future household waste generation and the effects of the prevention measures taken (the curves represent the prediction interval and shaded areas show the confidence intervals). 4. In the fourth step, Fig. 4, a sensitivity analysis is presented, showing decision makers the estimated effect of the individual predictors from the model (1) and the quality of forecasting.

Fig. 3 .

 3 Fig. 3. Step 3. Final prediction of household waste generation. Source: authors.

Fig. 4 .

 4 Fig. 4. Step 4. Sensitivity analysis of forecasting household waste generation. Source: authors.

of the multi-linear regression model of waste category generation with

	Table of equivalence
	of Annex III of the WSR between the European Waste Classification for Statistics,
	version 4 (EWC-Stat Rev 4) (substance oriented waste statistical nomenclature) and
	the European List of Waste (LoW) established by the Commission Decision
	2000/532/EC.
	2. Processing of the historical annual waste generation and treatment reports (2009-
	2015) based on the LoW provided by waste generators and facilities in the Czech
	Republic to the ISOH and creating their data sets of waste categories of the WSR.
	3. Identification and development of socioeconomic and demographic predictors for
	waste categories and activities based on the DPSIR framework (which have influ-
	ence on the generation of waste categories) using linked open government data
	(eGovernment systems) of the Czech Republic.
	4. Development