Weronika T Adrian

Antoni Ligęza
email: ligeza@agh.edu.pl

Grzegorz J Nalepa

Krzysztof Kaczor

Distributed and Collaborative Knowledge Management Using an Ontology-based System

Semantic annotations and formally grounded ontologies constitute flexible yet powerful methods of knowledge representation. Using them in a system allows to perform automated reasoning and can enhance the knowledge management. In the paper, we present a system for collaborative knowledge management, in which an ontology and ontological reasoning is used. The main objective of the application is to provide information for citizens about threats in an urban environment. The system integrates a database and an ontology for storing and inferring desired information. While a terminology of the traffic danger domain is described by the ontology, the location details of traffic conditions are stored in the database. During run-time, the ontology is populated with instances stored in the database and used by a Description Logic reasoner to infer new facts.

Introduction

One of the important research fields of Artificial Intelligence (AI) is the area of Knowledge Representation and Reasoning (KR&R) [START_REF] Brachman | Knowledge Representation and Reasoning[END_REF]. The Semantic Web [START_REF] Berners-Lee | The Semantic Web. Scientific American[END_REF] initiative is sometimes perceived as the new incarnation of AI, tackling some of its problems and challenges. Although this worldwide project is not aimed at constructing intelligent machines, it has resulted in development of several effective KR&R methods. Representation of knowledge is done on a few levels of abstraction. For single objects, attributes and relations to other objects (resources) are defined, by use of semantic annotations. These attributes and relations are organized into semantic vocabularies for various domains. Classification of objects and classes definition using their interdependencies is done with use of ontologies [START_REF] Guarino | Formal ontology and information systems[END_REF] of different expressiveness and formality level. Stating logical axioms about classes enable automated reasoning and inferring conclusions about single objects. There is an ongoing research on integrating ontologies with higher-level representation of rules. Semantic applications can make use of this multilevel knowledge representation and exhibit semi-intelligent behavior.

Web-based information systems have been widely used to facilitate communication and distribution of information in a rapid and efficient way. Whether through official news portals or social systems like Facebook or Twitter, people inform each other about the events or dangers. Using GIS systems [START_REF]The Handbook of Geographic Information Science[END_REF] that allow to store, represent and search geographic information, users can add location metadata to the information they provide or get useful data based on their localization (e.g. by the use of a GPS). One of the area which still needs a careful attention from the information systems is the local safety of citizens in the urban environment.

Projects such as Wikipedia has demonstrated that people are willing to cooperate if they find it worthwhile and the system is easy to use. Collaborative knowledge engineering and management can be enhanced by employing intelligent techniques, for example by using formal knowledge representation. However, a system interface must remain simple. An interesting and promising example of such a combination are semantic wikis [START_REF] Nalepa | Collective knowledge engineering with semantic wikis[END_REF][START_REF] Adrian | How to reason by HeaRT in a semantic knowledge-based wiki[END_REF], in which teams can collaboratively build and manage formally specified knowledge in an intuitive fashion and with little technical knowledge.

In this paper, we present a system for collaborative knowledge management enhanced with semantic knowledge representation and reasoning. The main objective of the system is to gather knowledge about threats of various sorts within a defined urban area. The system should serve the local community and the police. Our proposed solution combines social software features (commenting, ratings etc.) with a strong underlying logical representation of knowledge entered by users. The application employs AI methods, namely a domain ontology of traffic dangers and conditions, and a Description Logic (DL) [START_REF]The Description Logic Handbook: Theory, Implementation, and Applications[END_REF] reasoner to infer knowledge from facts explicitly present in the system.

The rest of the paper is organized as follows: in Section 2 the motivation for our research is given with references to selected previous works. Section 3 gives an overview of selected existing solutions and related work. In Section 4, basic assumptions for the system are specified. Section 5 gives an overview of the system, including its functionality, architecture, a threat ontology, the integration of an ontology and a database in the system, the reasoning in the system and the user interface. The implementation is briefly discussed in Section 6. Evaluation of the approach is sketched in Section 7. The paper is summarized in Section 8 and future work is outlined in Section 9.

Motivation

Within the INDECT project1 several problems related to security and intelligent information systems are investigated. Task 4.6 of the project focuses on development of a Web System for citizen provided information, automatic knowledge extraction, knowledge management and GIS integration [START_REF] Ligęza | Towards enregistration of threats in urban environments: practical consideration for a GIS-enabled web knowledge acquisition system[END_REF]. The main objective of our research is to develop a semantically enriched environment for collaborative knowledge management. Local communities should be able to quickly share information about current traffic dangers and threats, for instance closed roads, holes in the pavements and streets, dangerous districts or events that impede a regular traffic. The system proposed within the task should be a sort of a community portal that allows citizens to participate and cooperate in order to improve the security in the urban environment. Within the task several initial system prototypes have been developed [START_REF] Ligęza | Prototypes of a web system for citizen provided information, automatic knowledge extraction, knowledge management and gis integration[END_REF][START_REF] Waliszko | Traffic danger ontology for citizen safety web system[END_REF] and the current work consists in integrating the best solutions into the final system.

The system should use some sort of intelligent processing to provide possibly most useful knowledge to the users. To this end, a Knowledge-Based System (KBS) should be proposed, with a formalized knowledge representation and reasoning mechanisms. Categorization of threats and possibility of inferring new facts based on the ones entered by users is a desired feature. To enhance the automated knowledge processing of the system, semantic technologies for GIS were analyzed and discussed in [START_REF] Nalepa | Review of semantic web technologies for GIS. Automatyka: półrocznik Akademii Górniczo-Hutniczej im[END_REF].

While a threat domain ontology can be the same for various locations, different system installations will vary depending on the locations they work in. Abstract knowledge can and should be shared across applications boundaries to facilitate change management. On the other hand, the system should be robust and easily adaptable to local conditions, so the access to the actual data should be optimized.

The system should encompass social features, such as possibility to comment on, discuss and rate information entered by other users. This way, the users can gain or loose credibility and the community can indirectly control spam information. The user interface (UI) should be intuitive and easy to use, potentially adaptable to various hardware platforms including desktop and mobiles. Encompassing these requirement should provide a useful intelligent system for improving urban safety.

Threat Information Systems -an overview of existing solutions

In this section, we give a brief overview of the existing approaches and solutions for threat information systems. Crime Mapping systems were originally a class of systems that map, visualize and analyze crime incident patterns using Geographic Information Systems (GIS). This name has been later extended to incorporate all applications that aid in improving the public safety. This include natural disasters monitoring systems which are often designed for specific regions and the scope of their functionalities is usually limited to the specific types of disasters that are most common and most dangerous in those regions, systems monitoring threats on the roads and crime monitoring systems. A detailed survey of the existing crime mapping systems is given in [START_REF] Żywioł | Analysis and evaluation of crime mapping systems[END_REF].

Ubiquity of the Internet allowed the Crime Mapping class systems to be available not only for specialized units, but for everyone with Internet access, thus allowing the improvement of public safety.

Crime Mapping systems can be divided into categories based on the nature of threats they focus on. The INDECT Crime Mapping class system developed at the AGH University does not specialize in any of them. Instead, it attempts to track threats in all of those categories.

Categorization of Crime Mapping systems

Crime Mapping is a very extensive class of systems. To facilitate analysis and comparison of its representatives, several factors can be separated that the systems can be categorized by. The following categorization and the list of systems are not exhaustive, but should give an overview of the state of the art in this area.

Types of threats This seems to be the most important criterion, since the system is chosen by user based on kind of threats they are interested in monitoring or learning about. Crime Mapping systems focus on:

-Natural disasters: Earthquakes, Fires, Floods, Dry weather, Storms (e.g. thunderstorms, hurricanes, etc.), Tsunami, Tornados, Volcanic activity.

-Threats caused by a man: 1) Crimes: numerous types of criminal activity, e.g. robberies, assaults, car or house break-ins, sex crimes, etc., 2) Industry level: larger scale disasters caused by technology, e.g. chemical spills, toxic fumes, radiation, etc., 3) Infrastructure-type (e.g. old buildings, slippery stairs) -Transportation risks: 1) Hazards: various places or situations that may cause a risk to the driver, e.g. potholes, sharp curves, road bumps, etc., 2) Disruptions: not as much dangerous places, but more an inconvenience for people travelling, e.g. road works, traffic jams, narrowings or speed limits.

Geographical range Crime Mapping systems vary in area they cover. This also is an extremely important factor, as user will be looking for a system that covers the area of their interest. They are divided into following categories:

-Global: systems spanning the whole planet; -Local: limited only to some area. Depending how the range boundaries are defined, they can be limited: geographically, for example, a system monitoring fires in only one forest or warning about tornados on some specific continent, or socially, depending by social structure, e.g. from single city quarter, through the whole city to an entire country or even several of them.

Availability Crime Mapping systems can be also divided depending on who the system is available to. The significance of this factor is that users need to have means to access the system if they are to use it. Categories are:

-Publicly available: many systems are open for browsing for everyone, without even the necessity of registering or logging in. -With authorized access: users need to register with the system and log in to learn the information it offers. Registration itself could be open for everyone or somehow limited -for example, a registration fee could be collected. -Restricted: only people satisfying some defined conditions could be accepted as users, e.g. inhabitants of monitored area, members of an organization or employees of a company.

Data Source Finally, Crime Mapping systems vary in where the data collected and presented comes from. The source of information matters, because it has impact on its quality and credibility. Data can come from:

-Agencies, cooperating with the system, e.g. law enforcement or university; -Experts, analyzing data and providing the results to the system; -Automated monitoring, collected by various measuring or observation apparatus (e.g. seismic sensors or cameras), often subjected also to processing or analysis, which itself also can be automated; -Users -some systems allow users to provide data to the system, or even depend on that greatly.

Natural Disasters Information Systems

Natural disasters monitoring systems are often designed for specific regions and the scope of their functionalities is usually limited to the specific types of disasters that are most common and most dangerous in those regions. For example, the area of Great Britain has several different systems for warning about floods. Among others, there are: National Flood Hazard Mapping2 in Wales, Managing Flood Risk and Flood Warnings3 in Scotland and Risk of Flooding from Rivers and Sea4 in London and its vicinity.

Similarly, there is a California-Nevada Fault Map5 (see Fig. 1) , collecting information about earthquakes in the western states of USA, where those are a frequent occurrence due to the proximity to the San Andreas Fault.

Another system, developed as part of U.S. Geographical Survey's (USGS) Earthquake Hazards Program6 is available on the Internet to aid reducing earthquake hazard in the USA. However, their area of observations is not limited to the US only. The scientists behind the program study and keep track of earthquakes all around the world, providing not only local but also global information. This proves that aside from those regional systems for warning about national disasters there also are ones working globally. Other examples of such systems are Seismic Monitor7 , also gathering information about earthquakes around the world, and Global Flood Map8 .

Road Threats Information Systems

Another area where technological advancement is progressing constantly is the transportation. For common citizen especially important from the safety point of view is personal transport -car technology and road infrastructure.

GPS navigation devices become increasingly popular, and they not only serve as tools for finding optimal routes and guide the driver to their target, but often they also take into consideration and keep track of dangerous spots on the road, requiring the driver to be more cautious or avoid it completely. Places where, for example, accidents happen more often due to potholes or poor visibility, are sometimes included in maps the devices use, so that they inform the driver when they are approaching the danger.

Systems like that already exist. They collect information of such statistically risky places, but also keep track of other, temporary difficulties, like road works, narrowings, traffic jams or accidents. More often than in case of natural disasters monitoring systems these cover only some local areas -there is no actual global transport threats and difficulties monitor.

The examples of such systems are: Traffic Information Highways Agency (reporting bad weather, speed reductions, accidents and road works in England) 9 , Travel News Scotland (accidents, road works, other difficulties) 10 , TravelMidwest.com 11 (road capacity, road works, accidents, speed cameras, etc. in Chicago and several other cities and areas around Midwestern states). NAVTEQ 12 is one of the few of such services that attempt to provide traffic information from all around the world, yet it only provides information about capacity and delays, not about threats or accidents.

Similar Polish service with traffic information, "Serwis dla kierowców" (Service for Drivers) 13 , available on the website of General Directorate of National Roads and Motorways (GDDKiA), bears comparison with Traffic England. Although the interactive map is a little more difficult to use (mouse roller does not work as zoom in and out, drag and drop map moving sometimes fails), the road network this tool covers is much more dense and is not limited only to motorways. Moreover, user can switch between clear road map and information about road capacity (see Fig. 2).

Crime Mapping systems

The name of Crime Mapping system class comes from the original idea for publicly available systems providing information about crimes and threats in urban area. The possibilities for what such systems could incorporate and the vast amount of functionalities they could provide are growing along with the development of the Internet. Crime Mapping systems gain much in credibility and accuracy of presented data when they cooperate with law agencies and police departments. Since this way the agencies aid in increasing public safety, profits from such cooperation are mutual.

Because law differs between parts of the world, such systems are mostly local, varying in range from one police unit (e.g. Crime Tracker 14 for Jefferson Parish, Louisiana, USA), through a province or a county (Cornwall Crime Explorer 15 or Arizona Crime Search16 ) to a whole country (Crime Reports17 or Crime Mapping18 , both of which span over USA and Canada). Sadly, there are no global Crime Mapping systems. The systems present the threats in a visual form on a map and provide various output channels, e.g. e-mail notifications, text messages or even Twitter alerts. They operate on mobile devices and make use of their GPS systems. The apparent lack, however, is that the information presented to the users is strictly that which was entered. The original contribution of our approach is to supply the system with intelligent processing techniques based on ontological reasoning. Moreover, our approach aims at encompassing various kinds of threats by using a threat ontology.

A Conceptual Model for the System

The main focus of our research was on tools to process the information provided by citizens via a specialized website. In fact, a Web System software for citizen provided information, automatic knowledge extraction, knowledge management and GIS integration is to be developed. This task is intended to complement other work oriented towards automated information extraction from existing web resources by building an Internet-based, distributed information acquisition and automated knowledge management system.

Such a system should combine a CMS (Content Management System) system with a KMS (Knowledge Management System) incorporating intelligent information processing tools based on knowledge engineering. It should allow storing and retrieving of partially analyzed investigations. For spatial information processing and information presentation a GIS (Geographical Information Systems) technology should be used and logic-based systems technology for automated inference should be incorporated.

The main goal of the system is to serve as a distributed knowledge acquisition system for data, information and knowledge provided by citizens, as well as to enable limited automated knowledge management. In principle, the working scenario for the systems is as follows. The system offers a web interface (based on thin-client technologies; in practice a standard web viewer). The interface offers various functionalities for different types of users. The main functionality refers to enabling definition of a new threat. The provided knowledge is then checked for syntactic correctness and processed in an automatic way. The ultimate result is stored in the internal knowledge base. The knowledge base should enable automatic knowledge extraction and processing in order to maximize the effectiveness of the system.

System Overview

The proposed system is an ontology-driven application. It integrates a database and an ontology for storing and inferring knowledge about traffic dangers in a given area. While the abstract of traffic danger domain is described by the ontology, the location details of traffic conditions and geographical information (e.g. relations among concrete streets, districts and postal codes) are stored in a relational database. During run-time, the information from the database is integrated (synchronized) with the core ontology (the terminology is populated with instance data stored in a database). The synchronization is done automatically at the application start and at any time on a user demand. The synchronized ontology is then used by a DL reasoning engine to infer facts about chosen area. The deduction is based on definitions of threats which depends on specific traffic conditions present in specific locations.

Functionality and User Roles

The main objective of the proposed system is to provide citizens with a real-time data about dangers occurring in a chosen area. Part of this information is entered into the system by so-called trusted users. The rest is automatically inferred based on the axioms of the threat ontology and instance data of current conditions (facts).

Three kinds of users are distinguished within the system. Regular users can browse the system knowledge and ask questions about specific locations and dangers. They address the system with dynamic questions and get results of inferred traffic dangers.

Trusted users can modify the information stored in the database, e.g. they can update the locations of traffic conditions occurrences. The information is validated and stored in the database. Updated knowledge can be used for dangers deduction process, after synchronization with the ontology. Finally, the experts can modify the core ontology.

Architecture and Data Flow

The system is divided into three functionally different layers:

1. a web dashboard layer dedicated to the interaction with users (through browser clients), 2. a platform layer which is the core of system responsible for processing knowledge, and 3. a storage layer, where all the data is stored, in a database and an ontology (see Figure 4). All users can interact with the web-based dashboard for querying system, to get desired information. The main logic of the system (presented in Fig. 4 as three cogged wheels) consists in: downloading the core ontology ("Getting core ontology from URI"), synchronizing the core ontology with the current data uploaded by trusted users ("Synchronization with database"), and inferring the ontology dependencies ("Knowledge inferencing").

For working with most recent data, provided by trusted users, the synchronization mechanism integrates core ontology, describing the abstract of traffic dangers, with specific real time data. The process is executed for the first time on application start, i.e. the first request to the server while accessing the main page of the system. This functionality is also available on demand. After synchronization, the populated ontology is cached in memory and used for inferencing.

A single installation of the system (for instance for a single city) has its own database, in which the information about streets, districticts and actual conditions are stored. The core ontology on the other had can be shared by several installations of the system. It is accessible by an URI and can be stored on local or remote server.

Traffic Danger Ontology

As noted in [START_REF] Noy | Ontology Development 101: A Guide to Creating Your First Ontology[END_REF], "In recent years the development of ontologies has been moving from the realm of Artificial-Intelligence laboratories to the desktops of domain experts.". Sharing common understanding of the knowledge domain is one of the most critical reason for developing ontologies. Explicit domain assumptions provide a clear description of the domain knowledge and simplify the knowledge extensibility. In our case, the domain ontology consists of concepts of geographical locations (streets, districts, postal codes), traffic dangers (e.g. LowFrictionDanger, RoadConstructionDanger), traffic conditions (including among others a hierarchy of WeatherConditions and RoadConstructionConditions) and describes multiple relations among them. An excerpt of the ontology is shown in Figure 5. The ontology enable the system to reason upon stored facts and answer the questions of the following types:

-What traffic dangers can be encountered within a specific area? -Is there any danger within area of specific postal code or specific district? -What kind of dangers are connected with specific atmospheric conditions? -Are there any dangers connected with specific condition (e.g. low friction) in a specific area? -What are the sub-areas of a specific location? -Are there any traffic conditions provided for a specific location?

In order to answer these questions either a semantic reasoner is used (which performs classification of concepts within the ontology) or appropriate DL queries are constructed. Based on the definitions of TrafficDangers in the ontology and information about actual condition occurences, implicit knowledge may be deduced (what kind of danger results from given conditions in a selected area).

Integration of the Ontology and the Database

While the abstract domain knowledge is expressed by the ontology axioms, the operational knowledge of the system is stored in a relational database. The database schema can be observed in Figure 6. The knowledge stored in the database consists of the locations structure and the actual traffic conditions in these locations. Specifically, the locations of the traffic conditions occurrences are defined by postal codes. The postal codes are connected to streets, which in turn are connected to districts. For instance, one can add an information that a particular street is under construction (a RoadCon-structionCondition or one of its subclasses occurs) or that there is a specific weather condition in a specific district. One of the most important aspects of the system is the possibility of an integration of data from the database and the ontology. Upon the synchronization process, the core ontology is cached and populated with the data from the database becoming a synchronized ontology. While the core ontology describes a terminology of traffic danger, the synchronized one is related to a specific environment and used for reasoning. Consequently, synchronized ontology can differ between the various environments where the system is deployed. For example, traffic conditions information for Cracow can vary significantly from those in Montpelier. Although it is possible to have a single installation of the system and synchronizing the ontology at once with all global data, it can result in system overloading and decreasing performance while inferring dependencies.

Reasoning in the System

Reasoning in the system is provided by invoking a DL reasoner on a synchronized ontology. The sequence diagram (see Figure 7) the required steps for the reasoning process. Once the trusted users have provided traffic conditions facts, a regular user can check what threat they may expect in a specific area. Responding to the user request, the system imports the up-to-date facts into a locally stored ontology (synchronizes the ontology), and then query the ontology by posing appropriate DL queries. From a user perspective, a query is constructed by selecting a desired location through a web-based interface. Once the query is created, the DL reasoner is invoked to process it on the cached ontology. The inferred set of information is provided to the user. The reasoning takes relatively little time given the ontology having less than 100 classes. However, no benchmark testing has been done yet and it this is a subject for further work.

User Interface

The web-based interface of the system allows its users to create dynamic questions, and get the results about inferred traffic dangers. The prototype implementation [START_REF] Waliszko | Knowledge Representation and Processing Methods in Semantic Web[END_REF] uses simple forms by use of which the users can construct questions for the system, e.g. a user can choose a desired location from a drop-down list and ask what threats may be encountered in this particular area (see Fig. 8). Full development of a Graphical User Interface (GUI) with a map component is in progress. There is a process going on to integrate the logical layer of the system with an interface that uses maps and provides social features for the community of users. A fully-fledged GUI with an interactive map on which the users can navigate and filter threats by location, date, severity etc. has been developed (see Fig. 9), but is not yet fully integrated with the logical layer. In this version, a spatially-enabled database will be used which allows to store geographical data in an efficient way. The usability of the system is expected to increase, due to the possibility of visually choosing an area of interest (see Fig. 10) and the social features like rating threats and discussing them. (OWL2 XML21 , RDF/XML 22 or OWL2 Manchester Syntax) 23 ). The synchronization is based on the OWL API library 24 and provides up-to-date information (cache in memory) for the HermiT DL Reasoner.

The ontology can be stored on local or remote server and is accessed by an URI. The cooperation with a database is provided through the Hibernate ORM 25 technology. The simple form-based user interface has been built with the JavaServer Pages (JSP) 26 and jQuery JavaScript Library 27 , while requests from users and appropriate responses, are controlled by Spring MVC 28 . For logging the results of particular operations, log4j Java-based logging utility 29 is used. PostgreSQL 30 is choosen as SQL database. The application has been written in Java using the Eclipse Java IDE 31 . Dependencies man-Fig. 10. GUI for the system: a map with selected area. agement and versioning is the task of Apache Maven tool32 . All these technologies are free software or open source.

Evaluation

With respect to the criteria defined in Section 3, the system can be categorized as:

-General-purpose: it is designed to provide information about a wide variety of threats, -Local: it is specifically aimed at being used in small communities (neighborhood, city, etc.) -Publicly available (with limited functionality) and with authorized access (for more advanced features), -Depending on users as main data providers.

The system has several advantages which make it suitable for being used in the aforementioned local environment. The architecture of the system, with loose coupling of the database and ontology, enables using the same core ontology in various installations. Synchronized ontologies (populated with real time data) can differ between various environments. This decentralized way of cooperation, when each client have cached in memory its own synchronized-on-demand ontology instance, is chosen for performance optimization reasons. Comparisons to situation in which single instance of ontology is the centralized part, accessible for synchronization to all clients, provide obvious performance drawbacks. This is why it was considered as an anti-pattern and depreciated by design.

The web-based, collaborative nature of the system makes it possible for citizens to communicate with each other, discuss and rate threats, and let them -to some extendcontrol the quality of the information entered into the system. The effectiveness of using the system depends largely on the engagement of the community. The so-called network effect is an important factor, because the more people honestly engage in the knowledge management process, the better the quality of information given by the system.

The architecture and design principles, especially the use of an ontology, have also their drawbacks. Because ontologies should be designed to be reusable, it is required to spent relatively lots of effort to provide a good design and tests. Domain experts should be involved in developing these tasks in parallel with programmers, to provide short iterations, as results of quick responses for every inconsistencies in ontology (irrelevant hidden relationships, domain descriptions mistakes or usability problems).

The data in the system is currently acquired from users. However, the system would benefit from incorporating some knowledge from external data sources, e.g., weather forecast services etc.

Summary

AI techniques may be successfully used in various applications for Knowledge Management. Using an ontology in a KM system allows to store abstract data, share it across several installations and manage changes in a centralized way. A loose coupling of the ontology with a relational database allows to store concrete data about conceived area in a database and populate the ontology with instance data during application run-time. Embedding a Description Logics reasoner enable the system to reason upon explicit knowledge entered by users and give back a useful response. A graphical user interface with a map component and social software features make the system user friendly and has a gradual learning curve.

To the best of our knowledge, there does not exist a crime mapping system that uses ontologies and DL reasoning to provide rich information based on knowledge gathered in the system. Although there exist numerous solutions for various danger information systems, none of them describe the threats in a formalized ontological way, relate weather or road conditions to the possible dangers and reasons about these dependencies. We believe that this is our original contribution compared to existing work.

Future work

The system has been tested with several Web browsers and can be used on any device that support Web browsing. However, for mobile devices, some adaptations are needed. The current prototype implementation has a limited user interface. The intended integration with a GUI providing interactive map and social features is not yet finalized. A possible direction for further development could be focused on extensions for heterogeneous application-to-application communication. The RESTful Web Services 33 can be considered. External systems would be perceived as software agents. Their tasks could be focused on periodic connections to the system, getting some information set, and creating statistics about the traffic dangers. The statistics could visualize frequencies of particular dangers on a specific area or classify the safety of the selected district.

Another important research thread is the context-awareness of the system. Using the system should be seamlessly incorporated into the daily routines of its users. Therefore, certain adaptations are considered [START_REF] Bobek | Mobile context-based framework for monitoring threats in urban environment[END_REF] that will allow installations of the system to work on mobile devices in an adaptive way.

Fig. 1 .

 1 Fig. 1. California-Nevada Fault Map. Source: http://earthquake.usgs.gov/ earthquakes/recenteqscanv.

Fig. 2 .

 2 Fig. 2. Service for Drivers. Source: http://www.gddkia.gov.pl/pl/10/ serwis-dla-kierowcow.

Fig. 3 .

 3 Fig. 3. Interactive map with crime markers. Source: http://www.crimemapping.com.

Fig. 4 .

 4 Fig. 4. Data flow in the system

Fig. 5 .

 5 Fig. 5. Traffic Danger Ontology: Asserted class hierarchy

Fig. 6 .

 6 Fig. 6. ER diagram of the traffic database

Fig. 7 .

 7 Fig. 7. Sequence diagram for updating and inferring data

Fig. 8 .

 8 Fig. 8. An excerpt of web-based user interface of the system

Fig. 9 .

 9 Fig.9. GUI for the system with an interactive map and search filters.

See http://indect-project.eu.

See: http://www.floodmaps.ie

See: http://go.mappoint.net/sepa

See: http://www.environment-agency.gov.uk/homeandleisure/floods

See: http://earthquake.usgs.gov/earthquakes/recenteqscanv

See: http://earthquake.usgs.gov/earthquakes

See: http://www.iris.edu/seismon

See: http://globalfloodmap.org

See: http://www.highways.gov.uk/traffic/traffic.aspx

See: http://crimestats.jpso.com/crimetracker/externalmanager/ index.html

See: https://www.amethyst.gov.uk/crime_atlas/atlas.html

See: http://www.azcentral.com/CrimeMaps

See: https://www.crimereports.com

See: http://www.crimemapping.com

Implementation and DeploymentThe ontology has been developed in a top-down process with the Protégé 19 editor integrated with the HermiT DL Reasoner 20 . The ontology is provided in different formats19 See http://protege.stanford.edu/ 20 See http://owlapi.sourceforge.net/

See http://www.w3.org/TR/owl2-xml-serialization/

See http://maven.apache.org/

See http://www.ics.uci.edu/~fielding/pubs/dissertation/rest_ arch_style.

Acknowledgement

The research presented in this paper is carried out within the EU FP7 INDECT Project: "Intelligent information system supporting observation, searching and detection for security of citizens in urban environment" (http://indect-project.eu).


Keywords: Brazilian Legal Amazon, Protected areas, Deforestation, Impact analysis C21, Q23, Q28, Q57

Using a remotely sensed pixel data set, we develop a multilevel model and propensity score weighting with multilevel data to assess the impact of protected areas on deforestation in the Brazilian Amazon. These techniques allow taking into account location bias, contextual bias and the dependence of spatial units. The results suggest that protected areas have slowed down deforestation between 2005 and 2009, whatever the type of governance. The results also evidence that protected and unprotected areas do not share the same location characteristics. In addition, the effectiveness of protected areas differs according to socioeconomic and environmental variables measured at municipal level.

Introduction

Protected areas (PAs hereafter) play a critical role in conserving biodiversity and the provision of ecosystem services (Millennium Ecosystem Assessment 2005;Nagendra et al. 2013). They benefit from the support from the international community. The Convention on Biological Diversity (CBD) has committed to a specific set of actions targeted on PAs especially since 2004.

PAs take up a significant part of financial resources devoted to conservation. According to The Economics of Ecosystem and Biodiversity report, an estimated 6.5 to 10 billion USD are devoted to the support of PAs over the world (TEEB 2008, chap.8). Moreover the Global Environment Fund has granted more than 3 billion USD with a 12 billion USD co-financing to terrestrial PAs over the past 24 years (Global Environmental Facility 2015). Even if several authors underlined the significant financial shortfall to manage existing and expand PAs (Bruner et al. 2004), it is essential to assess their effectiveness especially in biodiversity hotspots like the Brazilian Amazonia. This is consistent with the 2011-2020 Strategic Plan for Biodiversity including the Aichi targets, which focuses on the effectiveness of existing terrestrial PAs rather than on further increases in protected areas (Target 11).

Brazil has dramatically increased the surfaces for conservation purposes in the Amazonian forest for several years. According to INPE (Brazilian Institute of Space Research), PAs of various types covered more than 1.2 million square kilometers up to 2012. These PAs have continuously expanded since the beginning of the 2000s. In the meantime, several studies documented a net decrease in deforestation rates. The decrease seems however to be more uncertain since 2012. Scholars do not necessarily agree on the causal link between PAs expansion and the downward dynamics of deforestation. Several authors advocated the role of economic conditions and especially the global financial crisis (Nepstad et al. 2009). Other authors rather put emphasis on the role of public policies (Arima et al. 2014;Cisneros et al. 2015) with a particular interest on PAs [START_REF] Pfaff | Governance, location and avoided deforestation from protected areas: Greater restrictions can have lower impact, due to differences in location[END_REF]Pfaff et al. 2015) since Brazil has an ancient history with them (Rylands & Brandon 2005).

Aggregate data as from the FAO Forest Resource Assessments or national data from existing censuses are not appropriate to highlight the effect of PAs on deforestation.

Deforestation decisions are subject to interactions between decision makers who operate at different administrative levels. For instance, forest dwellers are embedded and influenced by local conditions. There is therefore a need for micro level studies using fine resolution data which are more relevant for impact analyses. 1 Satellite imagery provides high resolution data on land uses and therefore enables micro level empirical analyses of deforestation. (Nelson & Geoghegan 2002) underline how important is remotely sensed data for land use analysis.

Early examples of such uses are contributions to the determinants of deforestation (e.g. (Chomitz & Gray 1996) on Belize or (Vance & Geoghegan 2002) on Mexico); [START_REF] Cropper | Predicting the location of deforestation: The role of roads and protected areas in north Thailand[END_REF]) evidenced that wildlife sanctuaries may have reduced the likelihood of forest clearing in Thailand.

Recent studies on the effectiveness of PAs on deforestation relied on remotely sensed pixel data. (Nelson & Chomitz 2011) studied the impact of PAs on deforestation on a worldwide sample of tropical forest countries while proxying tropical deforestation with the occurrence of forest fires. As for studies dedicated to Latin American countries, authors found a positive impact of PAs on deforestation while controlling for the location bias [START_REF] Cropper | Predicting the location of deforestation: The role of roads and protected areas in north Thailand[END_REF]Joppa & Pfaff 2009). For instance (Andam et al. 2008;Pfaff et al. 2009;Robalino et al. 2015) focused on the well-known protected-area system in Costa Rica. The same methodology was used by (Bray et al. 2008) on the Maya forest of Guatemala and Mexico. Brazil was also intensively studied by [START_REF] Pfaff | Governance, location and avoided deforestation from protected areas: Greater restrictions can have lower impact, due to differences in location[END_REF] for the state of Acre and (Pfaff et al. 2015) for the entire Legal Amazonia.

From a general point of view, these studies take the location bias into account. They control for the fact that PAs are not randomly distributed i.e. they are more likely to be localized in remote areas with lower agricultural profitability and henceforth the deforestation pressure is lower. Fine scale (pixelised data) combined with hierarchical data (data municipal for instance) however raises other potential biases. They stem from contextual and correlated effects [START_REF] Manski | Identification of endogenous social effects: the reflection problem[END_REF]. Contextual effects mean that several exogenous characteristics at municipal level could have an impact on deforestation decisions at pixel level. Correlated effects mean that pixels belonging to the same municipality share common unobservable characteristics which in turn generate similar deforestation behavior.

This paper adds to this strand of micro focused literature on the effect of PAs on deforestation in several ways. We take advantage of the hierarchical structure of Brazilian data which are available at pixel and municipal levels. This was done for instance by (Holland et al. 2014) who studied the effect of land tenure on deforestation in the Ecuadorian Amazon. In our paper, contextual and correlated effects are explicitly addressed in addition to location bias. The remotely sensed pixel data set generated from satellite imagery is exhaustive i.e. is not drawn at random. Two complementary estimators are run. Multilevel models allow for taking the hierarchical nature of our data into account. Propensity score weighting with multilevel data allow for comparing treated and untreated pixels with similar location and contextual characteristics. Our main result is that PAs -all categories taken together -allowed avoiding 36,686 square kilometers of deforestation compared to the observed 65,326 over the period 2005-2009. The rest of the paper is organized as follows. Section 2 presents how the data set was constructed and provides background on the Brazilian conservation policy. Section 3 is devoted to the empirical strategy. Section 4 concludes.

Data elaboration and background on the Brazilian Amazonia

A rich data set is constructed on the Brazilian Legal Amazon that combines high resolution data obtained from satellite imagery and municipal data compiled from the agricultural census conducted at municipal level under the auspices of the Brazilian Institute of Geography and Statistics (IBGE: Instituto Brasileiro de Geografia e Estatistica).

Forest cover

We have built the forest cover and PAs data using remotely sensed pixel data from the We chose to work with information directly measured on the pixels in order to have accurate information on the location (distance to roads, slope, altitude, fertility). As illustrated in Figures 1 and2, we created square grids with sides measuring 10 km throughout the Legal Amazon with ArcGIS software. Observations are centroids of these grids, therefore we have a unique observation for each pixel. For instance, for each pixel, the data indicate a single class of land cover: forest or protected area or deforestation. Among the pixels obtained, we selected those corresponding to the forest in 2004 which gives 32,569 pixels. The spacing between two pixels reduces the problem of spatial autocorrelation of the error terms [START_REF] Cropper | Predicting the location of deforestation: The role of roads and protected areas in north Thailand[END_REF]. Finally, to avoid overestimating the impact of PAs, we removed 4,135 pixels that became PAs after 2004. Deforestation is a binary variable that takes the value of 1 if the pixel was cleared between 2005 and 2009. In other words, a pixel is considered as deforested if it was a forested in 2004 but no longer in 2009. Figure 1 provides a map of pixels distribution in the Brazilian Amazon. We can analyze the representativeness of our sample by comparing some descriptive statistics in our sample with those observed in the Brazilian Amazon. The distribution of land uses in our sample is consistent with what is observed in the Brazilian Legal Amazônia.

Indeed, indigenous lands, sustainable use areas and integral protection areas represented in 2004 respectively 20.29%, 10.05% and 6.07% of the forest area of the Amazon and in our sample they represent respectively 20.13%, 8.57% and 5.01% of forest. The rate of deforestation in Amazonia observed between 2005 and 2009 is 2.42% and 2.54 % in our sample.

Location characteristics

Several studies have shown that the location of the forest can influence the profit obtained from cleared land. Indeed, the profitability of agricultural land depends on fertility, slope and proximity to the nearest road, river and markets (towns). When looking at the descriptive statistics presented in Table 1, we observe that PAs are on average located in high slope areas with low fertility, important rainfall and distant from roads, rivers and town.

Thus it is important to consider these factors to properly identify the impact of PAs in terms of avoided deforestation [START_REF] Pfaff | Governance, location and avoided deforestation from protected areas: Greater restrictions can have lower impact, due to differences in location[END_REF].

Municipal context

Legal Amazon is an administrative division of Brazil with a surface of 5 million sq. km. It includes seven states (Rondonia, Acre, Amazonas, Roraima, Para, Amapa, Tocantins, Mato Grosso, and parts of Maranhão and Goias) and 788 municipalities. The municipal context of a pixel has a strong chance to influence the likelihood that a pixel is cleared (or protected). Taking these contextual effects [START_REF] Manski | Identification of endogenous social effects: the reflection problem[END_REF] into account implies that two pixels sharing the same location characteristics but with a different municipal context do not have the same probability of being cleared. Contextual effects can be captured at the municipal level by explanatory variables measuring the pressure on the resource (population density), the level of wealth (GDP per capita), forest cover and a proxy for biodiversity (the number of mammals). Indeed these species are generally used as umbrellas species [START_REF] Carroll | Carnivores as focal species for conservation planning in the Rocky Mountain region[END_REF] and can help to protect others species like amphibians, reptiles, birds or plants species. Omitting contextual effects is a source of bias when assessing the impact of PAs on deforestation. Statistics presented in Table 1 show that, as a matter of fact, PAs are localized in municipalities with high forest cover, high biodiversity, low GDP per capita and low population density.

3 Did protected areas slowdown deforestation in Legal Amazon?

The empirical strategy is implemented in two steps. First, we use multilevel nonlinear models to take into account the hierarchical nature of our data. These models allow us to control for the correlation of observations within the MCAs. Indeed in non-multilevel estimates, not only can this correlation affect the value of the coefficients but also their significance [START_REF] Rabe-Hesketh | Review of multilevel and longitudinal modeling using stata, second edition[END_REF]. In these models we control for location characteristics to take into account the location bias. In the second part we use propensity score matching methods that allow for creating a control untreated group with similar location characteristics and municipal contexts when compared to pixels from the treated group.

Baseline estimates of the effect of protected areas

Land can either be kept forested or cleared for agricultural development purposes. The deforestation decision depends on the net benefit or net present value generated from forest conversion into agricultural lands (e.g. Perman et al 2003 p. 273;Fisher et al. 1972).

The decision is either influenced by location characteristics measured at the pixel level (indexed by i) or by contextual characteristics at the municipal level (indexed by j).

Characteristics at the pixel level include the status of the pixel which can either be located in a PA or not. Variables that affect the profitability of agriculture and henceforth enter into the net benefit of clearing (slope, distance to roads, etc.) are also measured at the pixel level (see the complete list of variables in table 1). The municipal context is represented by the initial forest cover, GDP, population density and biodiversity.

We thus consider a latent variable 𝑦 𝑖𝑗 * defined as the net present value of forested land conversion of plot i in municipality j that depends on the protection status of the land, the location of features and contextual effects: 𝐼𝑃𝐴 𝑖𝑗 , 𝑆𝑈 𝑖𝑗 and 𝐼𝐿 𝑖𝑗 are binary variables representing respectively integral protection areas, sustainable use areas and indigenous lands; location characteristics: 𝑋 𝑖𝑗 ; and municipal context: 𝑍 𝑗 . Since the net present value of forest conversion is not observed, we therefore have:

𝐷𝑒𝑓 𝑖𝑗 = 1 if 𝑦 𝑖𝑗 * > 0 𝐷𝑒𝑓 𝑖𝑗 = 0 if 𝑦 𝑖𝑗 * ≤ 0
The results of multilevel models can be biased if unobserved heterogeneity at the level of the municipality (𝜇 𝑗 ) is correlated with the explanatory variables. We dealt with this problem by using a within (municipality) transformation of the continuous explanatory variables (i.e.

𝑋 𝑖𝑗 -𝑋 𝑗 ̅ ) and the inclusion of their municipal group means (i.e. 𝑋 ̅ 𝑗 ) in the regression. The estimated model corresponds to the well-known Mundlak's model (1978) and averaged variables also capture contextual effects. Therefore the reduced form equation of the probability 𝐷𝑒𝑓 𝑖𝑗 is as follows:

𝐷𝑒𝑓 𝑖𝑗 = 𝛼 1 𝐼𝑃𝐴 𝑖𝑗 + 𝛼 2 𝑆𝑈 𝑖𝑗 + 𝛼 3 𝐼𝐿 𝑖𝑗 + 𝛽 1 (𝑋 𝑖𝑗 -𝑋 ̅ 𝑗 ) + 𝛾 1 𝐼𝑃𝐴 ̅̅̅̅̅ 𝑗 + 𝛾 2 𝑆𝑈 ̅̅̅̅ 𝑗 + 𝛾 3 𝐼𝐿 ̅ 𝑗 + 𝛾 4 𝑋 ̅ 𝑗 + 𝛾 5 𝑍 𝑗 + 𝜇 𝑗 + 𝑒 𝑖𝑗 (1)
where 𝐷𝑒𝑓 𝑖𝑗 a binary variable equal to 1 if the pixel i was cleared between 2005 and 2009.

The error term (𝜀 𝑖𝑗 = 𝜇 𝑗 + 𝑒 𝑖𝑗 ) of equation ( 1) is broken down into two parts. The first component is the unobserved heterogeneity 𝜇 𝑗 ∼ 𝑁(0, 𝜎 𝜇 2 ), it is specific to each municipality and constant between pixels in the same municipality. The second component 𝑒 𝑖𝑗 ∼ 𝑁(0, 𝜎 𝑒 2 ) varies between both pixels and municipalities. This decomposition of the error term of the model into its nesting components allows taking into account the spatial correlation of the error term in municipalities [START_REF] Anselin | Under the hood: Issues in the specification and interpretation of spatial regression models[END_REF][START_REF] Wendland | Regional-and district-level drivers of timber harvesting in European Russia after the collapse of the Soviet Union[END_REF].

The χ 2 statistic of the Hausman test allows accepting the null hypothesis of independence between errors and explanatory variables at the 1% level. Moreover, the statistics of the likelihood ratio test (3.9) between the model without multilevel (restricted model) and unrestricted model (with multilevel model) allows rejecting the null hypothesis of random effects. We then calculate the intra-municipality correlation 𝜌 = 𝜎² 𝜇 𝜎² 𝜇 + 𝜎² 𝑒 , According to the result, 24.15% of the total variance in deforestation is explained at the municipal level.

Finally, the joint nullity of all coefficients of contextual effects at the 1% level is also rejected.

Therefore, the multilevel model is more statistically efficient than fixed effects and can produce policy-relevant estimates while allowing a wider range of research questions to be addressed, for instance, the analyze of the impact of contextual effects.

The estimation results are presented in Table A1 in the appendix and the marginal effects in Table 2. Whatever the specification these results show that the parameters associated with PAs variables have a negative and very significant effect on the probability that a pixel is cleared. Based on the marginal effects of the multilevel logit model, we can say that the probability that a pixel is cleared decrease of 0.16 %, 0.04 % and 0.06 % respectively if the pixel is an indigenous lands, a sustainable use area and an integral protection area.

Indigenous lands seem more efficient because in all specifications their marginal effect is larger than those of sustainable use areas and integral use areas. The low level of marginal effects can be explained by the low rate of deforestation during the period studied: only 2.54% of the pixels have been cleared.2

Among the characteristics of location, accessibility of the pixel (distance to the nearest road) appears to be the most important determinant. Indeed, the more a pixel is away from the road, the more the probability to be cleared is low: this is the infrastructure effect [START_REF] Angelsen | Playing Games in the Forest: State-Local Conflicts of Land Appropriation[END_REF]. This variable seems to capture the whole effect of accessibility because, the distance to a river or large cities have no impact on deforestation. Rainfall tends to decrease deforestation: too much rain reduces agricultural profitability of the land to cleared. Finally the lack of significant effects of the slope and fertility can be explained by the low variability of these variables. Indeed, the Legal Amazon is mostly composed of fertile areas (92% of the land) with low slope (0.84% in average).

The average of sustainable use areas at the municipal level has a significantly negative impact on deforestation. In other words, sustainable use areas not only help to avoid deforestation at the pixel level but also contribute to reduce the deforestation rate in the municipality. This is a synergy effect. In contrast, the distance to the nearest road and rainfall contribute to reduce deforestation.

As regards the municipal context, forest area is an important factor of deforestation. A large forest area favors deforestation because it is a signal of the abundance of the land and the agricultural potential. However beyond 53% forest in the municipality, the availability of forest area reduces deforestation. Generally municipalities with high forest cover are far from main roads and the main markets. A high level of income per head reduces deforestation. Finally, as expected, a large number of mammal species is associated with lower levels of deforestation.

Propensity score weighting with multilevel data analysis

Following [START_REF] Pfaff | Governance, location and avoided deforestation from protected areas: Greater restrictions can have lower impact, due to differences in location[END_REF], we use matching methods to better assess the impact of PAs.

First, matching methods are nonparametric while in the previous regressions we have assumed a nonlinear relationship between deforestation PAs. Second, matching methods allow comparing the treated and untreated pixels similar in terms of observable characteristics. Finally, they are better adapted to the evaluation of public policies and allow to directly obtain the effect of treatment. However, the hierarchical nature of our data could bias the results of matching methods commonly used. To account for this potential bias we use the techniques of propensity score weighting with multilevel data that include fixed effects or random effects developed by Li et al. (2012).

The propensity score is the probability of being treated given the observable characteristics and is generally estimated using a logistic regression model (See for review [START_REF] Rosenbaum | The central role of the propensity score in observational studies for causal effects[END_REF][START_REF] Dehejia | Propensity score-matching methods for non-experimental causal studies[END_REF][START_REF] Heckman | Matching as an econometric evaluation estimator[END_REF]. Let 𝑃𝐴 𝑖𝑗 a binary variable equal to 1 if the pixel is a protected area and 0 otherwise. We want to compare a pixel i in the municipality j that is protected to a similar pixel, i.e. having the same individual and contextual characteristics but that is not protected. We compute the Average Treatment on the Treated (ATT): the difference in the probability of being cleared between those treated (Protected areas) and those untreated with the same probability of being treated.

We first estimate the likelihood or propensity score for a pixel to be protected using logit models:

𝑃𝐴 𝑖𝑗 = 𝜃 1 (𝑋 𝑖𝑗 -𝑋 𝑗 ̅ ) + 𝜃 2 𝑋 ̅ 𝑗 + 𝜃 3 𝑍 𝑗 + 𝛾 𝑗 + 𝜔 𝑖𝑗 (2)
with 𝑋 𝑖𝑗 location variables measured on the pixels, 𝑍 𝑗 contextual variables, 𝛾 𝑗 specific effects to municipalities, 𝜔 𝑖𝑗 the error term identically and independently distributed. In this equation we exclude PAs explanatory variables because they are endogenous; according to [START_REF] Wooldridge | Should Instrumental Variables be Used as Matching Variables?[END_REF] the use of endogenous variable in matching can make the estimators inconsistent. This propensity score enables building a control group of pixels that is statistically comparable to the set of pixels located in PAs. This counterfactual analysis allows for calculating the effect of PAs in terms of avoided deforestation:

𝐴𝑇𝑇 = [𝐷𝑒𝑓 𝑖𝑗 (1) -𝐷𝑒𝑓 𝑖𝑗 (0)| 𝑃𝐴 𝑖𝑗 = 1] (3)
We estimate the propensity score equation (Equation 2) for each type of protected area in two ways: i) with a multilevel logit model including the location variables and context variables, ii) and fixed effects logit model with location variables (contextual variables not varying within municipalities disappear). First, we calculate the ATT using the inverseprobability weighting method. This way of calculating allows two pixels having the same observable characteristics to be considered comparable even if they are not in the same municipality. The nonparametric clustered estimator allows for limiting the comparison between pixels of the same municipality. To calculate this effect we first calculate the ATT for each municipality using propensity scores computed with multilevel logit and logit fixed effect model:

𝐴𝑇𝑇 𝑗 = [𝐷𝑒𝑓 𝑖𝑗 (1) -𝐷𝑒𝑓 𝑖𝑗 (0)| 𝑃𝐴 𝑖𝑗 = 1] (4)
The ATT of nonparametric clustered estimator is calculated as follows:

𝐴𝑇𝑇 𝑐𝑙 = ∑ 𝑝 𝑗 𝑗 𝐴𝑇𝑇 ̂𝑗 ∑ 𝑝 𝑗 𝑗 with 𝑝 𝑗 = ∑ 𝑝 𝑖𝑗 𝑖
and 𝑝 𝑖𝑗 is the probability for a pixel 𝑖 to be a protected area in municipality 𝑗, this probability is calculated as specified in equation 2. Tables A3, A4 andA5 in the appendix report the results of regressions on the determinants of protected areas.

Matched unprotected versus protected areas (by protection type)

The estimation results are shown in table 3. For all estimation methods used, the results

show that whatever the type of PAs, the impact is robust. The impact of indigenous lands in terms of avoided deforestation is more important than the impact of integral protection areas and sustainable use areas. Avoided deforestation is at least 21,176 km 2 , 9,432 km 2 and 6078 km 2 respectively by the indigenous areas, sustainable use areas and integral areas; or at least 36,686 km 2 of avoided deforestation by all types of protected areas between 2005 and 2009. These results reinforce those obtained in the previous section.

The PSM is essentially based on observable characteristics of pixels. Unobservable characteristics (the lack of important explanatory variables) may bias the results. To analyze the impact of unobserved heterogeneity on our estimates we carry out a sensitivity analysis using the bounding approach proposed by [START_REF] Rosenbaum | Observational Studies[END_REF]. For binary outcomes, [START_REF] Aakvik | Bounding a matching estimator: The case of a Norwegian training program[END_REF] suggests to use the [START_REF] Mantel | Statistical Aspects of the Analysis of Data from Retrospective Studies of Disease[END_REF] test statistic. The negative selection bias occurs when the pixels most likely to be PAs tend to have lower rates of deforestation even if they were not protected and given that they have the same explanatory variables as the pixels in the comparison group. 3 The computation of this test is possible only for the marginal estimator PSM. Based on the assumption of negative bias selection, the tests suggest that our study is insensitive to a bias that would double the odds to be protected area for all specifications. The critical values suggest that our results are very little affected by a hidden bias.

Impact of protected areas by establishment's period (before 2000 and between 2000 and 2004)

The impact of protected areas should vary according to its creation period for two main reasons. First, from the 2000s, Brazil has undertaken a rigorous policy against deforestation.

We can assume that this policy has affected the choice of location of PAs to improve their efficiency. However, one could also think that the older a protected area is, the more restrictions of use are met and efficient.

Regression results shown in Table 4 are from sustainable use and integral protected areas data. The reason is that the creation dates of the indigenous areas are not always available.

In addition very few have been created between 2000 and 2004. Those recent PAs are found to be more effective than the old ones. Put differently, the localization of recent PAs seems more important than seniority. Indeed, new PAs have a greater agricultural potential since they are mostly located on fertile lands with a low slope and near the roads.

Propensity Score Matching for Multiple treatments

In the previous sections, we assessed separately the impact of each protected area on deforestation. Now, we consider that the protected area variable 𝑃𝐴 ̆𝑖𝑗 as a polytomous variable that can have one of the following four statuses: 0 for non-protected areas, 1 for indigenous lands, 2 for sustainable use areas or 3 for integral protection areas. The joint estimation of all of these effects improves statistical inference and allows to better define the control group and the comparison group. Thus equation (2) becomes a multinomial logit without specific unobserved heterogeneity to the group but with contextual effects.

According to Li et al., (2012), the introduction of contextual effects significantly reduces the bias linked to the hierarchical nature of our data. 5.

These results confirm the negative impact of PAs on deforestation at the 1% level obtained of separate estimates of the impact of protected areas. Furthermore we show that indigenous lands have been more efficient to avoid deforestation than sustainable use areas and integral protection areas. But we find no significant difference between the efficiency of sustainable use areas and that of integral protection areas.

Concluding remarks

The Brazilian Legal Amazon has already been the subject of numerous studies focusing on the impact of protected areas on deforestation. This paper adds to these studies while innovating on several methodological aspects. First we constructed a new remotely sensed data set constituted of 27,551 forested pixels in 2004 which could either be still forested or cleared in 2009. Second several estimators were implemented in order to control for location and contextual bias induced by the hierarchical structure of our remotely sensed pixel data set.

Multilevel models showed that whatever the specification the parameters associated with protected areas dummies reduced significantly the probability that a pixel is cleared.

Moreover it appears that indigenous protected areas are marginally more efficient than sustainable use areas and integral use areas. This is line with previous results suggesting that indigenous lands are more amenable to bring about support from local populations (Nolte et al. 2013).

Propensity score matching confirmed this result while evidencing a 36,686 square kilometers area of avoided deforestation between 2005 and 2009. Moreover recent protected areas are more efficient than older ones because they are mostly located in high deforestation pressure areas. Propensity score matching for multiple treatments do not however evidence a significant difference between the effectiveness of sustainable use and integral protection areas.

Impact analyses of protected areas on deforestation share a common feature. They only focus on the environmental benefits. An important question has not been explored so far that is related to a general cost-benefit analysis. Protected areas have been shown to generate avoided deforestation benefits, but their effects on other aspects such as poverty alleviation or local population welfare and income need more investigation. This would be in line with the so-called environment development dilemma.

Figure 1 .

 1 Figure 1. Distribution of pixels across Brazilian Amazônia

Figure 2 .

 2 Figure 2. Brazilian Amazônia's protection before and after 2004

Figure 3

 3 Figure 3 show that after 2004 the Brazilian Amazon experienced a decline in the annual deforestation rate decreased by 75% between 2005 and 2009.

Figure 3 .

 3 Figure 3. Evolution of protected areas and deforestation in the Brazilian Legal Amazon, square kilometers

  𝑃𝐴 ̆𝑖𝑗 = 𝜃 1 𝑋 𝑖𝑗 + 𝜃 2 𝑍 𝑗 + 𝜔 𝑖𝑗 Based on the work of[START_REF] Cattaneo | Efficient semiparametric estimation of multi-valued treatment effects under ignorability[END_REF],[START_REF] Wooldridge | Econometric Analysis of Cross Section and Panel Data[END_REF],[START_REF] Cattaneo | Estimation of multivalued treatment effects under conditional independence[END_REF], we use the three main existing methods 4 to estimate multivalued treatments causal effects: Regression Adjustment (RA), Inverse Probability Weighting (IPW), and Inverse-Probability-Weighted Regression Adjustment (IPWRA). The IPW estimator uses the inverse-probability weights, calculated using the treatment equation (multinomial logit model), to estimate the average treatment effects on the treated (ATETs) for each type of protected areas (𝐴𝑇𝑇 = [𝐷𝑒𝑓 𝑖𝑗 (1) -𝐷𝑒𝑓 𝑖𝑗 (0)| 𝑃𝐴 ̆𝑖𝑗 = 𝑡]), with t the treatment level of the treated potential outcome. The RA estimator uses separate estimates of deforestation on the explanatory variables to obtain the average of the predicted outcomes for each type of protected areas. The ATETs are obtained by taking the contrasts of the averages of predicted outcomes for each treatment level on the subsample of treated. Finally, using the estimated inverse probabilities (of treatment) weighting with regression adjustment IPWRA estimator allows to compute averages of treatment-level predicted deforestation. ATETs are the contrasts of these averages on the subsample of treated. The results are reported in Table

Table 1 . Location and municipal level characteristics Mean Mean-comparison tests (protected areas vs unprotected areas)

 1

					Integral
	Type of areas Unprotected	Protected	Sustainable	protected	Indigenous
		areas	areas (overall)	use areas	areas	lands
	Deforestation rate 2005-2009	0.0444	0.0027	-0.0385***	-0.0412***	-0.0428***
	Location characteristics
	(measured in 2004)
	Slope (degree)	0.7615	0.9408	-0.0705***	0.1982***	0.2576***
	Unsuitable lands (%)	0.0452	0.0919	-0.0425	-0.1239***	-0.1019***
	Moderate fertile lands (%)	0.6369	0.6209	0.0143***	-0.0421***	-0.0208***
	Very fertile lands (%)	0.3063	0.2798	-0.0173***	-0.0351***	-0.0278***
	Distance to the nearest road (km)	151.83	197.07	6.9215***	33.519***	60.061***
	Distance to the nearest river (km)	93.911	117.30	14.454***	35.010***	24.100***
	Distance to the nearest town (km)	338.40	446.04	51.337***	16.788***	107.64***
	Rainfall (millimeters)	1151	1199	31.613***	64.380***	49.412***
	Municipal context
	(average between 2005 and 2009)
	Forest cover (%)	74.628	80.277	4.529***	5.0383***	6.1338***
	GDP per capita (Constant 2000 R$)	4.9175	4.1290	-0.5331***	-0.3152***	-0.9630***
	Population density	3.8953	2.8703	1.5394***	-0.8233*	-1.9046***
	Number of mammal species	164.81	171.62	7.6073***	11.943***	5.5778***
	Number of pixels	15,012	12,539	2,706	1,582	8,251

Source: authors' calculations based on IBGE and INPE data. * Significance at 10%, ** Significance at 5%, *** Significance at 1%.

Table 2 . Marginal effects: Deforestation and protected areas

 2 Significance at 5%, *** Significance at 1%. Level one regressors are indexed by 𝑖 and ; contextual and municipal level regressors are indexed by 𝑗.

		Multilevel logit regression	Fixed effects Logit
		Marginal effect	Std. Err.	Marginal effect	Std. Err.
	Level one-regressors
	Indigenous	-0.0016***	0.0005	-0.2122***	0.0525
	Sustainable	-0.0004***	0.0001	-0.0621***	0.0197
	Integral	-0.0006***	0.0002	-0.0988***	0.0249
	Contextual effects
	Indigenous-M	-0.0000	0.0001
	Sustainable-M	-0.0005*	0.0003
	Integral-M	-0.0001	0.0003
	Location characteristics
	Level one-regressors
	Very fertile	0.0001	0.0001	0.0152	0.0189
	Slope	-0.0000	0.0000	-0.0022	0.0023
	Road	-0.0004**	0.0002	-0.0578**	0.0239
	River	-0.0000	0.0000	0.0000	0.0001
	City	0.0000	0.0000	0.0001	0.0001
	Rainfall	-0.0009*	0.0006	-0.1517*	0.0876
	Contextual effects
	Fertility-M	0.0001	0.0002
	Slope-M	0.0000	0.0000
	Road-M	-0.0004**	0.0002
	River-M	0.0000	0.0000
	City-M	0.0000	0.0000
	Rainfall-M	-0.0005*	0.0003
	Municipal context
	Forest	0.0000**	0.0000
	Forest_sq	-0.0000**	0.0000
	GDP	-0.0001**	0.0000
	Pop_dens	0.0000	0.0000
	Mammals	-0.0000**	0.0000
	sigma_u	1.0237***	0.0944
	rho	0.2415***	0.0338
	Observations	27115		12515
	Log lik.	-2265		-1685
	Hausman test
	χ statistic			3.29
	p-value			0.9515
	* Significance at 10%, **

Table 3 . Marginal and nonparametric clustered analysis

 3

		Weighted			Marginal		Clustered estimator
					Sensitivity analysis
				Coef	Gamma	Q_mh-	Coef
	Indigenous	Random		-0.0268 ***	14	0.1114	-0.0272 ***
				(0.0024)			(0 .0105)
	Avoided deforestation (km	2 )	21,176			21,492
		Fixed		-0.0294 ***	15	0.1083	-0.0486 ***
				(0.0028)			(0.0033)
	Avoided deforestation (km	2 )	23,231			38,402
	Sustainable Random		-0.0147 ***	1.9	0 .1126	-0.0214 ***
				(0.0040)			(0.0075)
	Avoided deforestation (km	2 )	9,432			13,731
		Fixed		-0.0192 ***	3	0.1091	-0.0209 ***
				(0.0041)			(0.0057)
	Avoided deforestation (km	2 )	12,320			13,410
	Integral	Random		-0.0234 ***	2.3	0.1006	-0.0210 ***
				(0.0016)			(0.0059)
	Avoided deforestation (km	2 )	7,688			6,899
		Fixed		-0.0252 ***	4.5	0.1045	-0.0185 ***
				(0.0051)			(0.049)
	Avoided deforestation (km	2 )	8,279			6,078
	Bootstrapped standard errors are reported in parenthesis. * Significance at 10%, ** Significance at 5%, *** Significance at
	1%. Gamma: odds of differential assignment due to unobserved factors and Q_mh-: Mantel-Haenszel statistic (assumption:
	underestimation of treatment effect).

Table 4 . The impact of protected areas by year of establishment

 4

						Protected areas established between
		Weighted Protected areas established before 2000	2000 and 2004
			Coef	Sensitivity analysis	Coef	Sensitivity analysis
				Gamma Q_mh-		Gamma	Q_mh-
	Sustainable	Random	-0.0112 ***	1.7	0.1011	-0.0211 ***	1.6	0.1178
			(0.0039)			(0.0049)
		Fixed	-0.0242 ***	2.9	0.1046	-0.0261 ***	3.2	0.1059
			(0.0056)			(0.0067)
	Integral	Random	-0.0215 ***	2.6	0.1144	-0.0256 ***	1.2	0.1005
			(0.0021)			(0.0009)
		Fixed	-0.0218 ***	3.0	0.1014	-0.0294 ***	1.7	0.1000
			(0.0064)			(0.0019)
	Bootstrapped standard errors are reported in parenthesis. * Significance at 10%, ** Significance at 5%, *** Significance at 1%.
	Gamma: odds of differential assignment due to unobserved factors and Q_mh-: Mantel-Haenszel statistic (assumption:
	underestimation of treatment effect).

Table 5 . Multivalued treatments analysis

 5 Standard errors are reported in parenthesis. * Significance at 10%, ** Significance at 5%, *** Significance at 1%.

		Regression		Inverse-Probability-
	ATET	Adjustment	Inverse Probability	Weighted
			Weighting	Regression Adjustment
	Indigenous vs none	-0.0178 ***	-0.0242 ***	-0.0156 ***
		(0.0015)	(0.0017)	(0.0022)
	Sustainable vs none	-0.0141 ***	-0.0195 ***	-0.0134 ***
		(0.0019)	(0.0022)	(0.0019)
	Integral vs none	-0.0154 ***	-0.0212 ***	-0.0134 ***
		( 0.0019)	(0.0022)	(0.0021)
	Indigenous vs Sustainable	-0.0037 ***	-0.0022 ***	-0.0046 ***
		(0.0013)	(0.0007)	(0.0014)
	Indigenous vs Integral	-0.0024 *	-0.0021 **	-0.0030 **
		(0.0014)	(0.0010)	(0.0014)
	Integral vs Sustainable	-0.0013	-0.00006	-0.0016
		(0.0019)	(0.0018)	(0.0020)

Table A4 : Propensity score equation of integral protection areas

 A4

		Multilevel logit regression	Fixed effects Logit
		Coef.	Std. Err.	Coef.	Std. Err.
	Location characteristics
	Level one-regressors
	Very fertile	-0.6539***	(0.1153)	-0.6537***	(0.1147)
	Slope	0.0321**	(0.0159)	0.0294*	(0.0158)
	Road	0.2038***	(0.0774)	0.2003***	(0.0771)
	River	0.0026***	(0.0006)	0.0027***	(0.0006)
	City	-0.0043***	(0.0006)	-0.0042***	(0.0006)
	Rainfall	-1.2296**	(0.5256)	-1.2316**	(0.5220)
	Contextual effects
	Fertlity-M	0.3578	(2.1788)
	Slope-M	-0.9508*	(0.4943)
	Road-M	0.0341	(0.5042)
	River-M	0.0061*	(0.0034)
	City-M	0.0045**	(0.0019)
	Rainfall-M	-3.8448*	(1.9830)
	Municipal context
	Forest	0.1186*	(0.0623)
	Forest_sq	-0.0015***	(0.0005)
	GDP	0.1002**	(0.0508)
	Pop_dens	-0.0130	(0.0198)
	Mammals	0.1286***	(0.0265)
	sigma_u	2.5986***	(0.2017)
	rho	0.8034***	0.0318
	Observations		27115		10339
	* Significance at 10%, ** Significance at 5%, *** Significance at 1%.

It refers to the modifiable areal unit problem (MAUP) when the way data is aggregated has an effect of the results.

In addition, in the computation of the probability of a positive outcome by the Stata command xtlogit it is assumed that the random and fixed effects are zero.

Given the negative estimated treatment effect, the bounds under the assumption of positive bias selection are somewhat less interesting.

We don't present Augmented Inverse Probability Weighting (AIPW) because it is not currently possible to compute ATT with this estimator.

Appendices


Laurent Lambert

Khalil Hachicha

Zahid Syed

Andrea Pinna

Andrea Ahmed

Patrick Pinna

Garda

Synchronizing physiological data and video in a telemedicine application: A multimedia approach

  L'archive ouverte pluridisciplinaire

I. INTRODUCTION

Telemedicine, in recent years, has emerged as a major new trend in medicine that exploits advancements in technologies such as communications, high speed internet and multimedia. The neurophysiological exams such as Electroencephalogram (EEG) are now often done with video recording of the exam, in order to facilitate remote diagnostic via telemedicine. This leads to two key issues. Firstly the equipment has to record all the exam modalities (physiological signals, audio, video, exam annotations, etc.) in a highly synchronized manner. The word synchronization refers to time and more precisely the temporal relations between media objects in a multimedia system. Secondly the data container format that ensures that all the gathered information are stored in a format that preserves all the synchronization information and makes the data exploitable by end user applications for diagnostics whether in-house, in same hospital or somewhere else via telemedicine.

Smart-EEG project [START_REF] Lambert | Smart-eeg : A new platform for tele-expertise of electroencephalogram[END_REF], [START_REF] Syed Zahid | SmartEEG : a multimodal tool for EEG signals[END_REF] aims to resolve both issues and it envisions a solution that provides highly synchronized data acquisition of all the exam components and stores them into a format that uses existing standards adapted to store this timed exam data. This paper is oriented to this second part and outlines the contributions of this work by proposing an MKV [START_REF] Lhomme | Mkv[END_REF] based solution.

This project is supported by Conseil régional d'Ile-de-France

The EDF standard [START_REF] Kemp | A simple format for exchange of digitized polygraphic recordings[END_REF] is widely used in hospitals to store the physiological data, all the medical tools used by the hospitals use EDF format or are EDF format compliant. This ensures interoperability among several hospitals or doctors even if they are not using the same tool. Hence, remaining compliant with EDF standard due to legacy reasons is an important aspect. However EDF standard was not designed to incorporate multimedia signals such as video, moreover it does not support compressed signals. This leads to a challenge to find an innovative manner that gives upgraded facilities that are needed to store timed information of multimodes, is streaming friendly as we are targeting telemedicine and yet also provides facility to keep EDF compliance of neurophysiological data if needed.

This paper is outlined as follows. Section II describes the state of the art in existing video-EEG exams and the limitations it has in terms of synchronization. Section III highlights two possible models of storing timed information data that can lead to multiple implementations that are discussed in Section IV. Section V presents our key innovations and how they help in resolving the problem. Finally Section VI concludes the discussions and mentions future outlook.

II. STATE OF THE ART

Current synchronization between physiological data and video are performed by saving the physiological data inside an EDF file and video in a multimedia container. A container is a file or stream which contains data of different kinds. It provides features such as temporal synchronization, encryption, tags, chapters, description of contained streams. The EDF file stores raw polygraphic recording while the video file usually contains compressed video and audio data. This encapsulation hierarchy is depicted in the figure 1. When playing back both type of signals, the two files are started synchronously and are assumed to stay synchronous during the entire sequence. It is easy to understand that one of the playbacks may drift in time and therefore loses synchronization. This methodology to store timing information is unsuitable for telemedicine usage where timing relationship between video and physiological signals is important. The drawbacks of this methods are :

• Playback synchronization cannot be guaranteed because the EDF file and the multimedia file does not have a common time base. • Physiological data is stored uncompressed. This limitation impacts both transmission bandwidth for telemedicine usage as well as storage for commodity tests. EEG tests performed over night are usually several Gigabytes and may benefit from a compression scheme. • Adding or removing information inside the EDF file involves recreating a complete file. Many Previous works have attempted to synchronize video and physiological data. In [START_REF] Zeng | A new video-synchronized multichannel biomedical data acquisition system[END_REF], Zeng et al introduced an acquisition system record allowing to acquire biomedical data along with video. Gackowski et al [START_REF] Gackowski | Development, implementation, and multicenter clinical validation of the teledicomadvanced, interactive teleconsultation system[END_REF] developed internet based tele Dicom to make the teleconsultation of medical image possible. Real time voice communication, visualization of synchronized digital imaging and communications is offered. Guerri [START_REF] Guerri | A multimedia telemedicine system to assess musculoskeletal disorders[END_REF] propose to use Java media framework (JMF) to synchronize audio, video , electromyograph data and lumbar device data. To achieve synchronization, the authors uses a glue file which maps timing from one file to the other. Campos [START_REF] Campos | Setting up a telemedicine service for remote real-time video-eeg consultation in la rioja (spain)[END_REF] has demonstrated the use of EEG by remotely viewing the acquisition screen. Even if this method gives close to perfect synchronization, it generates only a video file. This method makes hard to retrieve signals from the received stream. An alternative solution to provide compression alongside synchronization is to perform a data hiding method [START_REF] Pea | A data hiding technique to synchronously embed physiological signals in h.264/avc encoded video for medicine healthcare[END_REF]. In this method, one modality becomes a container for others. Usually, each image of a video sequence embed the data of the relative modalities. This way, when the decoder reads an image, it retrieves audio and physiological data. This scheme provides good synchronisation but has some drawbacks:

• One error in an image discard the related window of time.

• Removal of any track leads to decoding the main track, removing appropriate data and re-encoding the main track. Also, the removal of the main track is problematic and a new main track needs to be elected. • Using a video codec to encoded audio or physiological data lead to less efficient solution compared to specialized codec for every modalities.

III. CONTAINER MODELS

A stream of multi-modal data can be represented in a hierarchical manner. At the lowest level are samples. Samples are timed indivisible units of information. This represents the value of a physical quantity at a given time. For a video sequence, an image is a sample whereas for one dimensional signals such as audio, it is a scalar. In the latter case,

T rack1 T rack2 T rack3 t LDU11 LDU12 LDU13 LDU21 LDU22 LDU31 LDU32 LDU33 LDU34 LDU35 Channel1 Channel2
Fig. 2. Multimedia encapsulation, this example showcases two channels containing 2 tracks and 1 track respectively. In track one, each LDUs contain 4 samples, in track two each contains 3 samples each and in track three each contain 2.

samples are packed together temporally into Logical Data Units [START_REF] Blakowski | A media synchronization survey: reference model, specification, and case studies. Selected Areas in Communications[END_REF] (LDU) which are the second hierarchical level. LDUs are the atomic data which gets processed by a data container. Samples inside an LDU may be compressed to form a binary sequence. For that reason, LDUs are usually media agnostic that is it does not make any assumptions on the contained data. Those LDUs are categorized by their type to form the last hierarchical level: tracks (video, audio, physiological data). They are interleaved by the container handling synchronization. Often, they are ordered in chronological order. For completeness, a higher level exists which multiplexes channels or streams. This is, for example, used in TV broadcasting to stream multiple channel at once. Figure 2 exposes this encapsulation hierarchy.

A. Constant sample rate

The simplest model assumes that every LDU in a track is of constant rate. To calculate the maximum precision to place a LDU we use the following equation:

E = 1 2 * LDU rate
The LDU rate can be tricked by inserting dummy samples containing unreachable values or duplicated samples. In doing such, the LDU rate increases and therefore decreases the maximal error of the model. However, inserting those extra samples increases the file size. It is a trade off between precision and file size. Figure 3 illustrates two tracks synced by a constant rate container.

B. Timestamps based

The second model assumes non constant LDU rate. Therefore, LDUs are placed on a virtual time line referred to as

t t1 t2 t3 LDU11 LDU12 LDU13 LDU21 LDU22 LDU23 T rack1 T rack2
Worst error Fig. 3. Example of a constant sampling rate model. First track contains perfectly synced LDUs with the expected timings. The second track exhibits the worst precision, LDUs are exactly in between two expected timings.

the reference clock, which is of constant frequency. At each period of this virtual clock, a virtual counter is incremented. This counter represents the number of periods since the beginning of the media. Its value is conveyed alongside the LDU. The frequency at which the reference clock is incremented defines the finest grain at which you can place a LDU and is closely related to the maximum precision. Maximum error is when an event occurs right in between two ticks.

E = 1 2 * F ref clk
The width of the counter defines the time before the counter overflows. Usually, the counter is taken big enough so that this case does not happen. Figure 4 illustrates two tracks synced by a constant rate container.

IV. CONTAINER IMPLEMENTATIONS

A. Constant sample rate implementations 1) EDF+: An EDF+ file is composed of two parts, the header which contains information about the file and its signals. It is followed by data records containing actual data samples. One data record contains samples of every channel in a window of time. The size of this window is defined in the header. The size of the place holder for each sample is 16 bits. The LDU rate can be calculated as follows:

LDU rate = N samples T datarecord
Where T datarecord is the minimum time of a data record, N samples is the number of samples in one data record, the ratio of the two being the sample rate. The error is therefore directly proportional to the sample rate. T datarecord and N samples are both 8 ASCII characters. The error is minimised when T datarecord = "0.000001" and N samples = "99999999" which gives E = 50f s. This case would lead to a file growing at a rate of 20 Tbytes/seconds to store physiological signals only.

EDF+ has been proposed to add annotations support and non-contiguous recording by introducing a metadata channel. The place reserved for this channel is constant in every data record whether or not it is used to store annotations. This space is often unused and therefore wasted as it contains no information. The company BioSemi [11] has specified a BDF and BDF+ file to increase the size of a sample from 16 bits to 24 bits.

2) AVI: Audio Video Interleaved [START_REF]Audio video interleaved[END_REF] is a media container introduced by Microsoft. It is only designed to convey compressed video, audio and subtitles. The rate at which LDUs are presented is defined in the header of the file. Those rates are constant throughout the file. The sampling rates are written on 32 bit words and therefore the maximum expressible sampling rate is 2 32 . Thus, The maximum worst error is E = 1 2 * 2 32 = 0.11ns. This elementary container was later replaced by ASF.

B. Timestamp implementation

Most timing based implementations share the same hierarchical design. They are based on serialised objects which are identified by unique ID (GUID). An object may contain specific data specified by its ID or sub-object. For example, an object LDU is created which contains both a timestamp object, a data object and other meta data objects (specific track information).

1) ASF: Advanced Systems Format [START_REF]Windows media video[END_REF] is an object oriented container developed by Microsoft. The primary goals of ASF are efficient streaming capabilities and independence of any particular operating system and communications protocol.

It supports the inclusion of text streams, scripts and user defined data structure. LDUs are expressed in unit of 100ns which means that placing an LDU is in the worst case 50ns off timing.

2) The MPEG family: The MPEG systems family (MPEG1 systems [START_REF]information technology -coding of moving pictures and associated audio for digital storage media[END_REF], MPEG2 systems [START_REF]information technology -generic coding of moving pictures and associated audio information -part 1: Systems[END_REF], MPEG4 systems [START_REF]information technology -coding of audiovisual objects -part 1: Systems. Technical report[END_REF]) specifies how encoded LDUs are identified, synchronized and how meta-data are added to a stream. It is not to be mistaken with MPEG compression standards, which defines how data gets processed to reduce the size of an LDU.

In the MPEG1-systems specification, the multiplexing layer handles only video, audio and subtitles. Its synchronization layer uses a reference clock of 90 kHz, which induces a worst case error of 5.5us.

MPEG2-systems specification, improves those features by adding a user defined tag to the multiplexing layer and uses a 127Mhz reference clock. It also separates the usage of a single stream (program stream) used in DVDs for example and the transport of multiple streams (transport stream) used for broadcast applications.

MPEG4-systems gives the user the ability to configure the reference clock frequency. This frequency is at maximum 2 32 Hz, meaning that the best maximum error induced is 0.12ns. MPEG4-systems does no assumptions on the data it contains.

3) MKV: Matroska [START_REF] Lhomme | Mkv[END_REF] is an open standard container format. It was developped to offer a patent free alternative to the MPEG-systems family. It supports both streaming and file storage. MKV was designed to be flexible and extendible therefore no supposition are done on the LDUs. Timestamps are multiples of 1ns.

Furthermore, the format is based on a extendible language called EBML which enables it to have features such as seeking, chapters, meta-data inclusion, menu and encryption. This last part is useful in the field of telemedicine where, medical data secrecy is crucial.

4) SMIL: Synchronized Multimedia Integration Language [START_REF]Synchronized multimedia integration language (smil) 1.0 specification[END_REF] is an XML based synchronization language, which is actively used in Multimedia Messaging Service and for the SVG file format. It was designed for web applications by the World Wide Web Consortium. Its goal is to provide a temporal and spatial description of a scene containing heterogeneous media. The SMIL description specifies the relationships between media and provides hyperlinks to the actual data. It is not a container strictly speaking. This actually involves having multiple other files containing the actual data.

Although, SMIL represents time using timestamp, temporal information of an object is specified relatively to others. This usually means that the size needed to store temporal information is smaller. These relationships are described as h:min:s.ms., in which the number of decimals of milliseconds is unlimited. In theory, an error of zero may be achieved.

However, since the file is a textual description, its memory footprint is very large compared to a binary description. Moreover, SMIL is hardly extendible to use media that are not images, videos, audios and texts. Those two factors make it a bad candidate for our solution.

Table I summaries this section by comparing all implementations. Non timestamp implementations are not considered in our solution, as they involve increasing the amount of data to adjust the needed precision. Furthermore, SMIL does not meet our need of compactness since it has a textual representation. We choose MKV in our application because it's implementation is open and has no royalties unlike MPEGs container and ASF. However, those containers may be used if this criteria is not mandatory in other contexts.

V. OUR SOLUTION

Our solution is based on the MKV implementation. In order to incorporate physiological data, we have introduced a new track alongside the video and audio, which contains the physiological samples. Physiological data are chopped into windows of time to produce packets of data. In fact, each physiological LDU is created by copying a data record from an EDF+ file. By doing so, MKV provides resynchronization points between each data record. EDF+ is used as a codec inside of MKV. These LDUs are interleaved with video and audio packets by the MKV layer. We are using FFmpeg's implementation [18] of MKV to manage the synchronization layer. On the decoder side, LDUs are demultiplexed using their track ID and are placed temporally using the timestamp of each LDUs. Temporal relationship of samples inside a LDU is deduced by the sample rate. An implementation of this method composed of two modules is currently functional:

• An encoder side: encodes individual modalities with specialized codecs (video, audio, exg). From them, encoded LDUs are interleaved using MKV. The stream is sent on a TCP/IP connection. • A decoder side: reads the mkv stream from a TCP/IP connection. MKV provides the demultiplexing capabilities and the synchronisation layer for each modality.

LDUs are sent to the appropriate decoder and are then displayed synchronously. Along side this experiment, the data which travels on the TCP/IP connection is dumped in a file. The decoder is capable of reading this file and playing it back. This showcase the possibility of using this method for streaming application or for storage.

Our application uses this synchronisation scheme during an EEG exam, it includes the acquisition of Electroencephalogram, Electrocardiogram, Electromyogram, Electrooculogram as well as video and audio. This recording is afterward completed with the annotations of a technician. As a whole, this exam is sent to an expert for tele-diagnosis. It is also used for long term storage in an archive server.

Embedding physiological data inside a timestamp based container has multiple advantages:

• Resynchronization: Since every timing relationship is expressed as a common fine-grain time base, the capacity to correct the slight drift that one track may suffer is preserved. • Compression: Because this scheme may be used in a telemedicine context, the usage of a compression algorithm for physiological data is taken into consideration. Even if our implementation does not compress physiological data yet, the synchronization scheme does not restrain the capability of holding compressed data. • Insertion and withdrawal: Using multimedia container also simplifies the insertion or withdrawal of one track, as each track is independent. To remove a track, a simple filtering of LDUs of this type is necessary. To insert a new track, it may be accomplished by creating a new stream and interleaving the new LDUs. This operation can be achieved on the fly when reading a stream. Both operations do not require to decompress other media data. • Non-contiguous data: non-contiguous tracks, such as annotations, are easily integrable and have low overhead since no place is reserved for potential data. The annotations are present in the stream only when needed. Finally, as EDF is used as a codec inside the multimedia container, extracting the original EDF file is as simple as concatenating every LDUs of the physiological track. This is important since most hospital may not own the required software to read this new file format.

VI. CONCLUSION

Physiological signals coupled with synchronized video helps in improving interpretation of exams in which ordering of events is critical. In this paper, we have presented how we have integrated physiological data inside a multimedia container for streaming and storage purposes. This scheme is flexible enough to provide compression of physiological data if the need arises. Moreover, deleting or inserting new correlated data in a stream is simple enough as each modality remains independent. Furthermore, retrieving EDF file from our stream is trivial. These qualities make this solution a good candidate in the field of telemedicine.

Future works include integrating this file format as an exchange file in the Smart-EEG project. This synchronization mechanism will be used in a clinical evaluation of telemedicine applied to the EEG tests in France.

Fig. 1 .

 1 Fig. 1. Current synchronisation between EDF and video

Fig. 4 .

 4 Fig. 4. Example of a timestamp model. First track contains perfectly synced LDUs with the reference clock. Second track exhibits the worst precision when LDU 21 is exactly in between two reference clock ticks. Arrows show the rounded timestamp.

Fig. 5 .

 5 Fig. 5. Our solution to synchronize physiological data and Video

TABLE I SUMMARY

 I TABLE OF THE PERFORMANCE AND FEATURES OF EACH CONTAINER Container Maximum error Support compressed data Timestamp model Free of usage Agnostic Binary format

	EDF+	50fs	N	N	Y	N	Y
	AVI	0.11ns	Y	N	N	N	Y
	ASF	50ns	Y	Y	N	N	Y
	SMIL	unlimited	Y	Y	Y	N	N
	MPEG 1 systems	5.5us	Y	Y	N	N	Y
	MPEG 2 systems	4ns	Y	Y	N	Y	Y
	MPEG 4 systems	0.1ns	Y	Y	N	Y	Y
	MKV	0.5ns	Y	Y	Y	Y	Y

ACKNOWLEDGMENTS

This paper is based upon the work in the Smart-EEG project that is funded by the Conseil régional d'Ile-de-France. This project is conducted jointly with the companies CIRA, Partelec, ACACIA, 2CSI, the hospitals Georges Pompidou and Lariboisière, the ETIS labs and LIP6. The authors thank all the project members for discussions