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Contents lists available at ScienceDirect
Ocean and Coastal Management journal homepage: www.elsevier.com/locate/ocecoaman
Building a local spatial data infrastructure (SDI) to collect, manage and deliver coastal information Luis Americo Contia,∗, Homero Fonseca Filhoa, Alexander Turrab, A. Cecilia Z. Amaralc a
Escola de Artes Ciências e Humanidades, Universidade de São Paulo (EACH - USP), Av. Arlindo Bettio, 1000, São Paulo, 03820-000, São Paulo, SP, Brazil Instituto Oceanográfico, Universidade de São Paulo (IO - USP), Brazil c Departamento de Biologia Animal, Instituto de Biologia, Universidade de Campinas (IB - UNICAMP), Cidade Universitária Zeferino Vaz, Barão Geraldo, Campinas, SP, 13083-970, Brazil b
A R T I C L E I N F O
A B S T R A C T
Keywords: Coastal zones Spatial data infrastructure Geodatabase Coastal management Marine data model
Mapping techniques and spatial data management in coastal areas are still the subject of methodological and technological development, especially regarding systems for the organization and integration of data and information. The availability of integrated and comprehensive spatial information within the context of a multidisciplinary project is therefore an important step towards to defining methodological solutions to the characterization of coastal environment and their interrelations for management. The “Biota/Araçá Project” (Biodiversity and functioning of a subtropical coastal ecosystem: subsidies for integrated management) is a multi-disciplinary program of biodiversity assessment of a small bay in Sao Paulo coast (Brazil). This paper discuss the issues involved in establishing a local spatial data infrastructure (SDI) developed to allow access, model and sharing of spatial data within such collaborative and multidisciplinary research project context.
1. Introduction The importance of coastal zones has led several countries towards developing research and management plans for the protection of such environments (Celliers et al., 2013; Mulder et al., 2011; Portman et al., 2012). These efforts are reflected in the economic investments in studies that aim to increase the knowledge of costal processes in order to minimize the impacts of anthropogenic activities and natural hazards that generate threats to humans and the environment. Additionally, this knowledge is used develop plans for sustainable natural resource use and ecosystem service management (Davidson-Arnott, 2010; Woodroffe, 2002). Brazil has a coastal zone that extends for approximately 9200 km, but is yet to develop comprehensive research and management plans. Currently, the initiatives are isolated and deal with particular problems in an ad hoc manner and are progressively adapted (Hoefel, 1998; Turra et al., 2013). Many of these solutions are ineffective because they are rushed without proper attention to and study of the techniques employed. Governance action should be based on multidisciplinary projects in marine and coastal areas that aim to achieve an integrated environmental characterization, therefore, geographic information is a fundamental component for the implementation of effective evaluation and monitoring programs (Soranno et al., 2015). According to Goodchild
∗
(1987), it is imperative that information models represent the multidimensional nature of reality as closely as possible; the more complex and dynamic the data universe, the greater the complexity of the information. New methodological approaches that involve mapping and creation of information systems of coastal and marine areas, based on the evaluation of biotic, abiotic, and anthropic properties, have been proposed by researchers in several parts of the world to determine the vulnerability and adaptability of these areas to environmental variations (Bartlett, 2000; Rodríguez et al., 2009; Tribbia and Moser, 2008). Hence, a wide range of projects, involving spatial data integration, that aim to develop and implement conservation and management policies have been developed, however, there are gaps in the effectiveness with which geographic information flows through different stages: acquisition to validation, processing, and web publishing. Methodological and technological developments in mapping techniques and spatial data management for coastal areas are still required, especially for applications that organize and integrate data and their use as a management tool. In this way, the creation and development of a data management system within the context of a multidisciplinary project can be an important step towards methodologically defining solutions to this issue. In this context, shared spatial data management tools and Coastal
Corresponding author. E-mail address:
[email protected] (L.A. Conti).
https://doi.org/10.1016/j.ocecoaman.2018.01.034 Received 30 July 2017; Received in revised form 15 January 2018; Accepted 31 January 2018 0964-5691/ © 2018 Elsevier Ltd. All rights reserved.
Please cite this article as: Conti, L.A., Ocean and Coastal Management (2018), https://doi.org/10.1016/j.ocecoaman.2018.01.034
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has been exposed to various human interventions, such as irregular occupation of coastal areas, discharge of domestic sewage, frequent oil spills/leaks due to the activities of the Port of São Sebastião and Waterway Terminal of Petrobras, for many years (Amaral et al., 2010; Peres et al., 2016). An important feature of this project was the need for effective interaction between teams of researchers with different areas of expertise. Such an integration of researchers from diverse areas provides a strategic background for knowledge and capacity building and development of human resources (Amaral et al., 2016). The implementation of this project has been divided into 12 thematic modules structured around three overlapping knowledge domains. Each module has been implemented with specific approaches of gathering, processing, and storing information based on the techniques, methods, and experiences inherent to each discipline. Thus, one particular module (in this study) has been implemented to develop the CSDI. Since a large amount of data and information was generated, it was necessary to create a specific module responsible for the harmonization, organization, and ensuring availability of these data between the modules and for external users (Fig. 2). The conception and implementation of the CSDI is usually relevant for global, national and regional initiatives where the coastal data and information are integrated and distributed within broader management programs such as the European “INSPIRE”, Asia Pacific SDI, and Global Spatial Data Infrastructure (GSDI) (Longhorn, 2004). In contrast, the CSDI implementation in Biota/Araçá Project uses a local, intra-organizational, service-oriented SDI in order to allow data and information sharing between researchers and stakeholders linked to a specific research area for the identification and collection of data and information from diverse sources and of diverse natures. In this paper, we discuss some implementation issues, potentialities, and restrictions regarding the use of a local CSDI. A critical review of the process is established, and key ideas and lessons learned are discussed. Additionally, “best practices” for the organization and integration of the CSDI, in similar projects, are generated.
Spatial Data Infrastructure (CSDI) are key instruments for integrating information and sharing it between different areas of marine and coastal research and management (Kelly and Tuxen, 2003; Oliveira et al., 2014). These technologies fulfill the global requirements of facilitating access to and sharing of spatial data held by many stakeholders to maximize its use, while reducing the cost of data management and production (Georis-Creuseveau et al., 2017). In this study, the methods for management and distribution of spatial data based on a data model and Web Mapping services are assessed. A methodological basis for implementing a local CSDI for a multidisciplinary study involving aspects of biodiversity, environment, and management was established. The framework created was generated in a multidisciplinary research project titled “Biodiversity and functioning of a subtropical coastal ecosystem: subsidies for integrated management” applied to the region of Araçá Bay, located on the northern coast of the State of São Paulo. This project will hereafter be referred as “Biota/Araçá Project”. The project was implemented aiming at the use of this small coastal area, susceptible to a wide range of natural and anthropogenic factors, to understand the importance and functioning of the ecosystem and its relationship with the local communities. 1.1. Study area and context Araçá Bay is a small bay located on the São Sebastião channel in the State of São Paulo (Fig. 1). It is bordered by rocky flanks and covers four beaches, two islets, three main cores of mangrove forests, and an extensive soft-bottom plain, which is entirely exposed during spring tides (Amaral et al., 2016). These morphological features prevent the region from being directly affected by external hydrodynamic factors of the São Sebastião channel. The physiognomy of Araçá Bay is composed of beaches with smooth slopes and intertidal zones with widths in the range of 50–300 m. The Araçá Bay bottom sediment is composed mainly by fine and very fine sand with patches of mud and, and gravel sediments, thus, it can be classified as a protected tide-dominated bay. However, it also suffers the action of low amplitude waves. Since this stretch of the coastline is in close proximity to urban infrastructures, it
Fig. 1. Study area: The Araçá Bay.
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Fig. 2. Organization of the thematic modules of the Biota/Araçá Project.
Fig. 3. Workflow for spatial data management in the biota/Araçá project.
of policy and technological issues. This strategy followed the five principles of best practices of coastal data management proposed Sayers et al. (2000) with modifications. These principles are 1-Data understanding, 2-Legal framework, 3-Processes and procedures, 4- Enabling technologies, and 5- Audit. To implement these principles in a meaningful way, a general data management policy was developed. The implementation process was organized into three phases. “Phase 1” involves establishment of data and metadata organization and sharing practices (the combination of “data understanding” and “legal framework”). Formats and mechanisms to allow interoperability and sharing of data between different thematic areas (or thematic modules) were defined to guide the acquisition and distribution of data. In this phase, parameters relevant for correlation and interoperability between different types of data (i.e., defining gaps and addressing sampling and acquisition processes) and legal and conceptual policies for implementation were also established. “Phase 2” involves the implementation of a spatial data framework (related to principle 3: “processes and procedures”) to establish and
2. CSDI organization 2.1. Conceptual framework The creation of a local SDI provides an effective way to facilitate better organization and sharing of spatial information between experts in different knowledge areas. This is especially in important in multidisciplinary fields such as coastal and marine sciences, where experts with different knowledge backgrounds have to work together thus generating a wide variety of data patterns and standards (Najdawi and Ghatasha, 2012). Furthermore, since marine and coastal environments do not have well-defined geographical borders or boundaries, mapping and characterization of such environments is usually carried out by integrating data and information of different natures, both in scale (spatial and temporal) and thematic characteristic (the structure and format of each level of information). The complexity of the strategy used for organizing a framework for the generation of a CSDI in the Biota/Araçá Project was a combination
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thematic modules) allows data to be accessed and modified; 2-“restricted” (exercised by all project members) allows data to be only queried and viewed; 3- “closed” allows access to only the metadata. Complementary to the development of agenda for the workshops and meetings between the data management technical team and the representatives of each module, guidelines regarding the format and structure of data and metadata were established. These guidelines are comprehensive and cover all aspects, from the standardization of geodesic reference systems, to the homogenization of vocabulary and definitions (ontologies and semantics) of such systems and were also established to gain an understanding of how each module produced data (i.e., tables, points, images, maps, etc.) and metadata and how these could be adapted into the data model proposed in the project. Such meetings also aimed to adapt information already generated and use the information generated to guide the collection of new data in a future workgroup approach (Nebert, 2004). Although the workshops and supporting efforts have been reasonably successful in removing apprehensions of the researchers regarding the spatial data policy of the project, major questions and barriers remained. These mainly concern the heterogeneity of usage and the proportion of spatial data “production/consumption” within different thematic modules. Even with the high level of participation of researchers and formal commitments and agreements among them, the diversity of data systems and the timing and complexity of data acquisition and collection compromise the effectiveness of data integrating mechanisms. Therefore, even after several meetings and workshops were held to define data usage practices and standards, it was necessary to maintain an organized taskforce to constantly evaluate the quality and integrity of the data and metadata and to organize the data structure (e.g., convert formats, adjust resolution, and define patterns of representation). One possible solution to this issue is the development of digital data collection and organization platforms that can automatically organize data and metadata parameters to facilitate connection between data generators and data providers. This was suggested by researchers working on areas such as biodiversity, geology, environment, and management (Green and Hagon, 2017; Jones et al., 2004; Pimm et al., 2015; Snaddon et al., 2013; Teacher et al., 2013).
implement a data model and define semantic and ontology rules that allow convergence, interoperability, and manipulation of complex data between thematic modules. Mechanisms to organize, store, and distribute data within the project modules are developed. “Phase 3” involves developing map server services (principle 4:“enabling technologies”) to manage and distribute geographic data between modules through an Internet geoportal. This provides a practical basis for delivering multi-information mapping models of the Araçá Bay. The processes of auditing and assessing the integrity and accuracy of the information (principle 5) have permeated all the executive phases. All the phases are embedded in a general workflow scheme for the management of spatial data generated in this project (Fig. 3). In subsequent sections, we have provided some discussions and critical analyses of major issues regarding the development and implementation of such processes. 2.2. Data and metadata organization and sharing practices The scientific information generated in environmental projects of coastal and marine areas are extremely valuable and complex. Therefore, for such projects, the establishment of a policy for sharing and accessing the data generated should be a priority to harmonize conflicting interests. For instance, some researchers may want to reserve the data generated for publication while other researchers may want to make these data available for use by other researchers or stakeholders. Unlike most regional and national repositories of geographic data and geoportals in which most of the information is derived from secondary sources such as local and national organizations, a local research project tends to produce unpublished data of scientific relevance. Therefore, compliance with policies for the protection of intellectual property should be a priority. Moreover, technical factors such as specific information standardization methods and systems and formats for collecting and organizing data and metadata are important for developing consistent data and information sharing practices (Elwood, 2008; Hale et al., 2003; Michener, 2015; Peters et al., 2014). On the other hand, Wallis et al. (2013) pointed out that slow adoption of tools and services such as data repositories indicates that technology alone cannot change the sharing practices of scientists; social and cultural factors should also be considered to encourage data sharing. Such policies should be based on how researchers produce data and how they use the data that has been shared with them. In the Biota/Araçá Project, the organization of a spatial data sharing policy followed a framework that consists of four steps. These steps are 1-Development of a meeting agenda between researchers; 2Communication and engagement efforts; 3- Evaluation and improvement; and 4- Establishment of supporting plan (Halpern et al., 2012). The main goal was to establish a participatory and collaborative method of integrating the information generated by various thematic modules. This will not only allow access to all the datasets generated in the project, but also determines the potential of integrated analysis and processing to some level (Fig. 3). The communication step was mainly carried out during events (workshops) where the CSDI group disclosed and discussed theoretical and objective aspects of the mechanisms of data usage and access and their importance within the scientific context of the project. In addition to specific issues such as level of accessibility, particular needs, and guidelines for interaction between the modules, the participant researchers were acquainted with the “data protection & information sharing policy” of the project and the funding agencies through a “consortium approach” (where members share general costs and benefits to optimize the process). Details are available in Devarakonda et al. (2010) and Harvey et al. (1999). At this stage, it was established that the data and information managed by the CSDI group would have three basic levels of accessibility. These levels are 1- “open” (exercised only by coordinators of
2.3. Implementation of the spatial data model In the second phase, the spatial data was organized as a local geodatabase on a “desktop” system in an Esri format (*.Gbd). An internal routine for the identification of errors, omissions, and inconsistencies in the gathered data was established by implementing new data standards. The integrity and accuracy of the information in the generator modules were constantly ascertained (Fig. 3). To accomplish this task, extensive efforts were spent to organize and harmonize the data from its original format to a format that is compatible with the data model used for this project (i.e. by transforming the raw observations and pre computed values from Excel sheets to spatial “feature class” formats). First, thematic modules were divided into three groups according to their structure and data delivery. These groups are “biodiversity”, “physical surrogates”, and “management and governance” (see Fig. 2). The biodiversity group added the modules “Benhtic”, “Nekton” and “Plankton”. The data generated in these modules were obtained from four sampling campaigns (seasonal). Therefore, the information was primarily organized as “point feature based objects”, where data on the taxonomic and accessory parameters (such as date, collection instruments, and environmental conditions) were stored in a structured database. Since each module established its own sampling designs and strategies in data structuring patterns, it was necessary to establish a normalization process to homogenize the presentation of the data. The taxonomic information was linked by a “URL” (http://sinbiota. biota.org.br/) to the São Paulo State's biodiversity information system, 4
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Fig. 4. -Example of taxonomic data accessed from the Biota/Araçá database that is linked to the SinBiota information system by a url tag in the geodatabase field.
“SinBiota”, where each sampling point is identified by a general system number and all the collected species were associated with the corresponding taxonomic reference (Chapman, 2001) (Fig. 4). The numeric/ quantitative parameters related to biodiversity data (e.g., biomass, species richness, and species number) were gridded on a regular grid (surface) with 1 × 1 m cell size. The physical base gridded dataset was grouped in to raster catalogs and resampled to a common spatial resolution of 1 m. A hierarchy was established to determine which information should be included in the geodatabase and which should be restricted since not all the generated geographic information can be made available (either to maintain technical relevancy or due to the conditions in the specific data sharing policy described previously). For instance, in the case of bottom sediment samples, although sample replicates were collected at each collection station, only the original sampling data were stored in the geodatabase. Table 1 lists the 46 parameters stored (with seasonal repetition) in the geodatabase. Fig. 5 shows examples of the distributions of some of the physical environmental parameters on a map of the study area. In order to effectively integrate all the data (to perform habitat or ecological modeling for instance), the datasets of raster surfaces were encoded to a Polygonal Hexagon Grid Database (PHGD) with a resolution of 5 m. All the parameters were integrated as fields in each hexagonal cell (Birch et al. (2007), Carr et al. (1992), and Polisciuc et al. (2016)). For each hexagon, a zonal statistical function was applied to extract the mean value of the analyzed parameters. Thus, the entire area of the bay was covered by geo-units, where each unit gathered all the information generated by the project for a particular location. This allows multi-parameter analysis and object-oriented modeling (such as niche modeling, query operations, and topological relationships). The advantage of using the PHGD rather than an ordinary raster catalog is that the inherent simplicity of the database structure allows researchers
from thematic modules that deal with data integration such as the “trophic interactions”, “ecological modeling”, and “ecosystem services” to access the data in a simple query format from each unit (Fig. 6). The data for the management and governance spatial dataset group were mostly acquired from federal and state agencies such as the environmental agency of the State of São Paulo (CETESB) and the Brazilian Institute of Geography and Statistics (IBGE). These agencies provided data on coastlines, protected areas, urban infrastructure, and coastal and maritime planning, mostly in the vector format (shapefiles). Since information in such datasets is restricted to the areas from terrestrial domain, data and metadata were incorporated into the database as “complementary information”. The database and the data management system were translated to an ArcMarine Data Model (AMDM) (Wright, 2007). A data model can be thought of as a conceptual model that allows a common structure of data acquisition and manipulation of a given specification to be built. The AMDM was specifically built to encompass the peculiarities of marine data manipulation and storage including the possibility of association between and integration of vector and raster data. In the present project, all the information layers were organized and assembled in the ArcGIS system in which all the thematic modules are organized within the conceptual context in a database associated with the AMDM. The general process of adaptation and creation of a “Biota/ Araçá Project data model” is based on the AMDM structure with local adaptations indicated by the characteristics of each module. The framework constructed is shown in Fig. 7. The color of the rectangles indicates the general spatial data format (point, line, polygon, or raster) and the acronyms refer to the marine data types. Using the AMDM has produced good results in the modeling of benthic habitats in marine areas since it utilizes the potentials of the ArcGIS system (Vetter et al., 2011). However, due to a recent development, constant updating of theoretical and methodological bases is
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documentation throughout the process. In order to gain a better understanding of the process, researchers were required to fill out a questionnaire to assess the efficiency of the information organization process, from the collection of data to the implementation of the AMDM formats. More than 80%of the researchers surveyed answered that data pre-processing has consumed a significant amount of time and resources. It was estimated that 10–20% of the research time was spent on data organization. Therefore, despite the fact that the AMDM has facilitated the overall implementation of the local spatial data infrastructure, the development of tools that aimed to increase the effectiveness with which information was transferred from the data providers to the data management group was not always effective.
Table 1 -List of parameters used in the geodatabase. Data type
Data source
Parameter
Repetition
Bathymetry
Echo sounding
Bottom sediment
Satellite Sampling/ gridded
Depth Declivity Aspect Tidal zones Depth Mean diameter
1 1 1 1 1 4
Sorting Skewness Kurtosis % Gravel % Sand % Mud Texture Al
4 4 4 4 4 4 1 2
Fe As Cd Cr Cu Ni P Pb Sc Sn Zn UCM Aliphatic PCI C N S CaCO3 Mean
2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 4 4 4 4
Max Bacterial activity
4 4
Chlorophyll Heterotrophic nanoflagellates FeO pigments Margalef index Chlorophyll
4 4 4 4 4
Temperature Salinity O2 Distance to coastline Distance to sediment source Distance to sampling points
4 4 4 1 1 1
Geochemistry
Side scan sonar Sampling/ gridded
Orbital Velocity
Hydrodynamic model
Biological Activity
Sampling/ gridded
Water
Geography
Sampling/ gridded
Buffer
2.4. Development of a coastal web atlas (CWA) Based on the established data and metadata infrastructure, involving established technologies, policies, and standards, of the AMDM, a distribution tool was created for the sharing and dissemination of georeferenced information via the internet. The “web” maps, also called “web atlas” or “geoportals” (Coastal Web Atlas or CWA), allow the visualization of and access to geographic data through a set of geospatial services available on the internet. Additionally, they allow the execution of analysis and modeling processes that could only be conducted by specialized teams in a “desktop” environment in the recent past. Such advancements allow a wide dissemination of the technologies used for the construction and development of these applications at different levels, from local tools to large national and continental systems (Merrifield et al., 2013; Mitsova et al., 2013). Examples of national and regional CWA can be described in Haddad et al. (2005), O'Dea et al. (2011) and Kopke and Dwyer (2016): UK Coastal and Marine Resource Atlas (CAMRA) (http://magic.defra.gov. uk); De Kustatlas Online, Belgium (DKO) (http://www.kustatlas.be); The Marine Irish Digital Atlas (MIDA) (http://mida.ucc.Ie); Oregon Coastal Atlas (OCA) (http://www.coastalatlas.net); North Carolina Coastal Atlas (https://www.nccoastalatlas.org/) African Coastal atlas (http://www.africanmarineatlas.org/). The main purpose of such a system was to facilitate access to spatial data and to disseminate information, especially information regarding variable distribution criteria and interactive mapping services. Another purpose was to establish the demands of and gaps in the information in order to increase the accessibility of project data by researchers and the public. Such tools are described in literature as a fundamental instrument for the organization of national territories. This illustrates the growing international recognition of the potential advantages of marine data systematization (Conti et al., 2013; O'Dea et al., 2011). The entire structure of the data and metadata was established based on the ESRI standards, created through the “ArcGIS Server”. The same captive standardization process was followed for the web tools. The ArcGIS for Server is a geographic data distribution platform that provides maps, models, and tools to users within the organization and on the internet. Information is managed through multiuser applications (Law, 2013). In order to allow access the project information stored in the AMDM system via the ArcGIS Server, the database needs to be configured into a “PostgreSQL” tool and its spatial database extender, “PostGIS”, on the server. This allows remote users to access and edit project data without necessarily having direct access to the central application. PostGIS is a free library with an open source font code that enables the use of objectrelational Database Management System (DBMS) i.e., “PostgreSQL” for spatial data. PostGIS provides more than 300 operators and spatial functions within the DBMS (Obe and Hsu, 2015). The generation of maps and applications (web services) and the organization of the database inside the server were done through the “ESRIPortal”. An application within this portal allows the data coming from the server to be published in the form of a set of maps (web atlas/
required. Therefore, using the AMDM in a multidisciplinary project such as the Biota/Araçá Project is a considerable methodological challenge. One of the limitations of implementing the AMDM is that much of the data collected for coastal areas were related to terrestrial parameters (management and governance spatial dataset group) what implies to consider new relationship between features or simply not formally fit any category defined in AMDM, and organize it as an “ordinary” component (e.g., feature class) within the database. Another limitation is the complex and time-consuming process of formatting and adapting the information generated by data providers to the final structure of the data model. According to Soranno et al. (2015), for integration of a relatively manageable number of datasets, merging can be done manually and well-informed quality control and assurance checks can be completed using the expertise of individual datasets. However, the creation of a large number of curated data products uses “scaled” (i.e., automated) methods that require extensive
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Fig. 5. -Examples of the distribution of environmental parameters on a map of the Araçá Bay. (A - sediment mean diameter; B-% of sand in sediment; C - organic carbon content; D -sorting; E-mean orbital velocity; F-bathymetry; G-total benthic biomass; H-calcium carbonate content; I-side scan profiles).
organized. While the project is in progress, since researchers and users are still feeding and using the database, the database is automatically maintained and updated. However, after the end of the programs by research financiers, the continuity of the availability of resources is usually compromised. Unlike large national and multinational information availability programs, where specialized organizations and institutes ensure the maintenance of atlases and geoportals, local projects need long-term support to sustain such programs.
geoportal, Fig. 8) and other applications such as historical maps and thematic tools. A number of related coastal and marine web atlas projects at a supranational scale have developed with broad support from national and local environmental agencies. Such projects involve the integration of biotic and abiotic data to serve as a basis for the establishment of common environmental policies. These efforts are mainly focused on the development of methods and approaches that allow the integration of a large amount of information of distinct nature, scale, precision, and structure (Kopke and Dwyer, 2016). The creation of a CWA (and the infrastructure and database organization that precedes it) allows delivery, analyses, and processing of data and information. Most of the current systems are still in the embryonic stages in this respect. However, the consensus within the scientific community is that the availability of an ever-increasing amount of data and the development of cloud computing processes that displace most of the analysis and manipulation processes for the network itself will allow webatlas systems to serve as a basis for derivative research projects aimed at analyzing and applying data. This is especially true for coastal management and conservation programs such as the National Coastal Management Plan (NCMP) and Marine and Coastal Space Planning Programs (MCSPP) (Conti et al., 2013). Once the CWA is implemented, a taskforce that is responsible for the constant maintenance and updating of the database needs to be
3. Discussion and lessons learned The processes of normalization and harmonization of data and metadata in a coastal zone management project is quite complex. This is mainly due to the multidisciplinary and comprehensive nature of the different areas of knowledge (organized into thematic modules in this project), which have different objectives, data formats, timing, and needs. Thus, the organization of a data management group in this project was fundamental for integration of the modules. However, such proposed policies and standards should be implemented as soon as possible in order to guide all the phases of information management and usage. The establishment of constant channels of communication between different data producers for the creation of standards and policies to make the data interoperable and compatible was fundamental.
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Fig. 6. -Example of data accessed from the Polygonal Hexagon Grid Database (PHGD). Each geo-unit (hexagon) contains information about all the parameters considered in this project.
increase awareness about existing opportunities to improve sharing of coastal and marine data among scientists, politicians, and managers (Georis-Creuseveau et al., 2015). Frequent consultations and interactions with organizations like this are fundamental for the establishment of connectivity and interoperability between the data and admitted, but of the research project and national and international systems. This issue needs to be addressed by the developers of databases and tools of the local SDI. Regarding the technical aspects of data organization, both the form of information storage and management (the “Arc Marine Data Model” for the organization of and allowing access to the original datasets and the PHGD to integrate data and perform multi-parametric analysis) and the sharing and dissemination tool (the coastal web atlas) were of particular interest. These allowed integration of data with different natures (i.e., abiotic and biotic) and formats (e.g., raster, vectors, numeric, and thematic) on the same basis. Additionally, they allowed the integration and interoperation of data generated in this project with those in other information services. However, only when the form and frequency of access and the use of data by researchers are evaluated, it will be possible to effectively establish the effectiveness of the SDI created.
Thematic meetings and frequent workshops significantly facilitated the dissemination of information on the importance of implementing an IDE that optimizes efforts spent on the acquisition, organization, and maintenance of data to improve its accessibility and usability (see Strain et al., 2006). Since the elaboration of a local SDI presupposes a hierarchical integration of organizational levels, for a local data integration project to be effective, establishing interoperability standards and policies with regional or national (and even global) systems is fundamental. This ensures that the use of the data and information generated are not restricted within the core of the project but that it serves other users within a broader context. The “Biota/Araçá” project sought to connect the data standards to the Brazilian National Data Infrastructure (BNDI). However, this initiative (implemented in 2008) does not include the specificities of the coastal and marine environment. In this sense, organizations such as the International Oceanographic Data and Information Exchange(IODE) seek to encourage the sharing of experiences through the International Coastal Atlas Network (ICAN) and the marine/coastal SDI best practices project. This will help find common solutions for the development of standards and policies for data organization through tools such as Marine Web Atlas. Such solutions
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Fig. 7. Framework of data layers constructed based on the Arc Marine data model (adapted from Wright, 2000) for the “Biota/Araçá Project”.
4. Concluding remarks
investments towards the development of digital datasets should be shared to reduce costs and derive increased benefits from information and communication technologies, especially GIS. In countries such as Brazil, where policy for coastal data management is not established, the implementation of projects such as Biota/ Araçá can serve as a pilot to guide the establishment of standards, policies, strategies, and technologies for coastal management and administration. In this context, the implementation of the three-phase program: data organization, modeling, and distribution (shown in Fig. 3) is particularly interesting since it uses tools that can achieve better coordination and integration between modules, thus improving
A number of issues related to the organization of data generated in a multidisciplinary research project on a detailed scale need to be resolved. One of the main issues is the need to harmonize and facilitate communication between data producers (researchers), SDI data managers, and organizers. In this way, the information sharing process can be adequately standardized so that there is no loss, noise, or compromise in the quality of information that can (and should) be available to other users in the future. Turkstra et al. (2003) pointed out that there is an increasing awareness among data producers and consumers that
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Fig. 8. –Layout of the web atlas generated in the Biota/Araçá project.
the effectiveness of data usage, storage, processing, and flow. However, it is important to note that this program required a centralized architecture and organization management, which requires constant and close interactions between domain and informatics experts. Several studies have shown that the processes of organization and distribution of data will tend to be increasingly decentralized and participatory in the future (Dodge et al., 2013; Edgar et al., 2016; Fang et al., 2014; Gomez-Cabrero et al., 2014; Moore et al., 2017). Therefore, tools and instruments that automate much of the organization and distribution of environmental data, especially those of marine and coastal areas where information are generally sparse, valuable, and difficult to obtain, should be developed.
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