The triad of host, agent, and environment has become a widely accepted framework for understanding infectious diseases and human health. While modern medicine has traditionally focused on the individual, there is a renewed interest in the role of the environment. Recent studies have shifted from an early-twentieth-century emphasis on individual factors to a broader consideration of contextual factors, including environmental, climatic, and social settings as spatial determinants of health. This shifted focus has been particularly relevant in the context of the COVID-19 pandemic, where the built environment in urban settings is increasingly recognized as a crucial factor influencing disease transmission. However, operationalizing the complexity of associations between the built environment and health for empirical analyses presents significant challenges. This study aims to identify key caveats in the operationalization of spatial determinants of health for empirical analysis and proposes guiding principles for future research. We focus on how the built environment in urban settings was studied in recent literature on COVID-19. Based on a set of criteria, we analyze 23 studies and identify explicit and implicit assumptions regarding the health-related dimensions of the built environment. Our findings highlight the complexities and potential pitfalls, referred to as the ‘spatial trap,' in the current approaches to spatial epidemiology concerning COVID-19. We conclude with recommendations and guiding questions for future studies to avoid falsely attributing a built environment impact on health outcomes and to clarify explicit and implicit assumptions regarding the health-related dimensions.

The primary intent of this workshop is to provide practical training in using Statistics Canada geography files with the leading industry standard software: Environmental Systems Research Institute, Inc.(ESRI) ArcGIS 9x. Participants will be introduced to the key features of ArcGIS 9x, as well as to geographic concepts and principles essential to understanding and working with geographic information systems (GIS) software. The workshop will review a range of geography and attribute files available from Statistics Canada, as well as some best practices for accessing this information. A brief overview of complementary data sets available from federal and provincial agencies will be provided. There will also be an opportunity to complete a practical exercise using ArcGIS9x. (Note: Data associated with this presentation is available on the DLI FTP site under folder 1873-221.)

This course teaches how to best symbolize your map data so that your audience gets the information that it needs.Goals Apply principles of map symbology to map features. Understand basic principles of map symbology.

Learn state-of-the-art skills to build compelling, useful, and fun Web GIS apps easily, with no programming experience required.Building on the foundation of the previous three editions, Getting to Know Web GIS, fourth edition,features the latest advances in Esri’s entire Web GIS platform, from the cloud server side to the client side.Discover and apply what’s new in ArcGIS Online, ArcGIS Enterprise, Map Viewer, Esri StoryMaps, Web AppBuilder, ArcGIS Survey123, and more.Learn about recent Web GIS products such as ArcGIS Experience Builder, ArcGIS Indoors, and ArcGIS QuickCapture. Understand updates in mobile GIS such as ArcGIS Collector and AuGeo, and then build your own web apps.Further your knowledge and skills with detailed sections and chapters on ArcGIS Dashboards, ArcGIS Analytics for the Internet of Things, online spatial analysis, image services, 3D web scenes, ArcGIS API for JavaScript, and best practices in Web GIS.Each chapter is written for immediate productivity with a good balance of principles and hands-on exercises and includes:A conceptual discussion section to give you the big picture and principles,A detailed tutorial section with step-by-step instructions,A Q/A section to answer common questions,An assignment section to reinforce your comprehension, andA list of resources with more information.Ideal for classroom lab work and on-the-job training for GIS students, instructors, GIS analysts, managers, web developers, and other professionals, Getting to Know Web GIS, fourth edition, uses a holistic approach to systematically teach the breadth of the Esri Geospatial Cloud.AUDIENCEProfessional and scholarly. College/higher education. General/trade.AUTHOR BIOPinde Fu leads the ArcGIS Platform Engineering team at Esri Professional Services and teaches at universities including Harvard University Extension School. His specialties include web and mobile GIS technologies and applications in various industries. Several of his projects have won specialachievement awards. Fu is the lead author of Web GIS: Principles and Applications (Esri Press, 2010).Pub Date: Print: 7/21/2020 Digital: 6/16/2020 Format: Trade paperISBN: Print: 9781589485921 Digital: 9781589485938 Trim: 7.5 x 9 in.Price: Print: $94.99 USD Digital: $94.99 USD Pages: 490TABLE OF CONTENTSPrefaceForeword1 Get started with Web GIS2 Hosted feature layers and storytelling with GIS3 Web AppBuilder for ArcGIS and ArcGIS Experience Builder4 Mobile GIS5 Tile layers and on-premises Web GIS6 Spatial temporal data and real-time GIS7 3D web scenes8 Spatial analysis and geoprocessing9 Image service and online raster analysis10 Web GIS programming with ArcGIS API for JavaScriptPinde Fu | Interview with Esri Press | 2020-07-10 | 15:56 | Link.

In this course, you will explore the concepts, principles, and practices of acquiring, storing, analyzing, displaying, and using geospatial data. Additionally, you will investigate the science behind geographic information systems and the techniques and methods GIS scientists and professionals use to answer questions with a spatial component. In the lab section, you will become proficient with the ArcGIS Pro software package.

This course will prepare you to take more advanced geospatial science courses.

You will be asked to work through a series of modules that present information relating to a specific topic. You will also complete a series of lab exercises, assignments, and less guided challenges. Please see the sequencing document for our suggestions as to the order in which to work through the material. To aid in working through the lecture modules, we have provided PDF versions of the lectures with the slide notes included. This course makes use of the ArcGIS Pro software package from the Environmental Systems Research Institute (ESRI), and directions for installing the software have also been provided. If you are not a West Virginia University student, you can still complete the labs, but you will need to obtain access to the software on your own.

Cartography is the knowledge associated with the art, science, and technology of maps. Maps portray spatial relationships among selected phenomena of interest and increasingly are used for analysis and synthesis. Through digital cartography and web mapping, however, it is possible for almost anyone to produce a bad map in minutes. Although cartography has undergone a radical transformation through the introduction of digital technology, fundamental principles remain. Doing computer cartography well requires a broad understanding of graphicacy as a language (as well as numeracy and literacy). This course provides an introduction to the principles, concepts, software, and hardware necessary to produce good maps, especially in the context (and limitations) of geographic information systems (GIS) and the web.

You will be asked to work through a series of modules that present information relating to a specific topic. You will also complete a series of cartography projects to reinforce the material. Lastly, you will complete term projects. Please see the sequencing document for our suggestions as to the order in which to work through the material. We have also provided PDF versions of the lectures with the notes included.

This repository contains two Microsoft Excel documents:A quiz with eight questions, assigned to students in a graduate-level GIS programming course as part of Homework Assignment 2. The quiz assesses students' understanding of basic Python programming principles (such as loops and conditional statements).An Excel document with three worksheets, each corresponding to one homework assignment from the same graduate GIS programming course. The document includes self-reported background information (e.g., students' prior programming experience), details about the use of various resources (e.g., websites) for completing assignments, the perceived helpfulness of these resources, and scores for the homework assignments and quizzes.

GIS-datasets for the Street networks of Stockholm, Gothenburg and Eskilstuna produced as part of the Spatial Morphology Lab (SMoL).

The goal of the SMoL project is to develop a strong theory and methodology for urban planning & design research with an analytical approach. Three frequently recurring variables of spatial urban form are studied that together quite well capture and describe the central characteristics and qualities of the built environment: density, diversity and proximity.

The first measure describes how intensive a place can be used depending on how much built up area is found there. The second measure captures how differentiated the use of a place can be depending on the division in smaller units such as plots. The third measure describes how accessible a place is depending on how it relates with other places. Empirical studies have shown strong links between these metrics and people's use of cities such as pedestrian movement patterns.

To support this goal, a central objective of the project is the establishment of an international platform of GIS data models for comparative studies in spatial urban form comprising three European capitals: London in the UK, Amsterdam in the Netherlands and Stockholm in Sweden, as well as two additional Swedish cities of smaller size than Stockholm: Gothenburg and Eskilstuna.

The result of the project is a GIS database for the five cities covering the three basic layers of urban form: street network (motorised and non-motorised), buildings and plots systems.

The data is shared via SND to create a research infrastructure that is open to new study initiatives. The datasets for Amsterdam will also be uploaded to SND. The datasets of London cannot be uploaded because of licensing restrictions.

The street network GIS-maps include motorised and non-motorised networks. The motorised networks exclude all streets that are pedestrian-only and were cars are excluded. The network layers are based on the Swedish national road database, NVDB (Nationell Vägdatabas), downloaded from Trafikverket (https://lastkajen.trafikverket.se, date of download 15-5-2016, last update 8-11-2015). The original road-centre-line maps of all cities were edited based on the same basic representational principles and were converted into line-segment maps, using the following software: FME, Mapinfo professional and PST (Place Syntax Tool). The coordinate system is SWEREF99TM. In the final line-segment maps (GIS-layers) all roads are represented with one line irrespectively of the number of lanes, except from Motorways and Highways which are represented with two lines, one for each direction, again irrespectively of the number of lanes. We followed the same editing and generalizing procedure for all maps aiming to remove errors and to increase comparability between networks. This process included removing duplicate and isolated lines, snapping and generalizing. The snapping threshold used was 2m (end points closer than 2m were snapped together). The generalizing threshold used was 1m (successive line segments with angular deviation less than 1m were merged into one). In the final editing step, all road polylines were segmented to their constituting line-segments. The aim was to create appropriate line-segment maps to be analysed using Angular Segment Analysis, a network centrality analysis method introduced in Space Syntax.

All network layers are complemented with an “Unlink points” layer; a GIS point layer with the locations of all non-level intersections, such as overpasses and underpasses, bridges, tunnels, flyovers and the like. The Unlink point layer is necessary to conduct network analysis that takes into account the non-planarity of the street network, using such software as PST (Place Syntax Tool).

Japanese Knotweed s.l. taxa are amongst the most aggressive vascular plant Invasive Alien Species (IAS) in the world. These taxa form dense, suppressive monocultures and are persistent, pervasive invaders throughout the more economically developed countries (MEDCs) of the world. The current paper utilises the Object-Based Image Analysis (OBIA) approach of Definiens Imaging Developer software, in combination with very high spatial resolution (VHSR) colour infra-red (CIR) and visible-band (RGB) aerial photography in order to detect Japanese Knotweed s.l. taxa in Wales (UK). An algorithm was created using Definiens in order to detect these taxa, using variables found to effectively distinguish them from landscape and vegetation features. The results of the detection algorithm were accurate, as confirmed by field validation and desk-based studies. Further, these results may be incorporated into Geographical Information Systems (GIS) research as they are readily transferable as vector polygons (shapefiles). The successful detection results developed within the Definiens software should enable greater management and control efficacy. Further to this, the basic principles of the detection process could enable detection of these taxa worldwide, given the (relatively) limited technical requirements necessary to conduct further analyses.

GIS-datasets for the Street networks of Stockholm, Gothenburg and Eskilstuna produced as part of the Spatial Morphology Lab (SMoL).

The goal of the SMoL project is to develop a strong theory and methodology for urban planning & design research with an analytical approach. Three frequently recurring variables of spatial urban form are studied that together quite well capture and describe the central characteristics and qualities of the built environment: density, diversity and proximity.

The first measure describes how intensive a place can be used depending on how much built up area is found there. The second measure captures how differentiated the use of a place can be depending on the division in smaller units such as plots. The third measure describes how accessible a place is depending on how it relates with other places. Empirical studies have shown strong links between these metrics and people's use of cities such as pedestrian movement patterns.

To support this goal, a central objective of the project is the establishment of an international platform of GIS data models for comparative studies in spatial urban form comprising three European capitals: London in the UK, Amsterdam in the Netherlands and Stockholm in Sweden, as well as two additional Swedish cities of smaller size than Stockholm: Gothenburg and Eskilstuna.

The result of the project is a GIS database for the five cities covering the three basic layers of urban form: street network (motorised and non-motorised), buildings and plots systems.

The data is shared via SND to create a research infrastructure that is open to new study initiatives. The datasets for Amsterdam will also be uploaded to SND. The datasets of London cannot be uploaded because of licensing restrictions.

The street network GIS-maps include motorised and non-motorised networks. The non-motorized networks include all streets and paths that are accessible for people walking or cycling, including those that are shared with vehicles. All streets where walking or cycling is forbidden, such as motorways, highways, or high-speed tunnels, are not included in the network.

The non-motorised network layers for Stockholm and Eskilstuna are based on the Swedish national road database, NVDB (Nationell Vägdatabas), downloaded from Trafikverket (https://lastkajen.trafikverket.se, date of download 15-5-2016, last update 8-11-2015) . For Gothenburg, it is based on Open Street Maps (openstreetmap.org, http://download.geofabrik.de, date of download 29-4-2016), because the NVDB did not provide enough detail for the non-motorized network, as in the other cities. The original road-centre-line maps of all cities were edited based on the same basic representational principles and were converted into line-segment maps, using the following software: FME, Mapinfo professional and PST (Place Syntax Tool). The coordinate system is SWEREF99TM.

In the final line-segment maps (GIS-layers) all streets or paths are represented with one line irrespectively of the number of lanes or type, meaning that parallel lines representing a street and a pedestrian or a cycle path running on the side, are reduced to one line. The reason is that these parallel lines are nor physically or perceptually separated, and thus are accessible and recognized from pedestrians as one “line of movement” in the street network. If there are obstacles or great distance between parallel streets and paths, then the multiple lines remain. The aim is to make a skeletal network that better represents the total space, which is accessible for pedestrians to move, irrespectively of the typical separations or distinctions of streets and paths. This representational choice follows the Space Syntax methodology in representing the public space and the street network.

We followed the same editing and generalizing procedure for all maps aiming to remove errors and to increase comparability between networks. This process included removing duplicate and isolated lines, snapping and generalizing. The snapping threshold used was 2m (end points closer than 2m were snapped together). The generalizing threshold used was 1m (successive line segments with angular deviation less than 1m were merged into one). In the final editing step, all road polylines were segmented to their constituting line-segments. The aim was to create appropriate line-segment maps to be analysed using Angular Segment Analysis, a network centrality analysis method introduced in Space Syntax.

All network layers are complemented with an “Unlink points” layer; a GIS point layer with the locations of all non-level intersections, such as pedestrian bridges and tunnels. The Unlink point layer is necessary to conduct network analysis that takes into account the non-planarity of the street network, using such software as PST (Place Syntax Tool).

For more detailed documentation on the creation of the non-motorised network of Gothenburg, please download the specific doc

The Grid Garage Toolbox is designed to help you undertake the Geographic Information System (GIS) tasks required to process GIS data (geodata) into a standard, spatially aligned format. This format …Show full descriptionThe Grid Garage Toolbox is designed to help you undertake the Geographic Information System (GIS) tasks required to process GIS data (geodata) into a standard, spatially aligned format. This format is required by most, grid or raster, spatial modelling tools such as the Multi-criteria Analysis Shell for Spatial Decision Support (MCAS-S). Grid Garage contains 36 tools designed to save you time by batch processing repetitive GIS tasks as well diagnosing problems with data and capturing a record of processing step and any errors encountered. Grid Garage provides tools that function using a list based approach to batch processing where both inputs and outputs are specified in tables to enable selective batch processing and detailed result reporting. In many cases the tools simply extend the functionality of standard ArcGIS tools, providing some or all of the inputs required by these tools via the input table to enable batch processing on a 'per item' basis. This approach differs slightly from normal batch processing in ArcGIS, instead of manually selecting single items or a folder on which to apply a tool or model you provide a table listing target datasets. In summary the Grid Garage allows you to: List, describe and manage very large volumes of geodata. Batch process repetitive GIS tasks such as managing (renaming, describing etc.) or processing (clipping, resampling, reprojecting etc.) many geodata inputs such as time-series geodata derived from satellite imagery or climate models. Record any errors when batch processing and diagnose errors by interrogating the input geodata that failed. Develop your own models in ArcGIS ModelBuilder that allow you to automate any GIS workflow utilising one or more of the Grid Garage tools that can process an unlimited number of inputs. Automate the process of generating MCAS-S TIP metadata files for any number of input raster datasets. The Grid Garage is intended for use by anyone with an understanding of GIS principles and an intermediate to advanced level of GIS skills. Using the Grid Garage tools in ArcGIS ModelBuilder requires skills in the use of the ArcGIS ModelBuilder tool. Download Instructions: Create a new folder on your computer or network and then download and unzip the zip file from the GitHub Release page for each of the following items in the 'Data and Resources' section below. There is a folder in each zip file that contains all the files. See the Grid Garage User Guide for instructions on how to install and use the Grid Garage Toolbox with the sample data provided.

The Grid Garage Toolbox is designed to help you undertake the Geographic Information System (GIS) tasks required to process GIS data (geodata) into a standard, spatially aligned format. This format is required by most, grid or raster, spatial modelling tools such as the Multi-criteria Analysis Shell for Spatial Decision Support (MCAS-S). Grid Garage contains 36 tools designed to save you time by batch processing repetitive GIS tasks as well diagnosing problems with data and capturing a record of processing step and any errors encountered.

Grid Garage provides tools that function using a list based approach to batch processing where both inputs and outputs are specified in tables to enable selective batch processing and detailed result reporting. In many cases the tools simply extend the functionality of standard ArcGIS tools, providing some or all of the inputs required by these tools via the input table to enable batch processing on a 'per item' basis. This approach differs slightly from normal batch processing in ArcGIS, instead of manually selecting single items or a folder on which to apply a tool or model you provide a table listing target datasets. In summary the Grid Garage allows you to:

• List, describe and manage very large volumes of geodata.
• Batch process repetitive GIS tasks such as managing (renaming, describing etc.) or processing (clipping, resampling, reprojecting etc.) many geodata inputs such as time-series geodata derived from satellite imagery or climate models.
• Record any errors when batch processing and diagnose errors by interrogating the input geodata that failed.
• Develop your own models in ArcGIS ModelBuilder that allow you to automate any GIS workflow utilising one or more of the Grid Garage tools that can process an unlimited number of inputs.
• Automate the process of generating MCAS-S TIP metadata files for any number of input raster datasets.

The Grid Garage is intended for use by anyone with an understanding of GIS principles and an intermediate to advanced level of GIS skills. Using the Grid Garage tools in ArcGIS ModelBuilder requires skills in the use of the ArcGIS ModelBuilder tool.

Download Instructions: Create a new folder on your computer or network and then download and unzip the zip file from the GitHub Release page for each of the following items in the 'Data and Resources' section below. There is a folder in each zip file that contains all the files. See the Grid Garage User Guide for instructions on how to install and use the Grid Garage Toolbox with the sample data provided.

A body of groundwater is a separate volume of groundwater within one or more aquifers, constituting the basic delineation of aquatic environments intended to be the unit of assessment for the WFD. Groundwater bodies are derived from work done on the BDRHF v1 repository. The water bodies are divided according to the following main principles: - Water bodies are delimited on the basis of geological and hydrogeological criteria, - Redistribution of water bodies to take account of the effects of anthropogenic pressures must remain limited, - The boundaries of water bodies must be stable and sustainable, - Like surface water bodies, the delimitation of groundwater bodies is organized on the basis of a typology. This typology is largely inspired by that developed for hydrogeological entities defined in the context of the revision of the HFR DB. It is based on the geological nature and hydrodynamic behavior or "large" operation of aquifer systems (nature, flow velocity). It includes 2 levels of characteristics (main and secondary). - Water bodies can have exchanges between them, - All water withdrawals for drinking water supplying more than 10m3/day or used to supply water to more than 50 people must be included in a body of water, - Deep groundwater, unrelated to watercourses and surface ecosystems, from which no abstraction takes place and which is not likely to be used for drinking water because of its quality (salinity, temperature, etc.), or for technical and economic reasons (disproportionate cost of abstraction) may not constitute water bodies. - Given its size, a body of water may exhibit a certain spatial heterogeneity both in terms of its hydrogeological characteristics and its qualitative and quantitative status. - At any point several bodies of water may overlap. (source: according to BRGM, IMPLEMENTATION OF THE WFD: IDENTIFICATION AND DELIMITATION OF SOUTHWATER MASSES - methodological guide, January 2003) WFD reporting correspondence: This entity corresponds to WISE's Ground Water Body (GWBODY) concept.

Thunderstorm development needs three main ingredients. Like baking a cake, you need all of the ingredients in the correct order; otherwise, a thunderstorm may not form.MoistureInstabilityLiftBonus: Vertical Wind Shear

"The year 2035 regional land use plan incorporates the basic principles and concepts of the previously adopted plans. The plan promotes a compact, centralized regional settlement pattern, with urban development recommended to occur within, and along the periphery of, existing urban centers; promotes the location of new urban development in areas which are physically suitable for such development and which may be readily served by basic urban services, including sanitary sewer, water supply, and public transit services; and seeks to preserve the environmentally sensitive lands and the most productive farmlands in the Region. Like the previous plans, the new plan is advisory in nature. Plan implementation will depend largely upon the willingness of county and local governments to use land use controls to shape development patterns in the regional interest. The year 2035 regional land use plan will provide a sound regional development framework needed in support of transportation and other public facility planning at the regional level, and in support of the preparation of comprehensive plans and related plan implementation efforts by county and local units of government in the Region. " http://www.sewrpc.org/SEWRPCFiles/Publications/pr/pr-048_regional_land_use_plan_for_se_wi_2035.pdf

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles.

For four of the map units – 3-SDF, 4-SDAF, 27-POHV, and 31-LBY – modeling using GIS principles was also employed. Modeling involves using environmental conditions of a map unit, such as elevation, slope, and aspect, which were determined by the field-collected ecological data. Data satisfying these conditions were obtained from ancillary data sources, such as USGS DEM data. These data were fed into a model that will result in locations (pixels) where all the desired conditions exist. For example, if a certain map unit was a shrubland that predominantly occurs above 8000 feet, on slopes of 3-10%, and on west-facing aspects, the correctly-constructed model will output only locations where this combination of conditions will be found. The resulting areas were then examined manually with the traditional photo interpretation process to confirm that they indeed could be accepted as that map unit. If photo interpretation determines that the areas were not acceptable, then they were changed to a more appropriate map unit.

This course explores the theory, technology, and applications of remote sensing. It is designed for individuals with an interest in GIS and geospatial science who have no prior experience working with remotely sensed data. Lab exercises make use of the web and the ArcGIS Pro software. You will work with and explore a wide variety of data types including aerial imagery, satellite imagery, multispectral imagery, digital terrain data, light detection and ranging (LiDAR), thermal data, and synthetic aperture RaDAR (SAR). Remote sensing is a rapidly changing field influenced by big data, machine learning, deep learning, and cloud computing. In this course you will gain an overview of the subject of remote sensing, with a special emphasis on principles, limitations, and possibilities. In addition, this course emphasizes information literacy, and will develop your skills in finding, evaluating, and using scholarly information.

You will be asked to work through a series of modules that present information relating to a specific topic. You will also complete a series of lab exercises to reinforce the material. Lastly, you will complete paper reviews and a term project. We have also provided additional bonus material and links associated with surface hydrologic analysis with TauDEM, geographic object-based image analysis (GEOBIA), Google Earth Engine (GEE), and the geemap Python library for Google Earth Engine. Please see the sequencing document for our suggested order in which to work through the material. We have also provided PDF versions of the lectures with the notes included.

As part of the GIS support activities at the Wildbach- und Avalinenverbauung Sektion Tirol (Austrian Federal Ministry of Agriculture, Forestry, Environment and Water Management), a new approach with geographical location designations (e.g. district names) was developed, which makes it possible to automatically recognize and assign the projection of geodata. The effort for GIS users without knowledge of coordinate systems was enormous in the event of ambiguities before this solution. In the meantime, no additional support effort is required. The basic principle of the solution is that a geographical name is independent of coordinates or projection. If geometries of a place are combined with the geographical name in different projections, a clear assignment of the unclear spatial data set is almost always possible.

Robust risk assessment requires accurate flood intensity area mapping to allow for the identification of populations and elements at risk. However, available flood maps in West Africa lack spatial variability while global datasets have resolutions too coarse to be relevant for local scale risk assessment. Consequently, local disaster managers are forced to use traditional methods such as watermarks on buildings and media reports to identify flood hazard areas. In this study, remote sensing and Geographic Information System (GIS) techniques were combined with hydrological and statistical models to delineate the spatial limits of flood hazard zones in selected communities in Ghana, Burkina Faso and Benin. The approach involves estimating peak runoff concentrations at different elevations and then applying statistical methods to develop a Flood Hazard Index (FHI). Results show that about half of the study areas fall into high intensity flood zones. Empirical validation using statistical confusion matrix and the principles of Participatory GIS show that flood hazard areas could be mapped at an accuracy ranging from 77% to 81%. This was supported with local expert knowledge which accurately classified 79% of communities deemed to be highly susceptible to flood hazard. The results will assist disaster managers to reduce the risk to flood disasters at the community level where risk outcomes are first materialized.

The term "Smartline" refers to a GIS line map format which can allow rapid capture of diverse coastal data into a single consistently classified map, which in turn can be readily analysed for many purposes. This format has been used to create a detailed nationally-consistent coastal geomorphic map of Australia, which is currently being used for the National Coastal Vulnerability Assessment (NCVA) as part of the underpinning information for understanding the vulnerability to sea level rise and other climate change influenced hazards such as storm surge. The utility of the Smartline format results from application of a number of key principles. A hierarchical form- and fabric-based (rather than morpho-dynamic) geomorphic classification is used to classify coastal landforms in shore-parallel tidal zones relating to but not necessarily co-incident with the GIS line itself. Together with the use of broad but geomorphically-meaningful classes, this allows Smartline to readily import coastal data from a diversity of differently-classified prior sources into one consistent map. The resulting map can be as spatially detailed as the available data sources allow, and can be used in at least two key ways: Firstly, Smartline can work as a source of consistently classified information which has been distilled out of a diversity of data sources and presented in a simple format from which required information can be rapidly extracted using queries. Given the practical difficulty many coastal planners and managers face in accessing and using the vast amount of primary coastal data now available in Australia, Smartline can provide the means to assimilate and synthesise all this data into more usable forms.