This paper explores diverse visualisation methods of regionalisation based on a semantic analysis of institutionalised region names (choronyms) and adopts a fluid (fuzzy) approach to regional boundaries. Grounded in the theory of the institutionalisation of regions, the study examines how region shapes delineate their identities, resulting in an overlapping space of regions. Thus, the nature of this approach in regional geography requires multiple visualisations of areal features. The outcome is map representations of regions in the Czech-German-Polish borderland, illustrating regionalisation beyond traditional regional geography. European regionalism requires regionalisation using innovative cartographic visualisation methods to represent a cultural landscape without the use of borders.Boundaries, names of the regions and institutions form the basis of the region’s identities. European regionalism requires regionalisation using innovative cartographic visualisation methods to represent a cultural landscape without the use of borders. Boundaries, names of the regions and institutions form the basis of the region’s identities.

Accuracy assessment is one of the most important components of both applied and research-oriented remote sensing projects. For mapped classes that have sharp and easily identified boundaries, a broad array of accuracy assessment methods has been developed. However, accuracy assessment is in many cases complicated by classes that have fuzzy, indeterminate, or gradational boundaries, a condition which is common in real landscapes; for example, the boundaries of wetlands, many soil map units, and tree crowns. In such circumstances, the conventional approach of treating all reference pixels as equally important, whether located on the map close to the boundary of a class, or in the class center, can lead to misleading results. We therefore propose an accuracy assessment approach that relies on center-weighting map segment area to calculate a variety of common classification metrics including overall accuracy, class user’s and producer’s accuracy, precision, recall, specificity, and the F1 score. This method offers an augmentation of traditional assessment methods, can be used for both binary and multiclass assessment, allows for the calculation of count- and area-based measures, and permits the user to define the impact of distance from map segment edges based on a distance weighting exponent and a saturation threshold distance, after which the weighting ceases to grow. The method is demonstrated using synthetic and real examples, highlighting its use when the accuracy of maps with inherently uncertain class boundaries is evaluated.

Context

GeoJSON file of NYC Neighborhood boundaries maintained by Ontodia.

Content

From source:

NYC Neighborhoods polygons and correlated data with their respective Postal Codes, Assembly Districts, Community Districts, Congressional Districts, Council Districts and State Senate Districts created by Ontodia. There are hundreds of neighborhoods in New York City's five boroughs, each with unique characteristics and histories. Many historical neighborhood names are derived from the names of the previously independent villages, towns, and cities that were incorporated into into the City of New York in the consolidation of 1898. Other neighborhood names have been introduced by real estate developers and urban planners, sometimes contentiously. Boundaries of neighborhoods are notoriously fuzzy, although many boundaries are widely agreed upon. Complicating the definition of neighborhood further, boundaries may overlap, some neighborhoods may function as a micro-neighborhood within another neighborhood, or a larger district which can be made up of multiple neighborhoods. Names and boundaries of neighborhoods shift over time; they are determined by the collective conscious of the people who live, work, and play in these places. There is never an official version of neighborhoods, but the concept is deeply meaningful to many people. In many cases a New Yorker is just as proud to claim identity with a particular neighborhood, and visitors plan their trips around visits to specific neighborhoods. To display data about neighborhoods on NYCpedia we created our own neighborhood boundaries, 264 in all. In order to display a continuous map with no overlap some boundaries have been stretched or shrunk, and neighborhoods have been omitted in this version. We intend to expand our work developing neighborhood polygon files (all released with open source license) and also to collect and organize as many meaningful alternative versions of neighborhood boundaries as possible. If you are a map geek or software developer who builds apps about New York City you can find the shapefile and geoJSON of the NYCpedia neighborhoods on Data Wrangler. Drop us a line if you see any errors, or if you have suggestions for how to improve our conception of NYC geography.

Acknowledgements

Data set from: http://catalog.opendata.city/dataset/pediacities-nyc-neighborhoods

Boundaries should be considered as ‘fuzzy’ rather than definitive to reflect the transitional nature of wild land. It is an update and replacement to the previously published Core Areas of Wild Land (CAWL) produced in 2013. Note that the areas have been renumbered sequentially and differ from those on the CAWL map. For more information visit https://www.nature.scot/professional-advice/landscape-change/landscape-policy-and-guidance/landscape-policy-wild-landComplete metadata record on spatialdata.gov.scot

This wetlands dataset has its origins in the Wetlands of National Importance (WONI) project, which was part of the Sustainable Development Programme of Actions for Freshwaters which had the goal of identifying a list of water bodies that would protect a full range of freshwater biodiversity.
The pre-human extent of wetlands was produced using soil information from the New Zealand Land Resource Inventory (NZLRI) and a 15m digital elevation model (DEM) to refine soil boundaries. Current wetlands were defined by combining existing databases including LCDB2 (Land Cover Database version 2), NZMS 260 Topomaps, existing surveys from Regional Councils, Queen Elizabeth II (QEII) covenant wetland polygons, DOC surveys (WERI database), and the 15m DEM, to define a single set of wetland polygons and centre points. All this data was checked against a standardised set of Landsat imagery using the Ecosat technology and where necessary new wetland boundaries delineated.

Wetlands were classified into 7 groups at the hydro-class level using fuzzy expert rules.