Four Persistent Research Questions in Cartography

In recent decades, cartography has experienced a number of paradigm changes seen in refreshed research agendas and renewed education programs. Yet cartography remains the science, art and technology of making and using maps. This paper addresses four persistent research questions in cartography: 1 ) What is a map? 2) What are maps made for? 3) How are maps made? and 4) Who is making maps? Based on a retrospective analysis of cartographic advances since the introduction of the Internet in the early 1990s, the author gives an overview of evolution with regard to map types, map affordances, mapmaking workflows and the roles of mapmakers and map users. While some cartographic principles used since ancient times will continue to serve as anchor points for future development, ever-changing technological potentials and user requirements force us to maintain vitality with more and more innovative maps and map-based services. The author also appeals for a sustainable map creation ecosystem supported by cloud computing platforms

Four persistent research questions in cartography

U posljednjih nekoliko desetljeća kartografija je doživjela niz promjena paradigme koje su naznačene u osvježenom istraživanju i obnovljenim obrazovnim programima. Međutim, unatoč tim promjenama, kartografija ostaje znanost, vještina i tehnika izrađivanja i upotrebe karata. Ovaj se rad bavi četirima u kartografiji vječnim istraživačkim pitanjima: Što je karta? Čemu karte služe? Kako se karte izrađuju? Tko izrađuje karte? Na temelju retrospektivne analize napretka kartografije od uvođenja interneta početkom 1990. godine, autorica daje pregled razvoja u pogledu vrsta karata, svojstva karata, tijeka izrade karata i uloge izrađivača i korisnika karata. Iako neka kartografska načela još od davnih vremena i dalje služe kao sidrišta za budući razvoj, sve mijene tehnoloških mogućnosti i zahtjevi korisnika prisiljavaju nas da sačuvamo vitalnost predmetnog polja s inovativnijim kartama i uslugama temeljenim na karti. Štoviše, autorica poziva na održivi ekosustav stvaranja karata podržanih paradigmom računarstva u oblaku.In recent decades, cartography has experienced a number of paradigm changes seen in refreshed research agendas and renewed education programs. Yet cartography remains the science, art and technology of making and using maps. This paper addresses four persistent research questions in cartography: 1) What is a map? 2) What are maps made for? 3) How are maps made? and 4) Who is making maps? Based on a retrospective analysis of cartographic advances since the introduction of the Internet in the early 1990s, the author gives an overview of evolution with regard to map types, map affordances, mapmaking workflows and the roles of mapmakers and map users. While some cartographic principles used since ancient times will continue to serve as anchor points for future development, ever-changing technological potentials and user requirements force us to maintain vitality with more and more innovative maps and map-based services. The author also appeals for a sustainable map creation ecosystem supported by cloud computing platforms

Effect of Geospatial Uncertainty Borderization on Users' Heuristic Reasoning

Abstract. A set of mental strategies called "heuristics" – logical shortcuts that we use to make decisions under uncertainty – has become the subject of a growing number of studies. However, the process of heuristic reasoning about uncertain geospatial data remains relatively under-researched. With this study, we explored the relation between heuristics-driven decision-making and the visualization of geospatial data in states of uncertainty, with a specific focus on the visualization of borders, here termed "borderization". Therefore, we tested a set of cartographic techniques to visualize the boundaries of two types of natural hazards across a series of maps through a user survey. Respondents were asked to assess the safety and desirability of several housing locations potentially affected by air pollution or avalanches. Maps in the survey varied by "borderization" method, background color and type of information about uncertain data (e.g., extrinsic vs. intrinsic). Survey results, analyzed using a mixed quantitative-qualitative approach, confirmed previous suggestions that heuristics play a significant role in affecting users' map experience, and subsequent decision-making

Visual Analytics for Regional Economic Environment Factors Based on a Dashboard Design

Abstract. Economic environment is vital for commercial investment, city planning and company strategy planning in urban areas. Mastering the economical trend may help the entrepreneurs, government officers and individuals in their decision-making process. In this study, we explore multiple geo-economic datasets using visual analytics methods for understanding the economic environment. More specifically, we user time-series Gross Domestic Product (GDP) data as an economic indicator of economic development and land use data to support the spatial analysis at a refined geographic scale. The spatiotemporal patterns of the regional economic environment are revealed both qualitatively and quantitatively. The work has a three-fold contributions: (1) we apply a grid-based spatial interpolation model to derive GDP values at a file granularity based on land use data; (2) we design a novel interactive dashboard for the GDP data exploration, which serves as a visual analytical tool between data and users; (3) we combine quantitative analysis with visualizations to strengthen the qualitative analysis. The feasibility of visual analytics methods and the dashboard design are tested in one of the most developed regions, Jiangsu Province, China. Both expected and unexpected economical patterns were extracted.</p

Box2Poly: Memory-Efficient Polygon Prediction of Arbitrarily Shaped and Rotated Text

Recently, Transformer-based text detection techniques have sought to predict polygons by encoding the coordinates of individual boundary vertices using distinct query features. However, this approach incurs a significant memory overhead and struggles to effectively capture the intricate relationships between vertices belonging to the same instance. Consequently, irregular text layouts often lead to the prediction of outlined vertices, diminishing the quality of results. To address these challenges, we present an innovative approach rooted in Sparse R-CNN: a cascade decoding pipeline for polygon prediction. Our method ensures precision by iteratively refining polygon predictions, considering both the scale and location of preceding results. Leveraging this stabilized regression pipeline, even employing just a single feature vector to guide polygon instance regression yields promising detection results. Simultaneously, the leverage of instance-level feature proposal substantially enhances memory efficiency (>50% less vs. the state-of-the-art method DPText-DETR) and reduces inference speed (>40% less vs. DPText-DETR) with minor performance drop on benchmarks

Artificial Intelligence based Building Attributes Enrichment in OpenStreetMap using Street-view Images

This work aims to improve OSM building attributes using street-view images. As OSM data are open and street-level photos can be taken with standard cell phones, our approach is neither geospatially restricted nor economically discriminated. In addition, crowdsourced platforms, such as Flickr, Unsplash, and Mapillary, provide huge amounts of street-view images that contain valuable building attribute information. We seek to facilitate open data and citizen science and encourage people to map for their communities

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