Modular Data-Transformation Modelling with Geospatial Semantic Array Programming

de Rigo, D., <strong>Modular Data-Transformation Modelling with Geospatial Semantic Array Programming</strong>. FigShare Digital Science. DOI: 10.6084/m9.figshare.842695 <strong>Modular Data-Transformation Modelling with Geospatial Semantic Array Programming</strong> Daniele de Rigo <strong>Summary.</strong> Wide-scale transdisciplinary modelling for environment (WSTMe) is a scientific challenge with an increasingly important role in allowing strategic policy-making to be effectively discussed and programmed with the support of robust science [1]. Natural resources such as forests, water and soil, along with climate and human-driven changes, are subject to a network of interactions, whose large scale effects may be significant. WSTMe raises challenging issues when the characteristic heterogeneity of available geospatial information, complexity of systems and multiple sources of uncertainty (including those related to scientific software [2]) may affect the robustness, transparency and comprehensibility of hypotheses and results. In this respect, earth observation and computational science [3,4] are intrinsically linked and expected to deal with such a modular array of transdisciplinary aspects while preserving as much as possible conciseness and a terse semantics [5]. This is desirable in order to better communicate key messages and issues, both among different scientific communities and at the science-policy interface. Geospatial Semantic Array Programming (GeoSemAP) is a new approach [6] for WSTMe that has recently emerged in which a concise integration is introduced among semantics, geospatial tools and the array of data-transformation models (D-TM). WSTMe may often be described as a composition of D‑TMs where the flow of initial and derived/intermediate geo‑data highlights its array-based modular structure and semantics. Transparency (even due to the open science approach) is also a goal, to aid society in clearly understanding and controlling the implications of the technical apparatus on collective environmental decision-making [1–6]. <strong>Caption of the image</strong>. Wide-scale transdisciplinary modelling for environment (WSTMe) may often be described as a composition of data-transformation models (D‑TM) where the flow of initial and derived/intermediate geo‑data highlights its array-based modular structure and semantics (Geospatial Semantic Array Programming, GeoSemAP). Sources: [2,6]. <strong>References</strong> [1] van der Sluijs, J. P., 2005. Uncertainty as a Monster in the Science-Policy Interface: Four Coping Strategies. <em>Water Science &amp; Technology 52</em> (6), 87-92. http://scholar.google.com/scholar?cluster=3385318353116653032 [2] de Rigo, D., 2013. Software Uncertainty in Integrated Environmental Modelling: the role of Semantics and Open Science. <em>Geophysical Research Abstracts 15</em>, 13292+. http://scholar.google.com/scholar?cluster=13790404181931852043 [3] Peng, R. D., 2011. Reproducible Research in Computational Science. <em>Science 334</em> (6060), 1226-1227. http://scholar.google.com/scholar?cluster=905554772905069177 [4] Morin, A., Urban, J., Adams, P. D., Foster, I., Sali, A., Baker, D., Sliz, P., 2012. Shining Light into Black Boxes. <em>Science 336</em> (6078), 159-160. http://scholar.google.com/scholar?cluster=12575758499484368256 [5] de Rigo, D., 2012. Semantic Array Programming for Environmental Modelling: Application of the Mastrave Library. In: Seppelt, R., Voinov, A. A., Lange, S., Bankamp, D. (Eds.), <em>International Environmental Modelling and Software Society (iEMSs) 2012 International Congress on Environmental Modelling and Software. Managing Resources of a Limited Planet: Pathways and Visions under Uncertainty, Sixth Biennial Meeting</em>. pp. 1167-1176. http://scholar.google.com/scholar?cluster=6628751141895151391 [6] de Rigo, D., Corti, P., Caudullo, G., McInerney, D., Di Leo, M., San-Miguel-Ayanz, J., 2013. Toward Open Science at the European Scale: Geospatial Semantic Array Programming for Integrated Environmental Modelling. <em>Geophysical Research Abstracts 15</em>, 13245+. http://scholar.google.com/scholar?cluster=17118262245556811911

德·里戈（D. de Rigo），**《基于地理空间语义数组编程的模块化数据转换建模》**（Modular Data-Transformation Modelling with Geospatial Semantic Array Programming）。FigShare数字科学平台。DOI: 10.6084/m9.figshare.842695

**《基于地理空间语义数组编程的模块化数据转换建模》** 达尼埃莱·德·里戈（Daniele de Rigo）

**摘要**。面向环境的大规模跨学科建模（Wide-scale transdisciplinary modelling for environment, WSTMe）是一项科学挑战，其在依托扎实科学支撑有效开展战略政策讨论与规划的过程中，作用日益凸显[1]。森林、水、土壤等自然资源，与气候及人类活动驱动的变化之间存在复杂的交互网络，其大规模影响不容忽视。当可用地理空间信息固有的异质性、系统复杂性以及多重不确定性来源（包括与科学软件相关的不确定性[2]）可能影响假设与结果的稳健性、透明度和可理解性时，WSTMe便会面临诸多棘手问题。就此而言，地球观测与计算科学[3,4]存在内在关联，二者需应对此类模块化的跨学科研究维度，同时尽可能保持表述简洁与语义严谨[5]。这一要求有助于在不同科学共同体之间，以及在科学与政策的交互界面上，更好地传递核心信息与关键问题。地理空间语义数组编程（Geospatial Semantic Array Programming, GeoSemAP）是近年来为WSTMe提出的全新方法[6]，该方法实现了语义、地理空间工具与数据转换模型（data-transformation models, D-TM）集合的简洁整合。WSTMe通常可被描述为数据转换模型的组合，初始数据与衍生/中间地理数据的流动凸显了其基于数组的模块化结构与语义特征。透明度（依托开放科学理念）同样是核心目标，旨在助力社会清晰理解并把控技术体系对集体环境决策的影响[1–6]。

**图片说明**。面向环境的大规模跨学科建模（WSTMe）通常可被描述为数据转换模型（D-TM）的组合，初始数据与衍生/中间地理数据的流动凸显了其基于数组的模块化结构与语义特征（地理空间语义数组编程，GeoSemAP）。来源：[2,6]。

**参考文献**
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