NeoGeography has been defined as a blurring of the distinctions between producer, communicator and consumer of geographic information. The relationship between professional and amateur varies across disciplines. The subject matter of geography is familiar to everyone, and the acquisition and compilation of geographic data have become vastly easier as technology has advanced. The authority of traditional mapping agencies can be attributed to their specifications, production mechanisms and programs for quality control. Very different mechanisms work to ensure the quality of data volunteered by amateurs. Academic geographers are concerned with the extraction of knowledge from geographic data using a combination of analytic tools and accumulated theory. The definition of NeoGeography implies a misunderstanding of this role of the professional, but English lacks a basis for a better term.

NeoGeography and the nature of geographicexpertise

Interactive Democracy is an umbrella term that encompasses a variety of approaches to make collective decision making processes more engaging and responsive. A common goal of these approaches is to utilize modern information technology---in particular, the Internet---in order to enable more interactive decision making processes. An integral part of many interactive democracy proposals are online decision platforms that provide much more flexibility and interaction possibilities than traditional democratic systems. This is achieved by embracing the novel paradigm of delegative voting, often referred to as liquid democracy, which aims to reconcile the idealistic appeal of direct democracy with the practicality of representative democracy. The successful design of interactive democracy systems presents a multidisciplinary research challenge; one important aspect concerns the elicitation and aggregation of preferences. However, existing proposals are mostly disconnected from the vast body of scientific literature on preference aggregation and related topics. In this article, I argue that tools and techniques developed in the multiagent systems literature should be employed to aid the design of online decision platforms and other interactive democracy systems. Insights from computational social choice, an emerging research area at the intersection of computer science and economics, will be particularly relevant for this endeavor.

Interactive Democracy

Engaging global youth in participatory spatial data creation for the UN sustainable development goals: The case of open mapping for malaria prevention

Ten years have passed since in 2009 two independent technical systems pioneered liquid democracy (LD): Župa, a system developed for the Student Organisation of FIS in Slovenia, followed closely by LiquidFeedback, which served the Pirate Party in Berlin, Germany. First academic papers appeared in 2010 targeting the e-democracy research community, and later followed by researchers from artificial intelligence. This paper provides an overview of the scholarly discussions that developed in the past decade, and the technical implementations and initiatives that emerged.

An Overview of Ten Years of Liquid Democracy Research

Increasingly, popular online museums have significantly changed the way people acquire cultural knowledge. These online museums have been generating abundant amounts of cultural relics data. In recent years, researchers have used deep learning models that can automatically extract complex features and have rich representation capabilities to implement named-entity recognition (NER). However, the lack of labeled data in the field of cultural relics makes it difficult for deep learning models that rely on labeled data to achieve excellent performance. To address this problem, this paper proposes a semi-supervised deep learning model named SCRNER (Semi-supervised model for Cultural Relics’ Named Entity Recognition) that utilizes the bidirectional long short-term memory (BiLSTM) and conditional random fields (CRF) model trained by seldom labeled data and abundant unlabeled data to attain an effective performance. To satisfy the semi-supervised sample selection, we propose a repeat-labeled (relabeled) strategy to select samples of high confidence to enlarge the training set iteratively. In addition, we use embeddings from language model (ELMo) representations to dynamically acquire word representations as the input of the model to solve the problem of the blurred boundaries of cultural objects and Chinese characteristics of texts in the field of cultural relics. Experimental results demonstrate that our proposed model, trained on limited labeled data, achieves an effective performance in the task of named entity recognition of cultural relics.

Semi-Supervised Bidirectional Long Short-Term Memory and Conditional Random Fields Model for Named-Entity Recognition Using Embeddings from Language Models Representations.

Searching information in a Geographical Information System (GIS) usually imposes that users explore precompiled category catalogs and select the types of information they are looking for. Unfortunately, that approach is challenging because it forces people to adhere to a conceptualization of the information space that might be different from their own. In order to address this issue, we propose to support textual search as the basic interaction model, exploiting linguistic information, together with category exploration, for query interpretation and expansion. This paper describes our model and its adoption in the OnToMap Participatory GIS.

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Exploration of cultural heritage information via textual search queries

This paper focuses on the theme of the spatial representation of cities and the territory, reflecting on the prospects for innovation in the expressive means that serve the study of the city. The described research concerns project "Mappe di Comunita 3.0" (http://ontomap.dyndns.org/), funded by the Fondazione CRT. The project focuses on the definition of a methodology that implements a synergistic exchange between institutional territorial knowledge and the knowledge of the citizens, achievable thanks to the mediation of communication provided by a semantic representation of territorial knowledge. That type of representation supports the description of data and of its properties in a unified language. Moreover, it enables the sharing of information on the Web by providing an integrated perspective on territorial data

Production of spatial representations through collaborative mapping. An experiment

We present the information retrieval model adopted in the OnToMap Participatory GIS. The model addresses the limitations of keyword-based and category-based search by semantically interpreting the information needs specified in free-text search queries. The model is based on an ontological representation of linguistic and encyclopaedic knowledge, which makes it possible to exploit terms and synonyms occurring in the definitions of concepts to flexibly match the user's and system's terminologies. This feature enables users to query the application using their own vocabulary.

OnToMap: Semantic Community Maps for Knowledge Sharing

In this paper we describe the advancement of WeGovNow, an Horizon 2020 European Union project involving twelve partners from Germany, Sweden, Greece, Italy and United Kingdom, aimed at using state-of-the-art digital technologies in community engagement platforms to involve citizens in decision making processes within their local neighbourhood. Different software components, both previously existing and developed specially for the project and covering separate aspects of community engagement, were integrated in a single web platform offering an homogeneous experience to the users. One of the main common threads beyond this integration process is the ability to collect crowd mapped information and show them back to the users in an engaging way on maps, harmonizing data coming from the different components and making the mapped space easily explorable.

WeGovNow: A Map Based Platform to Engage the Local Civic Society

Textual queries are largely employed in information retrieval to let users specify search goals in a natural way. However, differences in user and system terminologies can challenge the identification of the user's information needs, and thus the generation of relevant results. We argue that the explicit management of ontological knowledge, and of the meaning of concepts (by integrating linguistic and encyclopaedic knowledge in the system ontology), can improve the analysis of search queries, because it enables a flexible identification of the topics the user is searching for, regardless of the adopted vocabulary. This paper proposes an information retrieval support model based on semantic concept identification. Starting from the recognition of the ontology concepts that the search query refers to, this model exploits the qualifiers specified in the query to select information items on the basis of possibly fine-grained features. Moreover, it supports query expansion and reformulation by suggesting the exploration of semantically similar concepts, as well as of concepts related to those referred in the query through thematic relations. A test on a data-set collected using the OnToMap Participatory GIS has shown that this approach provides accurate results.

Adriano Savoca

Papers

Exploration of cultural heritage information via textual search queries

Production of spatial representations through collaborative mapping. An experiment

OnToMap: Semantic Community Maps for Knowledge Sharing

WeGovNow: A Map Based Platform to Engage the Local Civic Society

Concept-aware geographic information retrieval