Search Results (17)

Geospatial data conflation involves matching and combining two maps to create a new map. It has received increased research attention in recent years due to its wide range of applications in GIS (Geographic Information System) data production and analysis. The map assignment problem (conceptualized in the 1980s) is one of the earliest conflation methods, in which GIS features from two maps are matched by minimizing their total discrepancy or distance. Recently, more flexible optimization models have been proposed. This includes conflation models based on the network flow problem and new models based on Mixed Integer Linear Programming (MILP). A natural question is: how are these models related or different, and how do they compare? In this study, an analytic review of major optimized conflation models in the literature is conducted and the structural linkages between them are identified. Moreover, a MILP model (the base-matching problem) and its bi-matching version are presented as a common basis. Our analysis shows that the assignment problem and all other optimized conflation models in the literature can be viewed or reformulated as variants of the base models. For network-flow based models, proof is presented that the base-matching problem is equivalent to the network-flow based fixed-charge-matching model. The equivalence of the MILP reformulation is also verified experimentally. For the existing MILP-based models, common notation is established and used to demonstrate that they are extensions of the base models in straight-forward ways. The contributions of this study are threefold. Firstly, it helps the analyst to understand the structural commonalities and differences of current conflation models and to choose different models. Secondly, by reformulating the network-flow models (and therefore, all current models) using MILP, the presented work eases the practical application of conflation by leveraging the many off-the-shelf MILP solvers. Thirdly, the base models can serve as a common ground for studying and writing new conflation models by allowing a modular and incremental way of model development. Full article

Recent advances in knowledge graphs show great promise to link various data together to provide a semantic network. Place is an important part in the big picture of the knowledge graph since it serves as a powerful glue to link any data to its georeference. A key technical challenge in constructing knowledge graphs with location nodes as geographical references is the matching of place entities. Traditional methods typically rely on rule-based matching or machine-learning techniques to determine if two place names refer to the same location. However, these approaches are often limited in the feature selection of places for matching criteria, resulting in imbalanced consideration of spatial and semantic features. Deep feature-based methods such as deep learning methods show great promise for improved place data conflation. This paper introduces a Semantic-Spatial Aware Representation Learning Model (SSARLM) for Place Matching. SSARLM liberates the tedious manual feature extraction step inherent in traditional methods, enabling an end-to-end place entity matching pipeline. Furthermore, we introduce an embedding fusion module designed for the unified encoding of semantic and spatial information. In the experiment, we evaluate the approach to named places from Guangzhou and Shanghai cities in GeoNames, OpenStreetMap (OSM), and Baidu Map. The SSARLM is compared with several classical and commonly used binary classification machine learning models, and the state-of-the-art large language model, GPT-4. The results demonstrate the benefit of pre-trained models in data conflation of named places. Full article

The psychrometric chart is the most common data visualization technique for the designers of thermal comfort systems worldwide. From its humble roots as means of expressing the characteristics of air in building systems design, the use of the chart has grown to include the representation of the zones of human thermal comfort according to both conventional and adaptive models. In this paper, we present an extensive history of this development and the fallacies with representing comfort simply as a box that sometimes moves on the chart. The origins of the link between refrigeration control and comfort control are examined through archival reviews, examining the works of Carrier, Yagoglou, and their contemporaries in the context of modern comfort mischaracterizations. A clearer understanding of the mapping of comfort, control, and climate metrics with psychrometrics is reported, and a critique of the conflation is reported to increase awareness of the limitations of such treatment of these three critical domains. Full article

Spatial data conflation is aimed at matching and merging objects in two datasets into a more comprehensive one. Starting from the “map assignment problem” in the 1980s, optimized conflation models treat feature matching as a natural optimization problem of minimizing certain metrics, such as the total discrepancy. One complication in optimized conflation is that heterogeneous datasets can represent geographic features differently. Features can correspond to target features in the other dataset either on a one-to-one basis (forming full matches) or on a many-to-one basis (forming partial matches). Traditional models consider either full matching or partial matches exclusively. This dichotomy has several issues. Firstly, full matching models are limited and cannot capture any partial match. Secondly, partial matching models treat full matches just as partial matches, and they are more prone to admit false matches. Thirdly, existing conflation models may introduce conflicting directional matches. This paper presents a new model that captures both full and partial matches simultaneously. This allows us to impose structural constraints differently on full/partial matches and enforce the consistency between directional matches. Experimental results show that the new model outperforms conventional optimized conflation models in terms of precision (89.2%), while achieving a similar recall (93.2%). Full article

The goal of this paper is to review the intertwined range of conceptualizations that have blurred developing leadership knowledge regarding environmental sustainability. An examination of the leadership literature reveals differential descriptions about sustainable, environmental, and sustainability leadership which are increasingly being used to imply what sustainability-focused leaders do, their interactions, their relationships, and how they address sustainable challenges. While extant research supports that leadership is a critical capability to respond and adapt to constant external environmental and economic upheaval in large firms, agreement about the types of leadership practices necessary to achieve positive environmental sustainability and eco-efficient outcomes is less clear in Small and Medium Enterprises (SMEs). To resolve these problems, we synthesize the sustainable, environmental and sustainability leadership literature by (a) reviewing and clarifying these leadership constructs, (b) theoretically unravelling these overlapping concepts, and (c) developing an integrated framework of intellectual capital and sustainability leadership practices. From a theoretical perspective, this paper seeks to make a significant contribution to the scholarly leadership literature by offering several leadership classifications of skills and knowledge relevant to leadership knowledge domains. Given that extant research has conflated many leadership approaches, this paper builds on the theoretical knowledge of the kind of leadership skills required for sustainability leadership. From a practical perspective, we provide SME leaders with knowledge about the types of leadership practices, behaviours, and activities that will enhance sustainable productivity in their firms. The paper is designed to advance a new way of thinking about existing sustainability leadership by presenting an original contribution that alters and reorganizes potential causal maps, that are potentially more valuable. Whilst most of the leadership research involves large firms, we seek to better understand and inform sustainability leadership in SMEs. Full article

Quantum information theorists have created axiomatic reconstructions of quantum mechanics (QM) that are very successful at identifying precisely what distinguishes quantum probability theory from classical and more general probability theories in terms of information-theoretic principles. Herein, we show how one such principle, Information Invariance and Continuity, at the foundation of those axiomatic reconstructions, maps to “no preferred reference frame” (NPRF, aka “the relativity principle”) as it pertains to the invariant measurement of Planck’s constant h for Stern-Gerlach (SG) spin measurements. This is in exact analogy to the relativity principle as it pertains to the invariant measurement of the speed of light c at the foundation of special relativity (SR). Essentially, quantum information theorists have extended Einstein’s use of NPRF from the boost invariance of measurements of c to include the SO(3) invariance of measurements of h between different reference frames of mutually complementary spin measurements via the principle of Information Invariance and Continuity. Consequently, the “mystery” of the Bell states is understood to result from conservation per Information Invariance and Continuity between different reference frames of mutually complementary qubit measurements, and this maps to conservation per NPRF in spacetime. If one falsely conflates the relativity principle with the classical theory of SR, then it may seem impossible that the relativity principle resides at the foundation of non-relativisitic QM. In fact, there is nothing inherently classical or quantum about NPRF. Thus, the axiomatic reconstructions of QM have succeeded in producing a principle account of QM that reveals as much about Nature as the postulates of SR. Full article

Point of interest (POI) data serves as a valuable source of semantic information for places of interest and has many geospatial applications in real estate, transportation, and urban planning. With the availability of different data sources, POI conflation serves as a valuable technique for enriching data quality and coverage by merging the POI data from multiple sources. This study proposes a novel end-to-end POI conflation framework consisting of six steps, starting with data procurement, schema standardisation, taxonomy mapping, POI matching, POI unification, and data verification. The feasibility of the proposed framework was demonstrated in a case study conducted in the eastern region of Singapore, where the POI data from five data sources was conflated to form a unified POI dataset. Based on the evaluation conducted, the resulting unified dataset was found to be more comprehensive and complete than any of the five POI data sources alone. Furthermore, the proposed approach for identifying POI matches between different data sources outperformed all baseline approaches with a matching accuracy of 97.6% with an average run time below 3 min when matching over 12,000 POIs to result in 8699 unique POIs, thereby demonstrating the framework’s scalability for large scale implementation in dense urban contexts. Full article

Van Gordon et al. outline the classification of their Ontological Addiction Theory (OAT), including its aetiology and treatment. In this review article I will from an appreciative perspective question some of its fundamental assumptions by presenting an alternative view on the ontology of ‘the person’, as distinct from its presently assumed conventional conflation with a contracted separate egoic self. I will propose this view as structurally and ethically significant for the ‘embodied’ experience of a reconstructed “dynamic and non-dual self”, as cultivated in their treatment. Rather than this reconstructed self simply being socially desirable for functional purposes, I will underscore the meaning-generative case for ontological status, in the absence of which, a pervasive ‘sense of lack’ is evident, with all attendant individual, psychological, social, ecological and ethical implications. This article brings a developmental psychology perspective to bear in appreciating ‘personhood’ as an emergent, progressively realised and is thus similarly aligned with the intent of OAT in overcoming egoic addictive suffering. This mapping of the territory however populates a blind spot in OAT’s diagnosis by affirming unique personhood, a quality of ‘integrative presence’, meaningfully understood as a psycho-spiritual ontological reality. It offers, as with OAT’s stated intent, the merit of avoiding attendant mental health and developmental pitfalls, which can beset what we may discern as an implicit transcendental reductionist assumption operative in OAT, where ‘the many’ are reduced to ‘the One’ and there are, it is assumed, no real many. This framing is resonant with the lived experience of healthy ‘individuation’, a process distinct from the problematic phenomenon of ‘individualism’, evidenced by the empirical data on post-conventional human development, which potentially provides diagnostic markers for any optimal treatment discernment. It is also attuned to what many recognise as a contemporary Fourth Turning in Buddhism, in its conscious evolutionary recognition of the emergence in non-dual states of a ‘unique personal perspective’, and/or a relative individuation within the whole. This differentiation has formerly been interpreted through an ‘impersonal’ lens as an egoic holdover, and potentially inhibits ethical action in the world, as distinct from the ethical import and potential fruits stemming from the ontological affirmation of the person. Full article

Combining misaligned spatial data from different sources complicates spatial analysis and creation of maps. Conflation is a process that solves the misalignment problem through spatial adjustment or attribute transfer between similar features in two datasets. Even though a combination of digital elevation model (DEM) and vector hydrographic lines is a common practice in spatial analysis and mapping, no method for automated conflation between these spatial data types has been developed so far. The problem of DEM and hydrography misalignment arises not only in map compilation, but also during the production of generalized datasets. There is a lack of automated solutions which can ensure that the drainage network represented in the surface of generalized DEM is spatially adjusted with independently generalized vector hydrography. We propose a new method that performs the conflation of DEM with linear hydrographic data and is embeddable into DEM generalization process. Given a set of reference hydrographic lines, our method automatically recognizes the most similar paths on DEM surface called counterpart streams. The elevation data extracted from DEM is then rubbersheeted locally using the links between counterpart streams and reference lines, and the conflated DEM is reconstructed from the rubbersheeted elevation data. The algorithm developed for extraction of counterpart streams ensures that the resulting set of lines comprises the network similar to the network of ordered reference lines. We also show how our approach can be seamlessly integrated into a TIN-based structural DEM generalization process with spatial adjustment to pre-generalized hydrographic lines as additional requirement. The combination of the GEBCO_2019 DEM and the Natural Earth 10M vector dataset is used to illustrate the effectiveness of DEM conflation both in map compilation and map generalization workflows. Resulting maps are geographically correct and are aesthetically more pleasing in comparison to a straightforward combination of misaligned DEM and hydrographic lines without conflation. Full article

Local mathematics consists of a collection of mathematical systems located at each space and time point. The collection is limited to the systems that include numbers in their axiomatic description. A scalar map between systems at different locations is based on the distinction of two conflated concepts, number and number value. The effect that this setup has on theory descriptions of physical and geometric systems is described. This includes a scalar spin 0 field in gauge theories, expectation values in quantum mechanics and path lengths in geometry. The possible relation of the scalar map to consciousness is noted. Full article

Road-matching processes establish links between multi-sourced road lines representing the same entities in the real world. Several road-matching methods have been developed in the last three decades. The main issue related to this process is selecting the most appropriate method. This selection depends on the data and requires a pre-process (i.e., accuracy assessment). This paper presents a new matching method for roads composed of different patterns. The proposed method matches road lines incrementally (i.e., from the most similar matching to the least similar). In the experimental testing, three road networks in Istanbul, Turkey, which are composed of tree, cellular, and hybrid patterns, provided by the municipality (authority), OpenStreetMap (volunteered), TomTom (private), and Basarsoft (private) were used. The similarity scores were determined using Hausdorff distance, orientation, sinuosity, mean perpendicular distance, mean length of triangle edges, and modified degree of connectivity. While the first four stages determined certain matches with regards to the scores, the last stage determined them with a criterion for overlapping areas among the buffers of the candidates. The results were evaluated with manual matching. According to the precision, recall, and F-value, the proposed method gives satisfactory results on different types of road patterns. Full article

With the increasingly urgent demand for map conflation and timely data updating, data matching has become a crucial issue in big data and the GIS community. However, non-rigid deviation, shape homogenization, and uncertain scale differences occur in crowdsourced and official building data, causing challenges in conflating heterogeneous building datasets from different sources and scales. This paper thus proposes an automated building data matching method based on relaxation labelling and pattern combinations. The proposed method first detects all possible matching objects and pattern combinations to create a matching table, and calculates four geo-similarities for each candidate-matching pair to initialize a probabilistic matching matrix. After that, the contextual information of neighboring candidate-matching pairs is explored to heuristically amend the geo-similarity-based matching matrix for achieving a contextual matching consistency. Three case studies are conducted to illustrate that the proposed method obtains high matching accuracies and correctly identifies various 1:1, 1:M, and M:N matching. This indicates the pattern-level relaxation labelling matching method can efficiently overcome the problems of shape homogeneity and non-rigid deviation, and meanwhile has weak sensitivity to uncertain scale differences, providing a functional solution for conflating crowdsourced and official building data. Full article

Several geospatial studies and applications require comprehensive semantic information from points-of-interest (POIs). However, this information is frequently dispersed across different collaborative mapping platforms. Surprisingly, there is still a research gap on the conflation of POIs from this type of geo-dataset. In this paper, we focus on the matching aspect of POI data conflation by proposing two matching strategies based on a graph whose nodes represent POIs and edges represent matching possibilities. We demonstrate how the graph is used for (1) dynamically defining the weights of the different POI similarity measures we consider; (2) tackling the issue that POIs should be left unmatched when they do not have a corresponding POI on the other dataset and (3) detecting multiple POIs from the same place in the same dataset and jointly matching these to the corresponding POI(s) from the other dataset. The strategies we propose do not require the collection of training samples or extensive parameter tuning. They were statistically compared with a “naive”, though commonly applied, matching approach considering POIs collected from OpenStreetMap and Foursquare from the city of London (England). In our experiments, we sequentially included each of our methodological suggestions in the matching procedure and each of them led to an increase in the accuracy in comparison to the previous results. Our best matching result achieved an overall accuracy of 91%, which is more than 10% higher than the accuracy achieved by the baseline method. Full article

Three-dimensional (3D) building models have been widely used in the fields of urban planning, navigation and virtual geographic environments. These models incorporate many details to address the complexities of urban environments. Level-of-detail (LOD) technology is commonly used to model progressive transmission and visualization. These detailed groups of models can be replaced by a single model using generalization. In this paper, the texture features are first introduced into the generalization process, and a self-organizing mapping (SOM)-based algorithm is used for texture classification. In addition, a new cognition-based hierarchical algorithm is proposed for model-group clustering. First, a constrained Delaunay triangulation (CDT) is constructed using the footprints of building models that are segmented by a road network, and a preliminary proximity graph is extracted from the CDT by visibility analysis. Second, the graph is further segmented by the texture–feature and landmark models. Third, a minimum support tree (MST) is created from the segmented graph, and the final groups are obtained by linear detection and discrete-model conflation. Finally, these groups are conflated using small-triangle removal while preserving the original textures. The experimental results demonstrate the effectiveness of this algorithm. Full article

In the past few years, volunteers have produced geographic information of different kinds, using a variety of different crowdsourcing platforms, within a broad range of contexts. However, there is still a lack of clarity about the specific types of tasks that volunteers can perform for deriving geographic information from remotely sensed imagery, and how the quality of the produced information can be assessed for particular task types. To fill this gap, we analyse the existing literature and propose a typology of tasks in geographic information crowdsourcing, which distinguishes between classification, digitisation and conflation tasks. We then present a case study related to the “Missing Maps” project aimed at crowdsourced classification to support humanitarian aid. We use our typology to distinguish between the different types of crowdsourced tasks in the project and choose classification tasks related to identifying roads and settlements for an evaluation of the crowdsourced classification. This evaluation shows that the volunteers achieved a satisfactory overall performance (accuracy: 89%; sensitivity: 73%; and precision: 89%). We also analyse different factors that could influence the performance, concluding that volunteers were more likely to incorrectly classify tasks with small objects. Furthermore, agreement among volunteers was shown to be a very good predictor of the reliability of crowdsourced classification: tasks with the highest agreement level were 41 times more probable to be correctly classified by volunteers. The results thus show that the crowdsourced classification of remotely sensed imagery is able to generate geographic information about human settlements with a high level of quality. This study also makes clear the different sophistication levels of tasks that can be performed by volunteers and reveals some factors that may have an impact on their performance. Full article