Spatial Items Research Articles

Item response theory in building environment engineering: A novel approach to identifying key residential environment items

The residential environment significantly influences residents’ health, quality of life, and well-being. Therefore, it is essential to assess the residential environment and implement improvement measures when problems are identified. In this context, the use of assessment tools such as BREEAM, LEED, and CASBEE has increased rapidly in recent years. Identifying key items with these assessment tools is crucial for prioritizing improvements to residential environment. This study introduces the novel application of item response theory (IRT) to building environment engineering data in order to identify key items from the CASBEE Residential Health Checklist. The strength of IRT lies in its ability to consider variations in scores for each item and the relative relationships between items, thereby enabling the identification of significantly influential key items. Examining Japanese residences, IRT identified 6 environmental items and 7 spatial items from a total of 44 items in the CASBEE Residential Health Checklist. Additionally, these environmental and spatial components were ranked by their explanatory power for the overall assessment of the residential environment. These findings contribute to prioritizing improvements in residential environments and provide a brief assessment method. IRT can be applied to other regions and building types, potentially enabling the identification of key items unique to each case. Thus, IRT is a versatile method with broad applicability for identifying key items in residential environment assessments.

Анализ подходов к образно-знаковому моделированию обстановки для решения специальных задач

The progress of computer technology has led to significant changes in the practice of spatial data graphical representation forming. The authors discuss the prospects of using cartographic animations to assess the situation on the ground, planning and managing the activities of operational services’ units in the course of solving special tasks, ways of increasing the automation level in the process of forming the situation’s spatial-temporal geo-information model. The goal is to solve a definite special problem due to the widespread use of the global navigation satellite systems consumer equipment, integrated with a wide range of onboard sensors (measuring instruments) in both stationary and mobile versions. It is justified that processing of point clouds obtained as a result of periodic measurements tied to a single time scale and spatial coordinate system will provide the management of operational services with a relevant geo-information model of the situation in the area of solving the mentioned matters. Various approaches to creating a figurative-sign model of the situation and formation of conventional signs used for its making are also considered. It is proposed to move from a traditional system of grouping spatial data in a geo-information model by thematic layers to an object-oriented hierarchy of spatial items, where the more global objects will be the "owners" of the constituent ones from the spatial or semantic point of view of the local features, that will provide increased flexibility of algorithms of automatic formation of a frame of cartographic animation due to application of recursive call of rendering methods for each component entering the hierarchy with arguments specific for each one.

Irrelevant speech impairs serial recall of verbal but not spatial items in children and adults

Immediate serial recall of visually presented items is reliably impaired by task-irrelevant speech that the participants are instructed to ignore (“irrelevant speech effect,” ISE). The ISE is stronger with changing speech tokens (words or syllables) when compared to repetitions of single tokens (“changing-state effect,” CSE). These phenomena have been attributed to sound-induced diversions of attention away from the focal task (attention capture account), or to specific interference of obligatory, involuntary sound processing with either the integrity of phonological traces in a phonological short-term store (phonological loop account), or the efficiency of a domain-general rehearsal process employed for serial order retention (changing-state account). Aiming to further explore the role of attention, phonological coding, and serial order retention in the ISE, we analyzed the effects of steady-state and changing-state speech on serial order reconstruction of visually presented verbal and spatial items in children (n = 81) and adults (n = 80). In the verbal task, both age groups performed worse with changing-state speech (sequences of different syllables) when compared with steady-state speech (one syllable repeated) and silence. Children were more impaired than adults by both speech sounds. In the spatial task, no disruptive effect of irrelevant speech was found in either group. These results indicate that irrelevant speech evokes similarity-based interference, and thus pose difficulties for the attention-capture and the changing-state account of the ISE.

Location-Aware Recommender System: A review of Application Domains and Current Developmental Processes

Recommender systems (RS) have been widely used to extract relevant and meaningful information from a vast body of data, to make appropriate suggestions to users with different preferences in various domains of applications. However, despite the success of the early recommendation systems, they suffer from two major challenges of cold start and data sparsity. Traditional RS consider an interaction between user and item (2D), neglecting contextual information such as location, until fairly recently. The contexts extend traditional RS to multi-dimension interaction and provides a useful information that allow recommendations to be more personalized. Surprisingly, taking these contexts such as location, into consideration eliminates the challenges of traditional RS. Location-Aware Recommender System (LARS) takes user's location into account as an additional context. The combination allows the prediction of spatial items, items closest to the users, to reduce information overload and was proved to be more effective than earlier RS. In this research, we provide a systematic literature of the existing literature in LARS from 2010 to 2021, focusing on the state-of-the-art methodologies, the domain of applications, and trends of publications in LARS. The paper proposed several models of LARS based on the traditional RS methodologies, providing future directions to researchers. Despite numerous reviews available on LARS, a review that proposed several LARS techniques were not found in the literature. The results indicated that the trend of publication in LARS is growing exponentially and that the field is getting attention rapidly with the number of publications on the rise every year.

Troubled past: A critical psychometric assessment of the self-report Survey of Autobiographical Memory (SAM).

The Survey of Autobiographical Memory (SAM) was designed as an easy-to-administer measure of self-perceived autobiographical memory (AM) recollection capacity. We provide a comprehensive psychometric evaluation of the SAM in younger and older adults. First, we evaluated the reliability of the SAM as a measure of self-perceived recollective capacity. Next, we tested whether the SAM was a valid measure of episodic and autobiographical memory performance, as assessed with widely used performance-based measures. Finally, we investigated associations between the SAM, cognitive measures and self-reported assessments of psychological functioning. The SAM demonstrated reliability as a self-report measure of perceived recollective capacity. High internal consistency was observed across subscales, with the exception of SAM-semantic. Evidence for independence among the subscales was mixed: SAM-episodic and SAM-semantic items showed poor correspondence with respective subscales. Good correspondence was observed between the future and spatial items and their SAM subscales. The SAM showed limited associations with AM performance as measured by the Autobiographical Interview (AI), yet was broadly associated with self-reported AI event vividness. SAM scores were weakly associated with performance-based memory measures and were age-invariant, inconsistent with known age effects on declarative memory. Converging evidence indicated that SAM-episodic and SAM-semantic subscales are not independent and should not be interpreted as specific measures of episodic or semantic memory. The SAM was robustly associated with self-efficacy, suggesting an association with confidence in domain general self-report abilities. We urge caution in the use and interpretation of the SAM as a measure of AM, pending revision and further psychometric validation.

A POI group recommendation method in location-based social networks based on user influence

Group recommendation has attracted researchers’ attention in various domains, specifically such approaches utilizing location-based social networks (LBSNs). However, point of interest (POI) group recommendation faces the challenge of aggregating diverse user preferences, while group members have different influences on the final decision of the group. Besides, the recommendation of spatial items is different from non-spatial items and the unique features of the spatial items such as distance must be considered in the recommendation. In this paper, a POI group recommendation method is proposed to tackle this problem. User influence is modeled fuzzy and taken into account the difference of users’ personality and their preferences when are alone or in a group, by using historical check-in data in LBSNs and in terms of category, distance and time. The proposed method is integrated with the weighted average aggregation to improve the efficiency of the POI group recommendation. Experimental results in a real dataset show improvement in the accuracy of POI group recommendations in varying sizes of groups. The results also get better when the user influence is calculated using the fuzzy approach. Besides, studying user behavior differences to choose the place to visit when alone or in a group shows that i) the flexibility of users in distance is less than time and category. It is also in the category less than time. ii) Time has a greater range of behavioral change than distance and category. iii) Users who actively participate in group decision making have a more significant number of visits in groups than when they are alone.

Possible Objects: Topological Approaches to Individuation.

We think of the world around us as divided into physical objects like toasters and daisies, rather than solely as a smear of properties like yellow and smooth. How do we single out these objects? One theory of object concepts uses part-of relations and relations of connectedness. According to this proposal, an object is a connected spatial item of maximal extent: Any other connected item that overlaps (i.e., shares a part with) the object must be a part of that object. This article reports four experiments that test this proposal. Participants see descriptions or diagrams of spatial items that vary across trials in their relative positions. In separate experiments, participants decide whether the items are physical objects, whether they are wholes, or how many objects are present. All experiments find support for connectedness as a contributor to object status, but they find little support for maximality. The results suggest that maximality is not a necessary feature of wholes or of objects.

Remember how to use it: Effector-dependent modulation of spatial working memory activity in posterior parietal cortex.

Working memory (WM) is the key process linking perception to action. Several lines of research have, accordingly, highlighted WM's engagement in sensori-motor associations between retrospective stimuli and future behavior. Using human fMRI we investigated whether prior information about the effector used to respond in a WM task would have an impact on the way the same sensory stimulus is maintained in memory despite a behavioral response could not be readily planned. We focused on WM-related activity in posterior parietal cortex during the maintenance of spatial items for a subsequent match-to-sample comparison, which was reported either with a verbal or with a manual response. We expected WM activity to be higher for manual response trials, because of posterior parietal cortex's engagement in both spatial WM and hand movement preparation. Increased fMRI activity for manual response trials in bilateral anterior intraparietal sulcus confirmed our expectations. These results imply that the maintenance of sensory material in WM is optimized for motor context, i.e. for the effector that will be relevant in the upcoming behavioral responses.

TPM

With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important way of helping users discover interesting locations to increase their engagement with location-based services. The availability of spatial, temporal, and social information in LBSNs offers an unprecedented opportunity to enhance the spatial item recommendation. Many previous works studied spatial and social influences on spatial item recommendation in LBSNs. Due to the strong correlations between a user’s check-in time and the corresponding check-in location, which include the sequential influence and temporal cyclic effect, it is essential for spatial item recommender system to exploit the temporal effect to improve the recommendation accuracy. Leveraging temporal information in spatial item recommendation is, however, very challenging, considering (1) when integrating sequential influences, users’ check-in data in LBSNs has a low sampling rate in both space and time, which renders existing location prediction techniques on GPS trajectories ineffective, and the prediction space is extremely large, with millions of distinct locations as the next prediction target, which impedes the application of classical Markov chain models; (2) there are various temporal cyclic patterns (i.e., daily, weekly, and monthly) in LBSNs, but existing work is limited to one specific pattern; and (3) there is no existing framework that unifies users’ personal interests, temporal cyclic patterns, and the sequential influence of recently visited locations in a principled manner. In light of the above challenges, we propose a Temporal Personalized Model ( TPM ), which introduces a novel latent variable topic-region to model and fuse sequential influence, cyclic patterns with personal interests in the latent and exponential space. The advantages of modeling the temporal effect at the topic-region level include a significantly reduced prediction space, an effective alleviation of data sparsity, and a direct expression of the semantic meaning of users’ spatial activities. Moreover, we introduce two methods to model the effect of various cyclic patterns. The first method is a time indexing scheme that encodes the effect of various cyclic patterns into a binary code. However, the indexing scheme faces the data sparsity problem in each time slice. To deal with this data sparsity problem, the second method slices the time according to each cyclic pattern separately and explores these patterns in a joint additive model. Furthermore, we design an asymmetric Locality Sensitive Hashing (ALSH) technique to speed up the online top- k recommendation process by extending the traditional LSH. We evaluate the performance of TPM on two real datasets and one large-scale synthetic dataset. The performance of TPM in recommending cold-start items is also evaluated. The results demonstrate a significant improvement in TPM’s ability to recommend spatial items, in terms of both effectiveness and efficiency, compared with the state-of-the-art methods.

The doubtful benefits of giving the benefit of the doubt: Lenient scoring of the spatial orientation items on the mini-Mental Status Exam increases false negative rates

Lenient scoring of spatial orientation errors (SOE) on the Mini-Mental State Exam (MMSE) is common practice, even though it deviates from standard protocol and may compromise its diagnostic power. This study was designed to empirically evaluate the effect of lenient scoring on the MMSE’s classification accuracy. Participants were 113 community dwelling older adults recruited for a research study, representing a wide range of range of neurological status from cognitively healthy to Alzheimer’s disease. Clinical classification was determined by expert assessors based on multiple sources of clinical evidence. Lenient scoring significantly inflated MMSE total scores (d = .88, large effect), and suppressed failure rates (from 26% to 14%). Standard scoring produced superior overall classification accuracy (75% vs. 67%) over lenient scoring and, more importantly, increased sensitivity from .33 to .53, with minimal loss in specificity (from 1.00 to .95). SOEs are empirical markers of cognitive decline and should not be adjusted based on clinical judgment. Results indicate that diminished sensitivity to cognitive impairment is an unintended consequence of lenient scoring and argue against this practice.

Functional similarities and differences between the coding of positional information in verbal and spatial short-term order memory

ABSTRACTTemporal grouping effects in verbal and spatial serial recall suggest that the representation of serial order in verbal and spatial short-term memory (STM) incorporates positional information. However, not all effects of grouping are created equal in the verbal and spatial domains. Although grouping a sequence of verbal items engenders an increase in between-group transpositions that maintain their within-group position, grouping a sequence of spatial items does not engender an increase in these so-called interposition errors. Here I present experimental and computational modeling evidence which suggests that positional information is represented in subtly different ways in verbal and spatial STM. Specifically, the findings indicate that in verbal STM, groups are coded for their position in a sequence and items are coded for their position in a group. By contrast, in spatial STM groups are coded for their position in a sequence, but items are coded for their position in a sequence, rather than in a group. Findings support the notion that positional information in verbal and spatial STM is represented by modality-specific mechanisms rather than a domain-general system.

How is the serial order of a visual sequence represented? Insights from transposition latencies.

A central goal of research on short-term memory (STM) over the past 2 decades has been to identify the mechanisms that underpin the representation of serial order, and to establish whether these mechanisms are the same across different modalities and domains (e.g., verbal, visual, spatial). A fruitful approach to addressing this question has involved comparing the transposition error latency predictions of models built from different candidate mechanisms for representing serial order. Experiments involving the output-timed serial recall of sequences of verbal (Farrell & Lewandowsky, 2004) and spatial (Hurlstone & Hitch, 2015) items have revealed an error latency profile consistent with the prediction of a competitive queuing mechanism within which serial order is represented via a primacy gradient of activations over items, associations between items and position markers, with suppression of items following recall. In this paper, we extend this chronometric analysis of recall errors to the serial recall of sequences of visual, nonspatial, items and find across 3 experiments an error latency profile broadly consistent with the prediction of the same representational mechanism. The findings suggest that common mechanisms and principles contribute to the representation of serial order across the verbal, visual, and spatial STM domains. The implications of these findings for theories of short-term and working memory are considered. (PsycINFO Database Record

R-tree for spatial data structure

Spatial database usually consult with the gathering of records which have a spatial coordinates and are described inside a space.
 On the way to support spatial items in a database gadget several issues need to be taken into consideration inclusive of spatial records models, indexing mechanisms, efficient query processing, and value models.
 A commonplace real global usage for an R-tree data shape might be to save spatial item, after which discover answer quick to queries. This means that its miles and green and essential index creation for plenty multidimensional statistics base software because each construction time and performance profits in question processing are vital. The proposed work is a simple implementation of spatial data structure that supports the manipulation, storage, and analysis and display data in visual form.

Mind-Language, the Expanding Heart of Cognition

On the basis of already published research on the phylogeny of language during the emergence of Homo Sapiens starting around 300,000 years ago, and on still-to-be-published research in its final phase on the psychogenesis of language starting in the 24th week of gestation, I will present my work on the central role of two virtual human constructs of man’s nervous system and brain confronted to their real environment, both natural and social. These two constructs, the mind and language, are the results of the development of the general pattern-capturing potential of the brain’s architecture. The mind and language develop simultaneously, reciprocally and in close coordination transforming the pattern-capturing potential of the brain into the mental and linguistic conceptualizing power of men and women. This long process of development can be captured in six stages: to sense; to perceive; to discriminate (or recognize) patterns; to experiment; to speculate, and to conceptualize spatial items and temporal processes. In psychogenetics, these six stages are essential for education. Lev Vygotsky, among others, has proposed the best approach to this conceptualizing competence in children and young adults, and expanding in our whole life.

ST-SAGE

With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important mobile application, especially when users travel away from home. However, this type of recommendation is very challenging compared to traditional recommender systems. A user may visit only a limited number of spatial items, leading to a very sparse user-item matrix. This matrix becomes even sparser when the user travels to a distant place, as most of the items visited by a user are usually located within a short distance from the user’s home. Moreover, user interests and behavior patterns may vary dramatically across different time and geographical regions. In light of this, we propose ST-SAGE, a spatial-temporal sparse additive generative model for spatial item recommendation in this article. ST-SAGE considers both personal interests of the users and the preferences of the crowd in the target region at the given time by exploiting both the co-occurrence patterns and content of spatial items. To further alleviate the data-sparsity issue, ST-SAGE exploits the geographical correlation by smoothing the crowd’s preferences over a well-designed spatial index structure called the spatial pyramid . To speed up the training process of ST-SAGE, we implement a parallel version of the model inference algorithm on the GraphLab framework. We conduct extensive experiments; the experimental results clearly demonstrate that ST-SAGE outperforms the state-of-the-art recommender systems in terms of recommendation effectiveness, model training efficiency, and online recommendation efficiency.

MINING CO-LOCATION PATTERNS FROM SPATIAL DATA

Due to the widespread application of geographic information systems (GIS) and GPS technology and the increasingly mature infrastructure for data collection, sharing, and integration, more and more research domains have gained access to high-quality geographic data and created new ways to incorporate spatial information and analysis in various studies. There is an urgent need for effective and efficient methods to extract unknown and unexpected information, e.g., co-location patterns, from spatial datasets of high dimensionality and complexity. A co-location pattern is defined as a subset of spatial items whose instances are often located together in spatial proximity. Current co-location mining algorithms are unable to quantify the spatial proximity of a co-location pattern. We propose a co-location pattern miner aiming to discover co-location patterns in a multidimensional spatial data by measuring the cohesion of a pattern. We present a model to measure the cohesion in an attempt to improve the efficiency of existing methods. The usefulness of our method is demonstrated by applying them on the publicly available spatial data of the city of Antwerp in Belgium. The experimental results show that our method is more efficient than existing methods.

MINING CO-LOCATION PATTERNS FROM SPATIAL DATA

Abstract. Due to the widespread application of geographic information systems (GIS) and GPS technology and the increasingly mature infrastructure for data collection, sharing, and integration, more and more research domains have gained access to high-quality geographic data and created new ways to incorporate spatial information and analysis in various studies. There is an urgent need for effective and efficient methods to extract unknown and unexpected information, e.g., co-location patterns, from spatial datasets of high dimensionality and complexity. A co-location pattern is defined as a subset of spatial items whose instances are often located together in spatial proximity. Current co-location mining algorithms are unable to quantify the spatial proximity of a co-location pattern. We propose a co-location pattern miner aiming to discover co-location patterns in a multidimensional spatial data by measuring the cohesion of a pattern. We present a model to measure the cohesion in an attempt to improve the efficiency of existing methods. The usefulness of our method is demonstrated by applying them on the publicly available spatial data of the city of Antwerp in Belgium. The experimental results show that our method is more efficient than existing methods.

Vividness of Object and Spatial Imagery.

Vividness is one of the fundamental characteristics of visual mental imagery. The first research goal was to examine whether vividness that refer to imagery of pictorial object (color, texture, or shape) versus spatial (three dimensional structure, location, or mechanism) properties constitute separate vividness dimensions. The second goal was to develop a vividness questionnaire separately assessing dimensions of imagery vividness. In Study 1, 111 students (M age = 21.8 years, SD = 1.3) evaluated the vividness of imagery evoked by nine object and nine spatial items from the pilot version of the new Vividness of Object and Spatial Imagery (VOSI) questionnaire, completed a self-report assessment of object and spatial imagery, and rated their aptitudes in art and science. Analysis indicated that imagery vividness comprised object and spatial dimensions. Object vividness items were positively associated with the self-report measure and ratings of artistic abilities, whereas spatial vividness items were positively associated with self-report measure and ratings of science abilities. In Study 2, an independent sample of 205 students (M age = 21 years, SD = 1.7) completed the second version of the VOSI, art and science aptitude ratings, and a number of self-report and performance measures assessing object and spatial imagery. Object and spatial imagery vividness items loaded on factors with 28 retained items; this two-factor vividness model fit the data better than a unidimensional vividness model. The questionnaire had satisfactory Cronbach's α for object vividness scale (.88) and for spatial vividness scale (.85). Correlational analyses supported convergent and discriminative validity of the VOSI. While object imagery vividness and spatial imagery vividness share some underlying vividness variance, they are dissociated into separate dimensions.

Personalized Recommender System using Collaborative Filtering Technique and Pyramid Maintenance Algorithm

Location aware recommender system(LAS) make use of spatial ratings for generating personalized recommendations.It uses Collaborative filtering techniques to generate recommendations based on user location,item location or both user and item location . LAS uses spatial ratings for spatial items , Spatial ratings for Non-spatial items, Non spatial ratings for spatial items to generate personalized recommendation. For spatial ratings for non spatial items LAS uses user partitioning technique where spatial ratings are distributed as per user location in the pyramid. Pyramid Maintenance algorithm provided to achieve required scalability or locality.LAS is scalable as number of γ-cells are increased in pyramid and to improve locality α-cells are increased to maintain CF Model. LAS is efficient as compare to traditional recommendation system because algorithm provided is strong enough to cope challenge of locality and scalability.

Spatial Structure of Evolutionary Models of Dialects in Contact.

Phylogenetic models, originally developed to demonstrate evolutionary biology, have been applied to a wide range of cultural data including natural language lexicons, manuscripts, folktales, material cultures, and religions. A fundamental question regarding the application of phylogenetic inference is whether trees are an appropriate approximation of cultural evolutionary history. Their validity in cultural applications has been scrutinized, particularly with respect to the lexicons of dialects in contact. Phylogenetic models organize evolutionary data into a series of branching events through time. However, branching events are typically not included in dialectological studies to interpret the distributions of lexical terms. Instead, dialectologists have offered spatial interpretations to represent lexical data. For example, new lexical items that emerge in a politico-cultural center are likely to spread to peripheries, but not vice versa. To explore the question of the tree model’s validity, we present a simple simulation model in which dialects form a spatial network and share lexical items through contact rather than through common ancestors. We input several network topologies to the model to generate synthetic data. We then analyze the synthesized data using conventional phylogenetic techniques. We found that a group of dialects can be considered tree-like even if it has not evolved in a temporally tree-like manner but has a temporally invariant, spatially tree-like structure. In addition, the simulation experiments appear to reproduce unnatural results observed in reconstructed trees for real data. These results motivate further investigation into the spatial structure of the evolutionary history of dialect lexicons as well as other cultural characteristics.