Temporal Axis Research Articles

DAS PRÁTICAS IRREGULARIDADES AO TERMO CORRUPÇÃO: CONSIDERAÇÕES SOBRE A ADMINISTRAÇÃO PÚBLICA NO BRASIL NOS ANOS 1950

In this study I seek to examine the process that displaces the term corruption of a vast semantic field and finds it gradually in the Vargas government, as well as the role of political conflict in this process through the disputes of political power projects in force. The temporal axis favors the years 1951 to 1954 because it includes constant denunciations in the newspapers of the time of irregularities involving agents of the public administration and of the government. For this analysis, three newspapers were used: Correio da Manhã, Tribuna da Imprensa and Última Hora. These respectively met the criteria of newspaper of great circulation, opposition and government. The stories were analyzed as narratives through which it is possible to identify social representations about corruption, state and public morality.

Analysis of Cultural Narratives in Works of Art for the Study of Ukrainian-Russian Relations

The article is devoted to the consideration of cultural narratives in works of art as a reflection of the nature of Ukrainian-Russian relations. The purpose of the article is to analyse the cultural narratives presented in works of art during the war, from the perspective of studying Ukrainian-Russian relations. The research methodology includes a qualitative analysis of works of art created during the war, using methods of content analysis and semiotic analysis. The research used the method of narrative analysis to reveal subjective ideas about the given problem. The presented factual information is analysed in the explicit content of the narrative. Content-oriented analysis is aimed at clarifying the implicit content: meaning and significance, manifestations of certain motives, features of the formulation of the narrative, its meaningful changes. Formal analysis consists in clarifying the structure of narratives, the sequence of changes, their relationship with the temporal axis, complexity and interconnectedness; feelings and images reflected in works of art. Semiotic analysis is used to study works of art, the meanings embedded in them in view of the change in the socio-political situation (from annexation to full-scale invasion). Scientific novelty. Its use, along with the traditional one, creates a new path for the development of conceptual art, which is now relevant, since it allows not only to restore one’s identity on the basis of ancient cultural traditions, but also to form a new awareness of oneself in the world space. Conclusions. As a result of the study, it was found that the pro-Russian narratives that existed in the cultural space of Ukraine before 2014 have disappeared, and pro-Ukrainian ones are being promoted more actively, which indicates an awareness of their identity and a desire for national independence. A rather dramatic change can be traced in relation to Russian culture, which is recognised as imposed from the outside and implanted over the centuries by force. Subsequent research involves systematisation and analysis of modern symbolism that arose during a full-scale war and evokes lasting associations.

A space-time generalized finite difference scheme for long wave propagation based on high-order Korteweg-de Vries type equations

In this paper, the space-time generalized finite difference scheme is applied to solve the nonlinear high-order Korteweg-de Vries equations in multiple dimensions. The proposed numerical scheme combines the space-time generalized finite difference method, the Levenberg-Marquardt algorithm, and a time-marching approach. The space-time generalized finite difference method treats the temporal axis as a spatial axis, enabling the proposed scheme to discretize all derivatives in the governing equation. This is accomplished through Taylor series expansion and the moving least squares method. Due to the expandability of the Taylor series to any order, the proposed numerical scheme excels in efficiently handling mixed and higher-order derivatives. These capabilities are distinct advantages of the proposed scheme. The resulting system of algebraic equations is sparse but overdetermined. Therefore, the Levenberg-Marquardt algorithm is directly applied to solve this nonlinear algebraic system. During the calculation process, the time-marching approach reduces computational effort and improves efficiency by dividing the space-time domain.

Every intrusion has its time: New zircon U-Pb ages from the Greater Antilles Arc in the Cordillera Central, Dominican Republic

The Cordillera Central on the island of Hispaniola comprises a section through the crust of the Late Cretaceous phase of the Greater Antilles Arc. The gabbroic-tonalitic batholiths of the Cordillera Central are widely considered to represent the temporal main axis of this island arc. Although they have been studied extensively, the timing of batholith emplacement is not well constrained. We present new LA-ICP-MS U-Pb zircon ages and whole-rock geochemical data from diorites, tonalites, and granites of the El Río, El Bao, Jumunuco, and Loma de Cabrera batholiths, and from tonalite boulders deposited in the Eocene-Oligocene Velázquitos Formation. The sample ages range from 97 to 80 Ma with small differences between the intrusive bodies. Samples from the El Río, Jumunuco, and Loma de Cabrera batholiths cover most of the above age range, while the rocks of the El Bao pluton yield a well-defined maximum around 87 Ma. In the El Río batholith, U-Pb ages are in good agreement with the exposed contact relationships. Based on their composition and existing geochronological data, we divide the intrusive history of the Cordillera Central into four stages reflecting different magma compositions: ultramafic to dioritic magmatism lasted from 105 to 87 Ma, tonalite units formed between 96 and 84 Ma, felsic tonalites and granites intruded from 87 to 80 Ma overlapping with a younger phase of dioritic magmatism between 84 and 78 Ma.The geochemical signatures of the felsic tonalites of the Jumunuco batholith and the granites of the El Río batholith are similar to the rhyolitic and dacitic island-arc volcanics from the Restauración Formation of the Tireo Group, thus confirming the relationship between the volcanic and plutonic units of the Greater Antilles Arc. Based on petrographical, geochemical, and geochronological data of the tonalite samples from the Velázquitos Formation, we interpret these boulders as being eroded from the Jumunuco batholith or the related Buena Vista pluton.

How viruses spread across space and time: forecasting pandemic progression by modelling geographico-temporal interactions

The COVID-19 pandemic has revealed severe flaws in the global healthcare systems ability to respond to unexpected health catastrophes. Much of the confusion and mishandling of the situation could be attributed to the failure in accurately predicting the spread of the virus across geographical locations. A global resource shortage in essential medical supplies and equipment, such as personal protective equipment (PPE) and ventilators, led to a compromised global supply chain. As a result, resources could not be allocated as needed to curb the spread of the pathogen in the most efficacious way. Although forecast models and machine learning algorithms have served as invaluable tools in devising effective response strategies, a large majority of these models were limited by their ability to describe the intricate interactions that underlie the spatio-temporal dynamics of viral proliferation. To address this issue, we employed a vector autoregression model to help capture the evolution of the disease across both the spatial and the temporal axes. Unlike traditional autoregression models, the present model is able to account for statistical regularities that exist both within a given region, and between geographical locations. Our results demonstrate that this approach accurately described the relationships across domestic and international localities throughout the evolution of the disease.

Open-Set Recognition of Pansori Rhythm Patterns Based on Audio Segmentation

Pansori, a traditional Korean form of musical storytelling, is characterized by performances involving a vocalist and a drummer. It is well-known for the singer’s expressive narrative (aniri) and delicate gesture with fan in hand. The classical Pansori repertoires mostly tell love, satire, and humor, as well as some social lessons. These performances, which can extend from three to five hours, necessitate that the vocalist adheres to precise rhythmic structures. The distinctive rhythms of Pansori are crucial for conveying both the narrative and musical expression effectively. This paper explores the challenge of open-set recognition, aiming to efficiently identify unknown Pansori rhythm patterns while applying the methodology to diverse acoustic datasets, such as sound events and genres. We propose a lightweight deep learning-based encoder–decoder segmentation model, which employs a 2-D log-Mel spectrogram as input for the encoder and produces a frame-based 1-D decision along the temporal axis. This segmentation approach, processing 2-D inputs to classify frame-wise rhythm patterns, proves effective in detecting unknown patterns within time-varying sound streams encountered in daily life. Throughout the training phase, both center and supervised contrastive losses, along with cross-entropy loss, are minimized. This strategy aimed to create a compact cluster structure within the feature space for known classes, thereby facilitating the recognition of unknown rhythm patterns by allocating ample space for their placement within the embedded feature space. Comprehensive experiments utilizing various datasets—including Pansori rhythm patterns (91.8%), synthetic datasets of instrument sounds (95.1%), music genres (76.9%), and sound datasets from DCASE challenges (73.0%)—demonstrate the efficacy of our proposed method to detect unknown events, as evidenced by the AUROC metrics.

An integrated assessment of niche partitioning reveals mechanisms of coexistence between mesocarnivores

Species diversity depends on the capacity of species to coexist, and when these share similar ecological requirements some degree of partitioning of the ecological niche is expected. Within the order Carnivora, direct and indirect interspecific interactions shape community structure and composition. Although strong negative interactions (e.g., interspecific killing) are expected between large carnivores and subordinate species, mesocarnivores that dominate most human disturbed landscapes can still strongly compete for resources given their similar body size and energy requirements. To identify the mechanisms of coexistence between mesocarnivore species in a Mediterranean landscape we assessed the ecological niche overlap between five species (Vulpes vulpes, Meles meles, Martes foina, Herpestes ichneumon, Genetta genetta), by simultaneously measuring the space, time, and trophic resource use. The multi-species occupancy model fit to camera-trapping data revealed spatial co-occurrence between most species’ pairs and an avoidance pattern between only stone marten and Eurasian badger (78 % probability of a negative effect). Using genetically identified scats, we assessed trophic niche breadth and overlap. Niche breadth was narrow for red fox, Eurasian badger, and stone marten, given these species consumed almost exclusively fruits and arthropods, and so trophic niche overlap was high. For the genet and the mongoose, the consumption of different food resources (e.g., mammals, birds, and reptiles) increased niche breadth and decreased the niche overlap with the other sympatric species. Finally, niche segregation was more significant along the temporal axis, since the mongoose has a diurnal activity pattern and the remaining carnivore species, although mostly nocturnal, had small asynchronies in the activity peaks which could decrease the chance of direct encounters. Therefore, our results suggest that similar habitat preferences and ecological requirements rather than interspecific competition shape the mesocarnivores’ resource use in this landscape. Nonetheless, fine-scale temporal segregation seems the main mechanism to facilitate coexistence, together with small differences in trophic resource use, especially for species with high spatial and temporal overlap.

Positive spatial and temporal density‐dependence drive early reproductive economy‐of‐scale effects of masting in a European old‐growth forest community

Abstract Masting, the spatial synchronization of interannual variation in seed production, can enhance reproductive efficiency through positive density‐dependent processes (DD) that result in economies of scale (EOS), such as decreased pollen limitation and predator satiation in years of high reproduction. While the general occurrence of such EOS effects has been documented for masting species, few studies simultaneously investigated how spatial and temporal variation in reproduction affects pollination and predation. Furthermore, it is unclear whether the same mechanisms apply to co‐occurring species with different levels of conspecific density, pollen limitation, and seed defences. Here, we use a long‐term dataset with high spatial resolution of seed production of European beech (Fagus sylvatica), Norway spruce (Picea abies), and silver fir (Abies alba) in a primeval montane forest to investigate the relationship between reproductive effort, pollination efficiency and predispersal predation by insects. We found that, along the temporal axis, the proportion of sound (fertilized and unpredated) seeds correlated positively with annual seed production over the 14‐year study period in all three species, most strongly in beech and only weakly in silver fir. Moreover, the results show that in beech, spatial seed density interacts with plot‐wide annual seed rain to enhance DD effects on seed predation, suggesting additive effects of synchronous reproduction on fitness benefits. Synthesis: For both pollination and predispersal predation in beech and spruce, the strongest DD effects occur at low levels of reproduction and quickly reach asymptotes at higher levels, suggesting the presence of thresholds in different EOS mechanisms. As variability and synchrony in mast‐seeding are expected to decline with climate change, EOS effects driven by DD may remain stable until the threshold is reached, at which sudden declines would result in devastating effects on the availability of viable seeds for germination and recruitment.

Time-resolved cryogenic electron tomography for the study of transient cellular processes.

Cryogenic electron tomography (cryo-ET) is the highest resolution imaging technique applicable to the life sciences, enabling subnanometer visualization of specimens preserved in their near native states. The rapid plunge freezing process used to prepare samples lends itself to time-resolved studies, which researchers have pursued for in vitro samples for decades. Here, we focus on developing a freezing apparatus for time-resolved studies in situ. The device mixes cellular samples with solution-phase stimulants before spraying them directly onto an electron microscopy grid that is transiting into cryogenic liquid ethane. By varying the flow rates of cell and stimulant solutions within the device, we can control the reaction time from tens of milliseconds to over a second before freezing. In a proof-of-principle demonstration, the freezing method is applied to a model bacterium, Caulobacter crescentus, mixed with an acidic buffer. Through cryo-ET we resolved structural changes throughout the cell, including surface-layer protein dissolution, outer membrane deformation, and cytosolic rearrangement, all within 1.5 s of reaction time. This new approach, Time-Resolved cryo-ET (TR-cryo-ET), enhances the capabilities of cryo-ET by incorporating a subsecond temporal axis and enables the visualization of induced structural changes at the molecular, organelle, or cellular level.

Multichannel Hierarchical Analysis of Time-Resolved Hyperspectral Data for Advanced Colorimetric E-Nose.

The colorimetric sensor-based electronic nose has been demonstrated to discriminate specific gaseous molecules for various applications, including health or environmental monitoring. However, conventional colorimetric sensor systems rely on RGB sensors, which cannot capture the complete spectral response of the system. This limitation can degrade the performance of machine learning analysis, leading to inaccurate identification of chemicals with similar functional groups. Here, we propose a novel time-resolved hyperspectral (TRH) data set from colorimetric array sensors consisting of 1D spatial, 1D spectral, and 1D temporal axes, which enables hierarchical analysis of multichannel 2D spectrograms via a convolution neural network (CNN). We assessed the outstanding classification performance of the TRH data set compared to an RGB data set by conducting a relative humidity (RH) concentration classification. The time-dependent spectral response of the colorimetric sensor was measured and trained as a CNN model using TRH and RGB sensor systems at different RH levels. While the TRH model shows a high classification accuracy of 97.5% for the RH concentration, the RGB model yields 72.5% under identical conditions. Furthermore, we demonstrated the detection of various functional volatile gases with the TRH system by using experimental and simulation approaches. The results reveal distinct spectral features from the TRH system, corresponding to changes in the concentration of each substance.

Diverse and Aligned Audio-to-Video Generation via Text-to-Video Model Adaptation

We consider the task of generating diverse and realistic videos guided by natural audio samples from a wide variety of semantic classes. For this task, the videos are required to be aligned both globally and temporally with the input audio: globally, the input audio is semantically associated with the entire output video, and temporally, each segment of the input audio is associated with a corresponding segment of that video. We utilize an existing text-conditioned video generation model and a pre-trained audio encoder model. The proposed method is based on a lightweight adaptor network, which learns to map the audio-based representation to the input representation expected by the text-to-video generation model. As such, it also enables video generation conditioned on text, audio, and, for the first time as far as we can ascertain, on both text and audio. We validate our method extensively on three datasets demonstrating significant semantic diversity of audio-video samples and further propose a novel evaluation metric (AV-Align) to assess the alignment of generated videos with input audio samples. AV-Align is based on the detection and comparison of energy peaks in both modalities. In comparison to recent state-of-the-art approaches, our method generates videos that are better aligned with the input sound, both with respect to content and temporal axis. We also show that videos produced by our method present higher visual quality and are more diverse. Code and samples are available at: https://pages.cs.huji.ac.il/adiyoss-lab/TempoTokens/.

Bacterial growth-mediated systems remodelling of Nicotiana benthamiana defines unique signatures of target protein production in molecular pharming.

The need for therapeutics to treat a plethora of medical conditions and diseases is on the rise and the demand for alternative approaches to mammalian-based production systems is increasing. Plant-based strategies provide a safe and effective alternative to produce biological drugs but have yet to enter mainstream manufacturing at a competitive level. Limitations associated with batch consistency and target protein production levels are present; however, strategies to overcome these challenges are underway. In this study, we apply state-of-the-art mass spectrometry-based proteomics to define proteome remodelling of the plant following agroinfiltration with bacteria grown under shake flask or bioreactor conditions. We observed distinct signatures of bacterial protein production corresponding to the different growth conditions that directly influence the plant defence responses and target protein production on a temporal axis. Our integration of proteomic profiling with small molecule detection and quantification reveals the fluctuation of secondary metabolite production over time to provide new insight into the complexities of dual system modulation in molecular pharming. Our findings suggest that bioreactor bacterial growth may promote evasion of early plant defence responses towards Agrobacterium tumefaciens (updated nomenclature to Rhizobium radiobacter). Furthermore, we uncover and explore specific targets for genetic manipulation to suppress host defences and increase recombinant protein production in molecular pharming.

Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence.

Emergent biological dynamics derive from the evolution of lower-level spatial and temporal processes. A long-standing challenge for scientists and engineers is identifying simple low-level rules that give rise to complex higher-level dynamics. High-resolution biological data acquisition enables this identification and has evolved at a rapid pace for both experimental and computational approaches. Simultaneously harnessing the resolution and managing the expense of emerging technologies-e.g. live cell imaging, scRNAseq, agent-based models-requires a deeper understanding of how spatial and temporal axes impact biological systems. Effective emulation is a promising solution to manage the expense of increasingly complex high-resolution computational models. In this research, we focus on the emulation of a tumor microenvironment agent-based model to examine the relationship between spatial and temporal environment features, and emergent tumor properties. Despite significant feature engineering, we find limited predictive capacity of tumor properties from initial system representations. However, incorporating temporal information derived from intermediate simulation states dramatically improves the predictive performance of machine learning models. We train a deep-learning emulator on intermediate simulation states and observe promising enhancements over emulators trained solely on initial conditions. Our results underscore the importance of incorporating temporal information in the evaluation of spatio-temporal emergent behavior. Nevertheless, the emulators exhibit inconsistent performance, suggesting that the underlying model characterizes unique cell populations dynamics that are not easily replaced. All source codes for the agent-based model, emulation, and analyses are publicly available at the corresponding DOIs: 10.5281/zenodo.10622155, 10.5281/zenodo.10611675, 10.5281/zenodo.10621244, respectively.

An encoding approach for stable change point detection

Without imposing prior distributional knowledge underlying multivariate time series of interest, we propose a nonparametric change-point detection approach to estimate the number of change points and their locations along the temporal axis. We develop a structural subsampling procedure such that the observations are encoded into multiple sequences of Bernoulli variables. A maximum likelihood approach in conjunction with a newly developed searching algorithm is implemented to detect change points on each Bernoulli process separately. Then, aggregation statistics are proposed to collectively synthesize change-point results from all individual univariate time series into consistent and stable location estimations. We also study a weighting strategy to measure the degree of relevance for different subsampled groups. Simulation studies are conducted and shown that the proposed change-point methodology for multivariate time series has favorable performance comparing with currently available state-of-the-art nonparametric methods under various settings with different degrees of complexity. Real data analyses are finally performed on categorical, ordinal, and continuous time series taken from fields of genetics, climate, and finance.

Improved temporal resolution and acceleration on 4D-MR angiography based on superselective pseudo-continuous arterial spin labeling combined with CENTRA-keyhole and view-sharing (4D-S-PACK) using an interpolation algorithm on the temporal axis and compressed sensing–sensitivity encoding (CS-SENSE)

PurposeTwo major drawbacks of 4D-MR angiography based on superselective pseudo-continuous arterial spin labeling combined with CENTRA-keyhole and view-sharing (4D-S-PACK) are the low temporal resolution and long scanning time. We investigated the feasibility of increasing the temporal resolution and accelerating the scanning time on 4D-S-PACK by using CS-SENSE and PhyZiodynamics, a novel image-processing program that interpolates images between phases to generate new phases and reduces image noise. MethodsSeven healthy volunteers were scanned with a 3.0 T MR scanner to visualize the internal carotid artery (ICA) system. PhyZiodynamics is a novel image-processing that interpolates images between phases to generate new phases and reduces image noise, and by increasing temporal resolution using PhyZiodynamics, inflow dynamic data (reference) were acquired by changing the labeling durations (100–2000 msec, 31 phases) in 4D-S-PACK. From this set of data, we selected seven time intervals to calculate interpolated time points with up to 61 intervals using ×10 for the generation of interpolated phases with PhyZiodynamics. In the denoising process of PhyZiodynamics, we processed the none, low, medium, high noise reduction dataset images. The time intensity curve (TIC), the contrast-to-noise ratio (CNR) were evaluated. In accelerating with CS-SENSE for 4D-S-PACK, 4D-S-PACK were scanned different SENSE or CS-SENSE acceleration factors: SENSE3, CS3–6. Signal intensity (SI), CNR, were evaluated for accelerating the 4D-S-PACK. With regard to arterial vascular visualization, we evaluated the middle cerebral artery (MCA: M1–4 segments). ResultsIn increasing temporal resolution, the TIC showed a similar trend between the reference dataset and the interpolated dataset. As the noise reduction weight increased, the CNR of the interpolated dataset were increased compared to that of the reference dataset. In accelerating 4D-S-PACK, the SI values of the SENSE3 dataset and CS dataset with CS3–6 were no significant differences. The image noise increased with the increase of acceleration factor, and the CNR decreased with the increase of acceleration factor. Significant differences in CNR were observed between acceleration factor of SENSE3 and CS6 for the M1–4 (P < 0.05). Visualization of small arteries (M4) became less reliable in CS5 or CS6 images. Significant differences were found for the scores of M2, M3 and M4 segments between SENSE3 and CS6. ConclusionWith PhyZiodynamics and CS-SENSE in 4D-S-PACK, we were able to shorten the scan time while improving the temporal resolution.

RA-marking, delimitation, and TA-headed directional PPs in Persian

Abstract In this article, we examine the semantics of RA in Persian, focusing on the aspectual notion of delimitation. Delimitation is a term commonly used in the study of aspect and information structure. We distinguish between two functions of RA: as a semantic operator that measures delimited events involving mereological theme and incremental path verbs, and as an information-structural marker where RA serves as frame-setting accusative adjuncts. We also explore the aspectuality of RA in correlation with a motion construction involving a TA-headed goal phrase within the scope of RA. The motion event with TA, representing the vector, is rendered bounded through the influence of RA, indicating the endpoint of the path. Using Croft’s (2012) two-phase dimensional model of aspect, we demonstrate that TA and RA are both associated with delimitation, but operate on different axes of boundedness – TA on the qualitative axis and RA on the temporal axis.

Spike learning based Privacy Preservation of Internet of Medical Things in Metaverse.

With the rising trend of digital technologies, such as augmented and virtual reality, Metaverse has gained a notable popularity. The applications that will eventually benefit from Metaverse is the telemedicine and e-health fields. However, the data and techniques used for realizing the medical side of Metaverse is vulnerable to data and class leakage attacks. Most of the existing studies focus on either of the problems through encryption techniques or addition of noise. In addition, the use of encryption techniques affects the overall performance of the medical services, which hinders its realization. In this regard, we propose Generative adversarial networks and spike learning based convolutional neural network (GASCNN) for medical images that is resilient to both the data and class leakage attacks. We first propose the GANs for generating synthetic medical images from residual networks feature maps. We then perform a transformation paradigm to convert ResNet to spike neural networks (SNN) and use spike learning technique to encrypt model weights by representing the spatial domain data into temporal axis, thus making it difficult to be reconstructed. We conduct extensive experiments on publicly available MRI dataset and show that the proposed work is resilient to various data and class leakage attacks in comparison to existing state-of-the-art works (1.75x increase in FID score) with the exception of slightly decreased performance (less than 3%) from its ResNet counterpart. while achieving 52x energy efficiency gain with respect to standard ResNet architecture.

Modification of the Differential Transform Method With Search Direction Along the Spatial Axis

This paper presents a modification of the Differential Transform Method (ModDTM) formulated so that the search direction of the solution is along a spatial axis (x). It is shown that the method is applicable to a wide spectrum of (1 + 1) partial differential equations, integro partial differential equations, and integral equations. The solutions obtained are in the form of a Taylor series, the coefficients of which are determined by recursively operating a differential transform of the equation. These solutions are either exact and in closed form, or are good approximations. To illustrate the application of the method, as well as to show its versatility, examples are chosen from all the categories mentioned earlier. These include equations such as the nonlinear Fisher, combined KdV‐mKdV, Hunter–Saxton (H‐S), Fornberg–Whitham (FW), coupled systems such as the Whitham–Broer–Kaup (WBK) and the sine‐Gordon (s‐G) equations, integral equations, Volterra partial integro‐differential equations (PIDE), and linear and nonlinear versions of the (complex) Schröedinger equation. The Tables and 3D‐plots show the good rate of convergence of the obtained solutions with the exact ones. It is thus found that the new scheme is successful in producing satisfactory results as any other method in which the conventional approach of searching for solutions along the temporal axis is followed. Further, the procedures involved are simple, and hence may be operated with great ease.

Spatiotemporal Variograms as Neighborhood Definers

Spatial neighborhood definitions are a consistent source of disagreement among geographic scholars. This research will focus on the implementation and evaluation of spatiotemporal variograms (STVs) as a source of spatial neighborhood definition. STVs show the similarity, measured by semivariance, of spatial events to each other when separated by time and space. Over both time and space, there should exist distances over which pairs of points become “independent” of one another. We seek seeks to answer two questions in relation to STVs and their use as neighborhood definitions: (1) What data and process adjustments are necessary to implement STVs to provide neighborhood search definitions in time and space, and (2) Given that there are many ways to define a neighborhood, STVs may provide a comprehensive method that uses the data themselves to inform the size and scope of neighborhoods, with the added advantage to evaluating both spatial and temporal axes at once. We demonstrate a well‐defined neighborhood that accounts for temporal variation as well as spatial and will be a needed addition as the tools incorporating simultaneous spatial and temporal neighborhoods are implemented.

Event pattern analysis: the point density around the appearance and disappearance of points

ABSTRACT Point pattern analysis is a fundamental analytical tool in various academic fields related to spatial information science. Basic but important patterns discussed in existing studies are spatially clustered and dispersed points. Analysis of these static patterns aims to reveal their underlying structure, i.e. why and how they are formed. However, to answer this research question, the direct tracking of the pattern formation process is more effective, though we have not yet fully conducted such analysis along the temporal axis. To fill the research gap, this paper develops a new method of spatiotemporal analysis. We focus on the point density around the appearance and disappearance of points. A research question is whether points appear/disappear in dense or sparse space. Extending Ripley’s K-function, we develop four measures statistically evaluating the point density. We applied the proposed method to the analysis of the competition among convenience store chains in Shibuya-ku, Tokyo.