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  Subjects -> GEOGRAPHY (Total: 493 journals)
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Journal of Geographical Systems
Journal Prestige (SJR): 0.589
Citation Impact (citeScore): 2
Number of Followers: 5  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1435-5949 - ISSN (Online) 1435-5930
Published by Springer-Verlag Homepage  [2469 journals]
  • A cluster-driven classification approach to truck stop location
           identification using passive GPS data

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      Abstract: Abstract Classifying the type of truck stops is vital in transportation planning and goods movement strategies. Truck stops could be classified into primary or secondary. While the latter entail stopping to re-fuel or rest, the former takes place to deliver or pick up merchandize. The availability of GPS transponders on board moving trucks and the ability to access such information in recent years has made it possible to analyze various freight aspects including movement trajectories and stopped locations. This paper utilizes machine learning methods and proposes a two-step cluster-based classification approach to classify truck stop locations into either primary or secondary. The DBSCAN clustering technique is applied on the GPS dataset to obtain stop locations. Next, several features per location are derived to classify the stops using well-known classification models. The generated information is then used to evaluate the approach using a large truck GPS dataset for the year 2016. The Random Forest classifier is chosen as it can identify primary stop locations with an accuracy of 97%. The overall accuracy of the classifier for correctly identifying both types of stops is 83%. Further, the prediction accuracy for primary stops is 92%.
      PubDate: 2022-06-11
       
  • A scoping review on the multiplicity of scale in spatial analysis

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      Abstract: Scale is a central concept in the geographical sciences and is an intrinsic property of many spatial systems. It also serves as an essential thread in the fabric of many other physical and social sciences, which has contributed to the use of different terminology for similar manifestations of what we refer to as ‘scale’, leading to a surprising amount of diversity around this fundamental concept and its various ‘multiscale’ extensions. To address this, we review common abstractions about spatial scale and how they are employed in quantitative research. We also explore areas where the conceptualizations of multiple spatial scales can be differentiated. This is achieved by first bridging terminology and concepts, and then conducting a scoping review of the topic. A typology for spatial scale is discussed that can be used to categorize its multifarious meanings and measures. This typology is then used to distinguish what we term ‘process scale,’ from other types of spatial scale and to highlight current trends in uncovering aspects of process scale. We end with suggestions on how to further build knowledge regarding spatial processes through the lens of spatial scale.
      PubDate: 2022-06-09
       
  • An analysis about the accuracy of geographic profiling in relation to the
           number of observations and the buffer zone

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      Abstract: Abstract Geographic Profiling (GP) attempts to reconstruct the spreading centre of a series of events due to the same cause. The result of the analysis provides an approximated localization of the spreading centre within an area (often represented as a red red), where the probability of finding it is higher than a given threshold (typically 95%). The analysis has as an assumption that the events will be likely to occur at very low probability around the spreading centre, in a ring-shaped zone called the buffer zone. Obvious examples are series of crimes perpetrated by an offender (unwilling to perpetrate offences close to home), or the localities of spread of an invasive species, where the buffer zone, if present, depends on the biological features of the species. Our first aim was to show how the addition of new events may change the preliminary approximate localization of the spreading centre. The analyses of the simulated data showed that if B, the parameter used to represent the radius of the buffer zone, varies within a range of 10% from the real value, after a low number of events (7–8), the method yields converging results in terms of distance between the barycentre of the red zone and the “real” user provided spreading centre of a simulated data set. The convergence occurs more slowly with the increase in inaccuracy of B. These results provide further validity to the method of the GP, showing that even an approximate choice of the B value can be sufficient for an accurate location of the spreading centre. The results allow also to quantify how many samples are needed in relation to the uncertainty of the chosen parameters, to obtain feasible results.
      PubDate: 2022-06-09
       
  • Accessibility analysis of urban fire stations within communities: a
           fine-scale perspective

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      Abstract: Abstract In recent years, with the rapid expansion of urban space and the explosion of population within communities in China, fire stations face challenges in providing timely response to potential demands throughout their service coverage. To ensure speedy and equitable provision of fire services, it is essential to evaluate the accessibility of fire stations under the current fire service systems. Traditional accessibility analysis is often based on the aggregated large areal units, such as the census tracks, failing to assess accessibility of individual buildings. In this regard, this study seeks to analyze potential accessibility to urban fire stations at a fine scale, i.e., the building level, and to provide valuable information to assist in strategic planning of fire stations in urban areas and within local communities. Because the detailed intra-community roads are not stored in the official city map database, we propose to use a classic GIS technology, the Delaunay triangulation model, to automatically extract the intra-community roads from building footprints. With these private roads integrated into the existing city roads, a simulated road network is constructed. Then, the Voronoi-based method and buffering method are used to assess accessibility to urban fire stations. Results reveal that the current layout of fire stations in the study area is not sufficient to achieve a complete coverage of potential demands in the stipulated time, and the traditional central areas enjoy better access to fire services compared to the periphery areas. The building-level analyses will not only enable decision-makers to strategically allocate new fire stations in the urban areas, but also help local authorities to enhance fire safety management within the community. The proposed method can also be applied to fine-scale analysis of neighborhood services’ accessibility in other cities.
      PubDate: 2022-05-24
       
  • Realizable accessibility: evaluating the reliability of public transit
           accessibility using high-resolution real-time data

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      Abstract: Abstract The widespread availability of high spatial and temporal resolution public transit data is improving the measurement and analysis of public transit-based accessibility to crucial community resources such as jobs and health care. A common approach is leveraging transit route and schedule data published by transit agencies. However, this often results in accessibility overestimations due to endemic delays due to traffic and incidents in bus systems. Retrospective real-time accessibility measures calculated using real-time bus location data attempt to reduce overestimation by capturing the actual performance of the transit system. These measures also overestimate accessibility since they assume that riders had perfect information on systems operations as they occurred. In this paper, we introduce realizable real-time accessibility based on space–time prisms as a more conservative and realistic measure. We, moreover, define accessibility unreliability to measure overestimation of schedule-based and retrospective accessibility measures. Using high-resolution General Transit Feed Specification real-time data, we conduct a case study in the Central Ohio Transit Authority bus system in Columbus, Ohio, USA. Our results prove that realizable accessibility is the most conservative of the three accessibility measures. We also explore the spatial and temporal patterns in the unreliability of both traditional measures. These patterns are consistent with prior findings of the spatial and temporal patterns of bus delays and risk of missing transfers. Realizable accessibility is a more practical, conservative, and robust measure to guide transit planning.
      PubDate: 2022-05-20
       
  • JGS Editors’ choice article

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      PubDate: 2022-04-26
       
  • A spatio-temporal autoregressive model for monitoring and predicting COVID
           infection rates

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      Abstract: Abstract The COVID-19 epidemic has raised major issues with regard to modelling and forecasting outcomes such as cases, deaths and hospitalisations. In particular, the forecasting of area-specific counts of infectious disease poses problems when counts are changing rapidly and there are infection hotspots, as in epidemic situations. Such forecasts are of central importance for prioritizing interventions or making severity designations for different areas. In this paper, we consider different specifications of autoregressive dependence in incidence counts as these may considerably impact on adaptivity in epidemic situations. In particular, we introduce parameters to allow temporal adaptivity in autoregressive dependence. A case study considers COVID-19 data for 144 English local authorities during the UK epidemic second wave in late 2020 and early 2021, which demonstrate geographical clustering in new cases—linked to the then emergent alpha variant. The model allows for both spatial and time variation in autoregressive effects. We assess sensitivity in short-term predictions and fit to specification (spatial vs space-time autoregression, linear vs log-linear, and form of space decay), and show improved one-step ahead and in-sample prediction using space-time autoregression including temporal adaptivity.
      PubDate: 2022-04-26
       
  • Detecting space–time patterns of disease risk under dynamic
           background population

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      Abstract: Abstract We are able to collect vast quantities of spatiotemporal data due to recent technological advances. Exploratory space–time data analysis approaches can facilitate the detection of patterns and formation of hypotheses about their driving processes. However, geographic patterns of social phenomena like crime or disease are driven by the underlying population. This research aims for incorporating temporal population dynamics into spatial analysis, a key omission of previous methods. As population data are becoming available at finer spatial and temporal granularity, we are increasingly able to capture the dynamic patterns of human activity. In this paper, we modify the space–time kernel density estimation method by accounting for spatially and temporally dynamic background populations (ST-DB), assess the benefits of considering the temporal dimension and finally, compare ST-DB to its purely spatial counterpart. We delineate clusters and compare them, as well as their significance, across multiple parameter configurations. We apply ST-DB to an outbreak of dengue fever in Cali, Colombia during 2010–2011. Our results show that incorporating the temporal dimension improves our ability to delineate significant clusters. This study addresses an urgent need in the spatiotemporal analysis literature by using population data at high spatial and temporal resolutions.
      PubDate: 2022-04-20
      DOI: 10.1007/s10109-022-00377-7
       
  • Embedding scale: new thinking of scale in machine learning and geographic
           representation

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      Abstract: Abstract Concepts of scale are at the heart of diverse scientific endeavors that seek to understand processes and how observations and analyses influence our understanding. While disciplinary discretions exist, researchers commonly devise spatial, temporal, and organizational scales in scoping phenomena of interest and determining measurements and representational frameworks in research design. The rise of the Fourth Paradigm for science drives data-intensive computing without preconceived notions regarding at what scale the phenomena or processes of interest operate, or at which level of details meaningful patterns may emerge. While scale is the a priori consideration for theory-driven research to seek ontological and relational affirmations, big data analytics and machine learning embed scale in algorithms and model outputs. In this paper, we examine embedded scale in data-driven machine learning research, connect the embedding scale to scale operating in the general theory of geographic representation in GIS and scaffold our arguments with a study of using machine learning to detect archeological features in drone-collected high-density images.
      PubDate: 2022-04-15
      DOI: 10.1007/s10109-022-00378-6
       
  • Urbanization and child growth failure in Sub-Saharan Africa: a
           geographical analysis

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      Abstract: Abstract This paper raises a fundamental question about Sub-Saharan Africa: has urbanization there been accompanied by improvements in personal wellbeing' It then proceeds to open an investigation focused on child health—in the form of child growth failure, including (i) stunting; (ii) wasting; and (iii) underweight—that addresses the question. The main contribution of the work is to reconcile an array of data, collected across different spatial scales and over different timeframes, in a manner that enables some preliminary insight into the relationships explored. Evidence derived from the analysis suggests that the wave of urbanization breaking across Sub-Saharan Africa is associated with improvements in wellbeing, a finding that is qualified by need for further research.
      PubDate: 2022-04-12
      DOI: 10.1007/s10109-022-00374-w
       
  • Simplifying the interpretation of continuous time models for
           spatio-temporal networks

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      Abstract: Abstract Autoregressive and moving average models for temporally dynamic networks treat time as a series of discrete steps which assumes even intervals between data measurements and can introduce bias if this assumption is not met. Using real and simulated data from the London Underground network, this paper illustrates the use of continuous time multilevel models to capture temporal trajectories of edge properties without the need for simultaneous measurements, along with two methods for producing interpretable summaries of model results. These including extracting ‘features’ of temporal patterns (e.g. maxima, time of maxima) which have utility in understanding the network properties of each connection and summarising whole-network properties as a continuous function of time which allows estimation of network properties at any time without temporal aggregation of non-simultaneous measurements. Results for temporal pattern features in the response variable were captured with reasonable accuracy. Variation in the temporal pattern features for the exposure variable was underestimated by the models. The models showed some lack of precision. Both model summaries provided clear ‘real-world’ interpretations and could be applied to data from a range of spatio-temporal network structures (e.g. rivers, social networks). These models should be tested more extensively in a range of scenarios, with potential improvements such as random effects in the exposure variable dimension.
      PubDate: 2022-04-01
       
  • Generating pseudo-absence samples of invasive species based on outlier
           detection in the geographical characteristic space

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      Abstract: Abstract Obtaining the diversity samples of invasive alien species (species presence and absence samples) is vital for species distribution models. However, because of the enhanced focus on collecting presence samples, most datasets regarding invasive species lack explicit absence samples. Thus, the generation of effective pseudo-absence samples of invasive species is a critical issue for building species distribution models. This paper proposes a pseudo-absence sampling approach based on outlier detection in the geographical characteristic space. First, principal component analysis is used to model the linear correlation of the original variables, and a statistical index is built to determine the weight of the principal components. Next, in the geographical characteristic space built based on the principal components and their corresponding weights, the local outlier factor is obtained to identify the pseudo-absence samples. The dataset regarding the invasive species Erigeron annuus in the Yangtze River Economic Belt is used to illustrate the general process of the proposed approach. The prediction results from logistical regression with the proposed approach are better than these with the spatial random sampling, surface range envelope, and one-class support vector machine models. These findings validate the effectiveness of the proposed sampling approach.
      PubDate: 2022-04-01
       
  • Spatiotemporal high-resolution prediction and mapping: methodology and
           application to dengue disease

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      Abstract: Abstract Dengue disease has become a major public health problem. Accurate and precise identification, prediction and mapping of high-risk areas are crucial elements of an effective and efficient early warning system in countering the spread of dengue disease. In this paper, we present the fusion area-cell spatiotemporal generalized geoadditive-Gaussian Markov random field (FGG-GMRF) framework for joint estimation of an area-cell model, involving temporally varying coefficients, spatially and temporally structured and unstructured random effects, and spatiotemporal interaction of the random effects. The spatiotemporal Gaussian field is applied to determine the unobserved relative risk at cell level. It is transformed to a Gaussian Markov random field using the finite element method and the linear stochastic partial differential equation approach to solve the “big n” problem. Sub-area relative risk estimates are obtained as block averages of the cell outcomes within each sub-area boundary. The FGG-GMRF model is estimated by applying Bayesian Integrated Nested Laplace Approximation. In the application to Bandung city, Indonesia, we combine low-resolution area level (district) spatiotemporal data on population at risk and incidence and high-resolution cell level data on weather variables to obtain predictions of relative risk at subdistrict level. The predicted dengue relative risk at subdistrict level suggests significant fine-scale heterogeneities which are not apparent when examining the area level. The relative risk varies considerably across subdistricts and time, with the latter showing an increase in the period January–July and a decrease in the period August–December.
      PubDate: 2022-02-19
      DOI: 10.1007/s10109-021-00368-0
       
  • Chinese toponym recognition with variant neural structures from social
           media messages based on BERT methods

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      Abstract: Abstract Many natural language tasks related to geographic information retrieval (GIR) require toponym recognition, and identifying Chinese toponyms from social media messages to share real-time information is a critical problem for many practical applications, such as natural disaster response and geolocating. In this article, we focused on toponym recognition from social media messages in Chinese. While existing off-the-shelf Chinese named entity recognition (NER) tools could be applied to identify toponyms, these approaches cannot address a variety of language irregularities taken from social media messages, including location name abbreviations, informal sentence structures and combination toponyms. We present a deep neural network named BERT-BiLSTM-CRF, which extends a basic bidirectional recurrent neural network model (BiLSTM) with the pretraining bidirectional encoder representation from transformers (BERT) representation to handle the toponym recognition task in Chinese text. Using three datasets taken from lists of alternative location names, the experimental results showed that the proposed model can significantly outperform previous Chinese NER models/algorithms and a set of state-of-the-art deep learning models.
      PubDate: 2022-02-18
      DOI: 10.1007/s10109-022-00375-9
       
  • A method for evaluating the degree of spatial and temporal avoidance in
           spatial point patterns

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      Abstract: Abstract This paper develops a new method for evaluating the degree of spatial and temporal avoidance in spatial point patterns. We consider point patterns that change over time, where points represent spatial objects that appear at certain locations, stay there for certain periods, and may finally disappear, such as buildings in cities, plants in fields, and birds' nests in forests. Spatial avoidance in this paper refers to the phenomenon that points appear in sparse spaces while points disappear in dense spaces. Spatial avoidance often leads to dispersed point patterns, which are observed in the distributions of drug stores, gas stations, and animal burrows. Temporal avoidance refers to the phenomenon that close points avoid the overlap of their lifetime. Temporal avoidance is found in the relationships between preys and predators, animal species that share the same water resources, and restaurants in shopping malls. The paper develops four measures to evaluate the spatial and temporal patterns of avoidance. Two measures consider the avoidance from a spatial perspective, while the other two focus on the temporal aspect of avoidance. To test the validity of the proposed method, this paper applies it to the analysis of the convenience stores in Shibuya-ku, Tokyo. The results indicated the proposed method's effectiveness and revealed the spatial and temporal patterns of avoidance of convenience stores that existing methods cannot detect.
      PubDate: 2022-02-12
      DOI: 10.1007/s10109-022-00373-x
       
  • Incorporating neighborhood scale effects into land loss modeling using
           semivariograms

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      Abstract: Abstract Scale effects are pervasive in geospatial modeling and affect the reliability of analysis results. This paper examines the neighborhood scale effects on the performance of land loss models in coastal Louisiana where Lower Mississippi River Basin is located. The study incorporates both natural and human variables and their corresponding neighborhood scale variables into land loss modeling. Semivariogram analysis was used to determine each explanatory variable’s neighborhood size at which the semivariance between sample points begins to level off. A new ‘neighborhood’ variable for each of those variables detected with a neighborhood size was created using the focal statistics to represent the neighborhood scale effects. Two land loss stepwise regression models, one without neighborhood variables and the other with neighborhood variables, were developed to test if incorporating neighborhood scale effects could improve the land loss model performance. Results show that the model’s overall accuracy improved significantly from 65.43 to 74.43% after including the neighborhood variables. Six neighborhood variables, in addition to 14 original variables, were selected as significant predictors of land loss probability. The six neighborhood variables include distance to the coastline, land fragmentation, oil and gas well density, percent of water area, pipeline density, and percent of the vacant house. The analysis shows that including variables representing the scale effects are critical for better performance in land loss modeling. Study findings add new insights into the complex land loss mechanism and help derive more accurate land loss predictions to inform coastal restoration and management decision-making.
      PubDate: 2022-02-09
      DOI: 10.1007/s10109-021-00372-4
       
  • Understanding the impact of temporal scale on human movement analytics

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      Abstract: Abstract Movement is manifested through a series of patterns at multiple spatial and temporal scales. Movement data today are becoming available at increasingly fine-grained temporal granularity. These observations often represent multiple behavioral modes and complex patterns along the movement path. However, the relationships between the observation scale of movement data and the analysis scales at which movement patterns are captured remain understudied. This article aims at investigating the role of temporal scale in movement data analytics. It takes up an important question of “how do decisions surrounding the scale of movement data and analyses impact our inferences about movement patterns'” Through a set of computational experiments in the context of human movement, we take a systematic look at the impact of varying temporal scales on common movement analytics techniques including trajectory analytics to calculate movement parameters (e.g., speed, path tortuosity), estimation of individual space usage, and interactions analysis to detect potential contacts between multiple mobile individuals.
      PubDate: 2022-02-08
      DOI: 10.1007/s10109-021-00370-6
       
  • Scale and local modeling: new perspectives on the modifiable areal unit
           problem and Simpson’s paradox

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      Abstract: Abstract The concept of ‘spatial scale’, or simply ‘scale’ is implicit in any discussion of global versus local models. The raison d’etre of local models is that a global scale (where here ‘global’ simply refers to all locations within a predefined area of interest) might be the incorrect scale at which to undertake any analysis of spatial processes; the alternative being a local scale (where here ‘local’ refers to individual locations). Here we explore two well-known scale issues in the context of local modeling: the modifiable areal unit problem (MAUP) and Simpson’s paradox. In doing so, we highlight that scale effects play two very different roles in any consideration of local versus global modeling. First, we examine the sensitivity of global and local models to the MAUP and show how the effects of the MAUP in global models are a function of the degree to which processes vary over space. This generates a new insight into the MAUP: it results from the properties of processes rather than the properties of data. Then we highlight the extreme differences that can result when calibrating global and local models and how Simpson’s paradox can arise in this context. In the examination of the MAUP, scale is treated as a measure of the degree to which data are aggregated prior to any form of modeling; in the study of Simpson’s paradox, scale refers to the geographical entity for which a model is calibrated.
      PubDate: 2022-01-24
      DOI: 10.1007/s10109-021-00371-5
       
  • Enhancing strategic defensive positioning and performance in the outfield

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      Abstract: Abstract Over the past 20 years, professional and collegiate baseball has undergone a transformation, with statistics and analytics increasingly factoring into most of the decisions being made on the field. One particular example of the increased role of analytics is in the positioning of outfielders, who are tasked with tracking down balls hit to the outfield to record outs and minimize potential offensive damage. This paper explores the potential of location analytics to enhance the strategic positioning of players, enabling improved response and performance. We implement a location optimization model to analyze collegiate ball-tracking data, seeking outfielder locations that simultaneously minimize the average distance to a batted ball and maximize the weighted importance of batted ball coverage within a response standard. Trade-off outfielder configurations are compared to observed fielder positioning, finding that location models and spatial optimization can lead to performance improvements ranging from 1 to 3%, offering a significant strategic advantage over the course of a season.
      PubDate: 2022-01-10
      DOI: 10.1007/s10109-021-00367-1
       
  • Measuring urban sentiments from social media data: a dual-polarity metric
           approach

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      Abstract: Abstract Urban sentiment, as people’ perception of city environment and events, is a direct indicator of the quality of life of residents and the unique identity of a city. Social media by which people express opinions directly provides a way to measure urban sentiment. However, it is challenging to depict collective sentiments when integrating the posts inside a particular place, because the sentiment polarities will eventually be neutralized and consequently result in misinterpretation. It is necessary to capture positive and negative emotions distinguishingly rather than integrating them indiscriminately. Following the psychological hypothesis that two polar emotions are processed in parallel and can coexist independently, a novel dual-polarity metric is proposed in this paper to simultaneously evaluate collective positive and negative sentiments in geotagged social media in a place. This new measurement overcomes the integration problem in traditional methods, and therefore can better capture collective urban sentiments and diverse perceptions of places. In a case study of Beijing, China, urban sentiments are extracted using this approach from massive geotagged posts on Sina Weibo, a Twitter-like social media platform in China, and then their spatial distribution and temporal rhythm are revealed. Positive sentiments are more spatially heterogeneous than negative sentiments. Positive sentiments are concentrated in scenic spots, commercial and cultural areas, while negative sentiments are mostly around transportation hubs, hospitals and colleges. Following the principle of sense of place, multi-source data are integrated to evaluate the effects of influencing factors. The variation of spatial factors aggravates the heterogeneity of urban sentiment. The discovered spatiotemporal patterns give an insight into the urban sentiment through online behaviors and can help to improve city functionality and sustainability.
      PubDate: 2022-01-05
      DOI: 10.1007/s10109-021-00369-z
       
 
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