Path segmentation for movement trajectories with irregular sampling frequency using space‐time interpolation and density‐based spatial clustering

Ying Song; Tianci Song; Rui Kuang

doi:10.1111/tgis.12549

Spatial clustering and space–time clusters of leukemia among children in Germany, 1987–2007

European Journal of Epidemiology ◽

10.1007/s10654-010-9488-7 ◽

2010 ◽

Vol 25 (9) ◽

pp. 627-633 ◽

Cited By ~ 16

Author(s):

Sven Schmiedel ◽

Maria Blettner ◽

Peter Kaatsch ◽

Joachim Schüz

Keyword(s):

Spatial Clustering ◽

Space Time

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Indoor air quality assessment in a residential apartment in Budapest

10.5194/egusphere-egu21-5583 ◽

2021 ◽

Author(s):

Bushra Atfeh ◽

Erzsébet Kristóf ◽

Róbert Mészáros ◽

Zoltán Barcza

Keyword(s):

Air Quality ◽

Data Processing ◽

Indoor Air Quality ◽

Indoor Air ◽

Low Cost ◽

Carbon Dioxide Concentration ◽

Monitoring Network ◽

Sampling Frequency ◽

Irregular Sampling ◽

Air Quality Measurements

This work focuses on indoor air quality measurements carried out in an apartment in the suburban region of Budapest. The measurements were made by an IQAir AirVisual node air quality monitor which is a so-called low-cost sensor capable to monitor PM2.5 and carbon dioxide concentration. In this study we analyze data measured during January 2017 that was characterized by an extreme air pollution episode in Budapest. The aim of the study was to calculate daily indoor PM2.5 concentrations that are comparable with the outdoor concentrations provided by the official Hungarian Air Quality Monitoring Network. Given the fact that AirVisual Pro provides data with irregular sampling frequency, data processing is expected to influence the calculated daily mean concentrations.&#160; The results indicated that the uneven sampling frequency characteristic of AirVisual node indeed causes problems during data processing and has an effect on the calculated means. We propose a &#8216;best method&#8217; for data processing for sensors with irregular sampling frequency.

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An Analysis of Movement by Visualizing Spatio-Temporal Characteristics of Logdata

Proceedings of the ICA ◽

10.5194/ica-proc-2-18-2019 ◽

2019 ◽

Vol 2 ◽

pp. 1-3

Author(s):

Nahye Cho ◽

Youngok Kang

Keyword(s):

Spatial Clustering ◽

Space Time ◽

Spatiotemporal Data ◽

Measuring Time ◽

Log Data ◽

Temporal Characteristics ◽

Time Path ◽

Density Based Clustering ◽

Spatio Temporal

Abstract. In this study, we visualized and analyzed log data in order to analyze the spatiotemporal characteristics of “moving” and “staying activities”. As a case study, we collected and preprocessed GPS log data generated by students participating in field activities. STP (Space-Time Path) was used to visualize movement logs. “Movement” and staying places were distinguished through density-based clustering, and the time “stayed” and activities performed at staying places were examined. The problem of over-measuring time at some staying places was examined. To resolve this, the 3D Density-Based Spatial Clustering of Application with Noise (DBSCAN) was used to more accurately measure the time spent at staying places. We propose 3D DBSCAN as methodology to accurately measure spatiotemporal data. We believe this method will remain effective even as this data becomes more numerous.

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Understanding Adverse Population Sentiment Towards the Spread of COVID-19 in the United States

10.1101/2021.07.15.21260543 ◽

2021 ◽

Author(s):

Alexander Hohl ◽

Moongi Choi ◽

Richard Medina ◽

Neng Wan ◽

Ming Wen

Keyword(s):

United States ◽

Spatial Clustering ◽

Initial Period ◽

Disease Outbreaks ◽

The United States ◽

Space Time ◽

Scan Statistic ◽

Housing Type ◽

Containment Measures ◽

Time Scan

Background - During the ongoing COVID-19 pandemic, the immediate threat of illness and mortality is not the only concern. In the United States, COVID-19 is not only causing physical suffering to patients, but also great levels of adverse sentiment (e.g., fear, panic, anxiety) among the public. Such secondary threats can be anticipated and explained through sentiment analysis of social media, such as Twitter. Methods - We obtained a dataset of geotagged tweets on the topic of COVID-19 in the contiguous United States during the period of 11/1/2019 - 9/15/2020. We classified each tweet into "adverse" and "non-adverse" using the NRC Emotion Lexicon and tallied up the counts for each category per county per day. We utilized the space-time scan statistic to find clusters and a three-stage regression approach to identify socioeconomic and demographic correlates of adverse sentiment. Results - We identified substantial spatiotemporal variation in adverse sentiment in our study area/period. After an initial period of low-level adverse sentiment (11/1/2019 - 1/15/2020), we observed a steep increase and subsequent fluctuation at a higher level (1/16/2020 - 9/15/2020). The number of daily tweets was low initially (11/1/2019 - 1/22/2020), followed by spikes and subsequent decreases until the end of the study period. The space-time scan statistic identified 12 clusters of adverse sentiment of varying size, location, and strength. Clusters were generally active during the time period of late March to May/June 2020. Increased adverse sentiment was associated with decreased racial/ethnic heterogeneity, decreased rurality, higher vulnerability in terms of minority status and language, and housing type and transportation. Conclusions - We utilized a dataset of geotagged tweets to identify the spatiotemporal patterns and the spatial correlates of adverse population sentiment during the first two waves of the COVID-19 pandemic in the United States. The characteristics of areas with high adverse sentiment may be relevant for communication of containment measures. The combination of spatial clustering and regression can be beneficial for understanding of the ramifications of COVID-19, as well as disease outbreaks in general.

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Detecting Malaria Hotspots in Haiti, a Low-Transmission Setting

American Journal of Tropical Medicine and Hygiene ◽

10.4269/ajtmh.20-0465 ◽

2021 ◽

Author(s):

Amber M. Dismer ◽

Jean Frantz Lemoine ◽

Mérilien Jean Baptiste ◽

Kimberly E. Mace ◽

Daniel Impoinvil ◽

...

Keyword(s):

Malaria Transmission ◽

Health Facility ◽

Spatial Clustering ◽

Control Program ◽

Parasite Prevalence ◽

Population Based ◽

Incidence Rates ◽

Space Time ◽

Health Facilities ◽

Low Transmission

In 2006, Haiti committed to malaria elimination when the transmission was thought to be low, but before robust national parasite prevalence estimates were available. In 2011, the first national population-based survey confirmed the national malaria parasite prevalence was < 1%. In both 2014 and 2015, Haiti reported approximately 17,000 malaria cases identified passively at health facilities. To detect malaria transmission hotspots for targeting interventions, the National Malaria Control Program (NMCP) piloted an enhanced geographic information surveillance system in three departments with relatively high-, medium-, and low-transmission areas. From October 2014–September 2015, NMCP staff abstracted health facility records of confirmed malaria cases from 59 health facilities and geo-located patients’ households. Household locations were aggregated to 1-km2 grid cells to calculate cumulative incidence rates (CIRs) per 1,000 persons. Spatial clustering of CIRs were tested using Getis-Ord Gi* analysis. Space–time permutation models searched for clusters up to 6 km in distance using a 1-month malaria transmission window. Of the 2,462 confirmed cases identified from health facility records, 58% were geo-located. Getis-Ord Gi* analysis identified 43 1-km2 hotspots in coastal and inland areas that overlapped primarily with 13 space–time clusters (size: 0.26–2.97 km). This pilot describes the feasibility of detecting malaria hotspots in resource-poor settings. More data from multiple years and serological household surveys are needed to assess completeness and hotspot stability. The NMCP can use these pilot methods and results to target foci investigations and malaria interventions more accurately.

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Flying In-formation: A computational method for the classification of host seeking mosquito flight patterns using path segmentation and unsupervised machine learning

10.1101/2021.11.24.469809 ◽

2021 ◽

Author(s):

Mark Fowler ◽

Anthony J Abbott ◽

Gregory PD Murray ◽

Philip J McCall

Keyword(s):

Machine Learning ◽

Rational Design ◽

Computational Method ◽

New Method ◽

Detailed Knowledge ◽

Flight Behaviour ◽

Unsupervised Machine Learning ◽

Movement Trajectories ◽

Vector Behaviour ◽

Path Segmentation

The rational design of effective vector control tools requires detailed knowledge of vector behaviour. Yet, behavioural observations, interpretations, evaluations and definitions by even the most experienced researcher are constrained by subjectivity and perceptual limits. Seeking an objective alternative to ‘expertise’, we developed and tested an unsupervised method for the automatic identification of videotracked mosquito flight behaviour. This method unites path-segmentation and unsupervised machine learning in an innovative workflow and is implemented using a combination of R and python. The workflow (1) records movement trajectories; (2) applies path-segmentation; (3) clusters path segments using unsupervised learning; and (4) interprets results. Analysis of the flight patterns of An. gambiae s.s., responding to human-baited insecticide-treated bednets (ITNs), by the new method identified four distinct behaviour modes: with ‘swooping’ and ‘approaching’ modes predominant at ITNs; increased ‘walking’ behaviours at untreated nets; similar rates of 'reacting' at both nets; and higher overall activity at treated nets. The method’s validity was tested by comparing these findings with those from a similar setting using an expertise-based method. The level of correspondence found between the studies validated the accuracy of the new method. While researcher-defined behaviours are inherently subjective, and prone to corollary shortcomings, the new approach’s mathematical method is objective, automatic, repeatable and a validated alternative for analysing complex vector behaviour. This method provides a novel and adaptable analytical tool and is freely available to vector biologists, ethologists and behavioural ecologists.

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Going Beyond 3-D Imaging: Automated 3-D Montaged image Analysis of Cytological Specimens

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100163873 ◽

1996 ◽

Vol 54 ◽

pp. 282-283

Author(s):

Badrinath Roysam ◽

Hakan Ancin ◽

Douglas E. Becker ◽

Robert W. Mackin ◽

Matthew M. Chestnut ◽

...

Keyword(s):

Image Analysis ◽

Structural Changes ◽

Sampling Methods ◽

Spatial Clustering ◽

Three Dimensional ◽

Field Of View ◽

Cell Counting ◽

Depth Of Field ◽

Tissue Cell ◽

Tissue Organization

This paper summarizes recent advances made by this group in the automated three-dimensional (3-D) image analysis of cytological specimens that are much thicker than the depth of field, and much wider than the field of view of the microscope. The imaging of thick samples is motivated by the need to sample large volumes of tissue rapidly, make more accurate measurements than possible with 2-D sampling, and also to perform analysis in a manner that preserves the relative locations and 3-D structures of the cells. The motivation to study specimens much wider than the field of view arises when measurements and insights at the tissue, rather than the cell level are needed.The term “analysis” indicates a activities ranging from cell counting, neuron tracing, cell morphometry, measurement of tracers, through characterization of large populations of cells with regard to higher-level tissue organization by detecting patterns such as 3-D spatial clustering, the presence of subpopulations, and their relationships to each other. Of even more interest are changes in these parameters as a function of development, and as a reaction to external stimuli. There is a widespread need to measure structural changes in tissue caused by toxins, physiologic states, biochemicals, aging, development, and electrochemical or physical stimuli. These agents could affect the number of cells per unit volume of tissue, cell volume and shape, and cause structural changes in individual cells, inter-connections, or subtle changes in higher-level tissue architecture. It is important to process large intact volumes of tissue to achieve adequate sampling and sensitivity to subtle changes. It is desirable to perform such studies rapidly, with utmost automation, and at minimal cost. Automated 3-D image analysis methods offer unique advantages and opportunities, without making simplifying assumptions of tissue uniformity, unlike random sampling methods such as stereology.12 Although stereological methods are known to be statistically unbiased, they may not be statistically efficient. Another disadvantage of sampling methods is the lack of full visual confirmation - an attractive feature of image analysis based methods.

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