Analysis of ‘Point Time Count’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/0b27e0a2-0790-442f-992b-6dc81613088c on 11 February 2022.

--- Dataset description provided by original source is as follows ---

Point in Time Count Numbers for 2007 to 2018 from HUD, which counts the number of people experiencing homelessness at the federal, state, and local level.

https://www.hudexchange.info/resource/5783/2018-ahar-part-1-pit-estimates-of-homelessness-in-the-us/

--- Original source retains full ownership of the source dataset ---

Access historical and point-in-time financial statements, ratios, multiples, and press releases, with LSEG's S&P Compustat Database.

Long-term historical (derived from GHCN) and future simulated (derived from BCCA) time series analyses for several meteorological variables are provided to several clients within the Northeast Climate Adaptation Science Center (NE CASC) footprint as background of the state of changes in their local climate. Variables include average annual and seasonal temperature and precipitation, extreme temperature and precipitation, wind, and snow depth. Precipitation includes both rain and snow.

This lesson was adapted from educational material written by Dr. Kateri Salk for her Fall 2019 Hydrologic Data Analysis course at Duke University. This is the first part of a two-part exercise focusing on time series analysis.

Introduction

Time series are a special class of dataset, where a response variable is tracked over time. The frequency of measurement and the timespan of the dataset can vary widely. At its most simple, a time series model includes an explanatory time component and a response variable. Mixed models can include additional explanatory variables (check out the nlme and lme4 R packages). We will be covering a few simple applications of time series analysis in these lessons.

Opportunities

Analysis of time series presents several opportunities. In aquatic sciences, some of the most common questions we can answer with time series modeling are:

• Has there been an increasing or decreasing trend in the response variable over time?

• Can we forecast conditions in the future?

  Challenges

Time series datasets come with several caveats, which need to be addressed in order to effectively model the system. A few common challenges that arise (and can occur together within a single dataset) are:

• Autocorrelation: Data points are not independent from one another (i.e., the measurement at a given time point is dependent on previous time point(s)).

• Data gaps: Data are not collected at regular intervals, necessitating interpolation between measurements. There are often gaps between monitoring periods. For many time series analyses, we need equally spaced points.

• Seasonality: Cyclic patterns in variables occur at regular intervals, impeding clear interpretation of a monotonic (unidirectional) trend. Ex. We can assume that summer temperatures are higher.

• Heteroscedasticity: The variance of the time series is not constant over time.

• Covariance: the covariance of the time series is not constant over time. Many of these models assume that the variance and covariance are similar over the time-->heteroschedasticity.

  Learning Objectives

After successfully completing this notebook, you will be able to:

1. Choose appropriate time series analyses for trend detection and forecasting

2. Discuss the influence of seasonality on time series analysis

3. Interpret and communicate results of time series analyses

Political relationships often vary over time, but standard models ignore temporal variation in regression relationships. We describe a Bayesian model that treats the change point in a time series as a parameter to be estimated. In this model, inference for the regression coefficients reflects prior uncertainty about the location of the change point. Inferences about regression coefficients, unconditional on the change-point location, can be obtained by simulation methods. The model is illustrated in an analysis of real wage growth in 18 OECD countries from 1965–1992.

The MAPS Model Location Time Series (MOLTS) is one of the model output datasets provided in the Southern Great Plains - 1997 (SGP97). The full MAPS MOLTS dataset covers most of North America east of the Rocky Mountains (283 locations). MOLTS are hourly time series output at selected locations that contain values for various surface parameters and ‘sounding' profiles at MAPS model levels and are derived from the MAPS model output. The MOLTS output files were converted into Joint Office for Science Support (JOSS) Quality Control Format (QCF), the same format used for atmospheric rawinsonde soundings processed by JOSS. The MOLTS output provided by JOSS online includes only the initial analysis output (i.e. no forecast MOLTS) and only state parameters (pressure, altitude, temperature, humidity, and wind). The full output, including the forecast MOLTS and all output parameters, in its original format (Binary Universal Form for the Representation of meteorological data, or BUFR) is available from the National Center for Atmospheric Research (NCAR)/Scientific Computing Division. The Forecast Systems Laboratory (FSL) operates the MAPS model with a resolution of 40 km and 40 vertical levels. The MAPS analysis and forecast fields are generated every 3 hours at 0000, 0300, 0600, 0900, 1200, 1500, 1800, and 2100 UTC daily. MOLTS are hourly vertical profile and surface time series derived from the MAPS model output. The complete MOLTS output includes six informational items, 16 parameters for each level and 27 parameters at the surface. Output are available each hour beginning at the initial analysis (the only output available from JOSS) and ending at the 48 hour forecast. JOSS converts the raw format files into JOSS QCF format which is the same format used for atmospheric sounding data such as National Weather Service (NWS) soundings. JOSS calculated the total wind speed and direction from the u and v wind components. JOSS calculated the mixing ratio from the specific humidity (Pruppacher and Klett 1980) and the dew point from the mixing ratio (Wallace and Hobbs 1977). Then the relative humidity was calculated from the dew point (Bolton 1980). JOSS did not conduct any quality control on this output. The header records (15 total records) contain output type, project ID, the location of the nearest station to the MOLTS location (this can be a rawinsonde station, an Atmospheric Radiation Measurement (ARM)/Cloud and Radiation Testbed (CART) station, a wind profiler station, a surface station, or just the nearest town), the location of the MOLTS output, and the valid time for the MOLTS output. The five header lines contain information identifying the sounding, and have a rigidly defined form. The following 6 header lines are used for auxiliary information and comments about the sounding, and they vary significantly from dataset to dataset. The last 3 header records contain header information for the data columns. Line 13 holds the field names, line 14 the field units, and line 15 contains dashes ('-' characters) delineating the extent of the field. Resources in this dataset:Resource Title: GeoData catalog record. File Name: Web Page, url: https://geodata.nal.usda.gov/geonetwork/srv/eng/catalog.search#/metadata/2ad09880-6439-440c-9829-c4653ec12a4f

Geostatistics analyzes and predicts the values associated with spatial or spatial-temporal phenomena. It incorporates the spatial (and in some cases temporal) coordinates of the data within the analyses. It is a practical means of describing spatial patterns and interpolating values for locations where samples were not taken (and measures the uncertainty of those values, which is critical to informed decision making). This archive contains results of geostatistical analysis of COVID-19 case counts for all available US counties. Test results were obtained with ArcGIS Pro (ESRI). Sources are state health departments, which are scraped and aggregated by the Johns Hopkins Coronavirus Resource Center and then pre-processed by MappingSupport.com.

This update of the Zenodo dataset (version 6) consists of three compressed archives containing geostatistical analyses of SARS-CoV-2 testing data. This dataset utilizes many of the geostatistical techniques used in previous versions of this Zenodo archive, but has been significantly expanded to include analyses of up-to-date U.S. COVID-19 case data (from March 24th to September 8^th, 2020):

Archive #1: “1.Geostat. Space-Time analysis of SARS-CoV-2 in the US (Mar24-Sept6).zip” – results of a geostatistical analysis of COVID-19 cases incorporating spatially-weighted hotspots that are conserved over one-week timespans. Results are reported starting from when U.S. COVID-19 case data first became available (March 24^th, 2020) for 25 consecutive 1-week intervals (March 24th through to September 6th, 2020). Hotspots, where found, are reported in each individual state, rather than the entire continental United States.

Archive #2: "2.Geostat. Spatial analysis of SARS-CoV-2 in the US (Mar24-Sept8).zip" – the results from geostatistical spatial analyses only of corrected COVID-19 case data for the continental United States, spanning the period from March 24^th through September 8th, 2020. The geostatistical techniques utilized in this archive includes ‘Hot Spot’ analysis and ‘Cluster and Outlier’ analysis.

Archive #3: "3.Kriging and Densification of SARS-CoV-2 in LA and MA.zip" – this dataset provides preliminary kriging and densification analysis of COVID-19 case data for certain dates within the U.S. states of Louisiana and Massachusetts.

These archives consist of map files (as both static images and as animations) and data files (including text files which contain the underlying data of said map files [where applicable]) which were generated when performing the following Geostatistical analyses: Hot Spot analysis (Getis-Ord Gi*) [‘Archive #1’: consecutive weeklong Space-Time Hot Spot analysis; ‘Archive #2’: daily Hot Spot Analysis], Cluster and Outlier analysis (Anselin Local Moran's I) [‘Archive #2’], Spatial Autocorrelation (Global Moran's I) [‘Archive #2’], and point-to-point comparisons with Kriging and Densification analysis [‘Archive #3’].

The Word document provided ("Description-of-Archive.Updated-Geostatistical-Analysis-of-SARS-CoV-2 (version 6).docx") details the contents of each file and folder within these three archives and gives general interpretations of these results.

A common feature of preclinical animal experiments is repeated measurement of the outcome, e.g., body weight measured in mice pups weekly for 20 weeks. Separate time point analysis or repeated measures analysis approaches can be used to analyze such data. Each approach requires assumptions about the underlying data and violations of these assumptions have implications for estimation of precision, and type I and type II error rates. Given the ethical responsibilities to maximize valid results obtained from animals used in research, our objective was to evaluate approaches to reporting repeated measures design used by investigators and to assess how assumptions about variation in the outcome over time impact type I and II error rates and precision of estimates. We assessed the reporting of repeated measures designs of 58 studies in preclinical animal experiments. We used simulation modelling to evaluate three approaches to statistical analysis of repeated measurement data. In particular, we assessed the impact of (a) repeated measure analysis assuming that the outcome had non-constant variation at all time points (heterogeneous variance) (b) repeated measure analysis assuming constant variation in the outcome (homogeneous variance), (c) separate ANOVA at individual time point in repeated measures designs. The evaluation of the three model fitting was based on comparing the p-values distributions, the type I and type II error rates and by implication, the shrinkage or inflation of standard error estimates from 1000 simulated dataset. Of 58 studies with repeated measures design, three provided a rationale for repeated measurement and 23 studies reported using a repeated-measures analysis approach. Of the 35 studies that did not use repeated-measures analysis, fourteen studies used only two time points to calculate weight change which potentially means collected data was not fully utilized. Other studies reported only select time points (n = 12) raising the issue of selective reporting. Simulation studies showed that an incorrect assumption about the variance structure resulted in modified error rates and precision estimates. The reporting of the validity of assumptions for repeated measurement data is very poor. The homogeneous variation assumption, which is often invalid for body weight measurements, should be confirmed prior to conducting the repeated-measures analysis using homogeneous covariance structure and adjusting the analysis using corrections or model specifications if this is not met.

We performed replicated, repeated-measures data of height, diameter and vitality at tree level to allow analysis of the spatial and temporal structure and diversity of a semi-natural mixed floodplain forest in Italy. Three inventories were performed in 1995, 2005 and 2016 in three ~1 ha plots with varying soil moisture regimes. The use of replicated, repeated-measures data rather than chronosequences allows the examination of true changes in spatial pattern processes through time in this forest type.

The global fixed-point time-lapse camera market is projected to reach USD 2.9 billion by 2033, exhibiting a CAGR of 12.3% during the forecast period (2023-2033). The market growth is attributed to the increasing adoption of time-lapse photography in various industries, including construction, mining, and infrastructure development. Governments and construction companies are leveraging time-lapse cameras to monitor project progress, create visual documentation, and conduct virtual site tours, leading to significant market demand. North America and Europe dominate the fixed-point time-lapse camera market, where construction and infrastructure activities are robust. The Asia Pacific region is anticipated to witness substantial growth due to rapid urbanization, increasing infrastructure development, and growing investments in smart city initiatives. Key market players such as ATLI Timelapse, OxBlue, TrueLook, EarthCam, and IBEAM Systems are continuously innovating and introducing advanced time-lapse camera systems with enhanced features, contributing to the market's growth trajectory.

RTEM Hackathon Tutorials

This data set and associated notebooks are meant to give you a head start in accessing the RTEM Hackathon by showing some examples of data extraction, processing, cleaning, and visualisation. Data availabe in this Kaggle page is only a selected part of the whole data set extracted for the tutorials. A series of Video Tutorials are associated with this dataset and notebooks and is found on the Onboard YouTube channel.

Part 1 - Onboard API and Onboard API Wrapper Introduction

An introduction to the API usage and how to retrieve data from it. This notebook is outlined in several YouTube videos that discuss: - how to get started with your account and get oriented to the Kaggle environment, - get acquainted with the Onboard API, - and start using the Onboard API wrapper to extract and explore data.

Part 2 - Meta-data and Point Exploration Demo

How to query data points meta-data, process them and visually explore them. This notebook is outlined in several YouTube videos that discuss: - how to get started exploring building metadata/points, - select/merge point lists and export as CSV - and visualize and explore the point lists

Part 3 - Time-series Data Extraction and Exploration Demo

How to query time-series from data points, process and visually explore them. This notebook is outlined in several YouTube videos that discuss: - how to load and filter time-series data from sensors - resample and transform time-series data - and create heat maps and boxplots of data for exploration

Part 4 - Example of starting point for analysis for RTEM and possible directions of analysis

A quick example of a starting point towards the analysis of the data for some sort of solution and reference to a paper that might help get an overview of the possible directions your team can go in. This notebook is outlined in several YouTube videos that discuss: - overview of use cases and judging criteria - an example of a real-world hypothesis - further development of that simple example

More information about the data and competition can be found on the RTEM Hackathon website.

Fitness landscape analysis can provide valuable insight about key characteristics of a problem. Many real-world problems have expensive-to-compute fitness functions and are multi-objective in nature. Surrogate-assisted evolutionary algorithms are often used to tackle such problems. Despite this, literature about analysing the fitness landscapes induced by surrogate models is limited, and even non-existent for multi-objective problems. This study addresses this critical gap by comparing landscapes of the real fitness function with those of surrogate models for multi-objective functions. Moreover, it does so temporally by examining landscape features at different points in time during optimisation, in the vicinity of the population at that point in time. We consider the well-known BBOB bi-objective benchmark functions in our experiments and employ a reference-vector guided surrogate-assisted evolutionary algorithm. The results of the landscape analysis on the real fitness landscape reveal significant distinctions between features at different time points during optimisation, and between real and surrogate landscape features. Furthermore, the study demonstrates that both surrogate and real landscape features are of importance when predicting algorithm performance, and that the outcome of an algorithm can be forecast to a decent standard by sampling these during evolution. The results could help to facilitate the design of surrogate switching approaches to improve performance in multi-objective optimisation.

This dataset contains data for static fitness landscape analyses as well as temporal analyses, focusing on five different surrogates: IDW, IDWR, KNN, LR-KNN, and No-Struct. The dataset comprises six ZIP files in total.

## Folder Descriptions

- idw/: Data related to the temporal analysis using the IDW surrogate.
- idwr/: Data related to the temporal analysis using the IDWR surrogate.
- knn/: Data related to the temporal analysis using the KNN surrogate.
- lr-knn/: Data related the temporal analysis using to the LR-KNN surrogate.
- no-struct/: Data related the temporal analysis using to the No-Struct surrogate.
- static/: Data related to the static analysis.

## Contents per folder

Each analysis is structured into three main folders:

- Samples: Contains all the solutions for true and surrogate fitness landscape feature extraction, including the performance metric.
- Features: Contains all the true and surrogate features for each repeat of the BBOB-BIOBJ problem.
- Performance: Contains all the performance (hv) for each repeat of the BBOB-BIOBJ problem.

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Point Of Care Data Management Software Market Size 2024-2028

The point of care data management software market size is forecast to increase by USD 636.4 million, at a CAGR of 12.11% between 2023 and 2028.

The market is experiencing significant growth due to several key drivers. Firstly, the elimination of human errors in data entry and processing is a major advantage, leading to improved accuracy and efficiency in healthcare delivery. Secondly, the rising initiatives for the adoption of Electronic Health Records (EHRs) have created a demand for POC data management software, enabling seamless data access and sharing among healthcare providers. However, privacy and security concerns remain a challenge, as sensitive patient information must be protected. Market trends include the integration of artificial intelligence and machine learning technologies to enhance data analysis and decision-making capabilities, as well as the increasing use of cloud-based solutions for remote access and real-time data sharing. Overall, the POC data management software market is expected to continue its growth trajectory, driven by these factors and the increasing need for efficient and accurate data management in healthcare.

What will be the Size of the Market During the Forecast Period?

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The market is witnessing significant growth due to the increasing adoption of POC testing in hospitals and clinics. POC testing allows doctors and nurses to make quick decisions based on real-time patient data, especially in critical care units such as ICUs. The market is driven by the rising prevalence of infectious diseases, lifestyle-related diseases, and cardiac diseases, which require timely diagnosis and treatment. POC testing is increasingly being used for diseases like diabetes, where continuous monitoring of blood glucose levels is essential. The market for home-based POC devices is also growing rapidly, especially for diabetes patients who require regular monitoring.
Electronic Health Records (EHR) and healthcare data analytics are essential components of POC data management software, enabling patient-centered care and population health management. The market for POC data management software includes various types of devices such as blood gas analyzers, SmartICUs, and rapid tests. Chronic lower respiratory diseases are a significant application area for POC testing and data management software. Medical centers and critical care units are the major end-users of POC data management software, and the market is expected to grow at a steady pace in the coming years.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  On-premises
  Cloud


Geography

  North America

    US


  APAC

    China
    Japan


  Europe

    Germany
    UK


  South America



  Middle East and Africa

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

Point of Care (POC) technologies have revolutionized healthcare by enabling real-time diagnosis and treatment of various diseases at the bedside or in clinics. Hospitals and diagnostic clinics are major end-users of POC data management software, which facilitates patient flow, improves communication between doctors and nurses, and enhances patient-centered care. POC testing plays a crucial role in managing infectious diseases, lifestyle-related diseases, and chronic conditions such as diabetes, cardiac diseases, and respiratory diseases like COPD and asthma. Home-based POC devices have gained popularity among diabetes patients, enabling self-monitoring and remote monitoring by healthcare providers. Electronic Health Records (EHR) and healthcare data analytics are integral components of POC data management software, allowing for population health management and critical care units' effective management.

ICUs and critical care units require real-time data analysis to ensure optimal patient care, making POC data management software indispensable. Blood gas analyzers, such as SmartICU, are essential POC devices used in critical care units to monitor patients' oxygen levels and acid-base balance. NTT DATA, Roche, Glytec, MEDITECH, and DataLink Software are prominent players in the POC data management market. Rapid tests for infectious diseases and chronic lower respiratory diseases are also critical applications of POC data management software. The market for POC data management software is expanding in geographic markets, with CLIA-waived tests and PRM solutions gaining popularity.

Get a glance at the market report of share of various segments Request Free Sample

The on-

The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline changes across the United States. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

Rate constant estimation with heavy water requires a long-term experiment with data collection at multiple time points (3–4 weeks for mitochondrial proteome dynamics in mice and much longer in other species). When tissue proteins are analyzed, this approach requires euthanizing animals at each time point or multiple tissue biopsies in humans. Although short-term protocols are available, they require knowledge of the maximum number of isotope labels (N) and accurate quantification of observed 2H-enrichment in the peptide. The high-resolution accurate mass spectrometers used for proteome dynamics studies are characterized by a systematic spectral error that compromises these measurements. To circumvent these issues, we developed a simple algorithm for the rate constant calculation based on a single labeled sample and comparable unlabeled (time 0) sample. The algorithm determines N for all proteogenic amino acids from a long-term experiment to calculate the predicted plateau 2H-labeling of peptides for a short-term protocol and estimates the rate constant based on the measured baseline and the predicted plateau 2H-labeling of peptides. The method was validated based on the rate constant estimation in a long-term experiment in mice and dogs. The improved 2 time-point method enables the rate constant calculation with less than 10% relative error compared to the bench-marked multi-point method in mice and dogs and allows us to detect diet-induced subtle changes in ApoAI turnover in mice. In conclusion, we have developed and validated a new algorithm for protein rate constant calculation based on 2-time point measurements that could also be applied to other biomolecules.

We performed replicated, repeated mapped tree inventory measures (x,y, height, diameter, vitality, etc.) to allow analysis of the spatial and temporal structure in 1 ha planted pedunculate oak (Quercus robur) stand established in 2003 to resemble the oak-hornnbeam forests for genetic conservation purposes in Po Valley (Foresta Carpaneta). Two inventories were carried out in 2009 and 2019. The use of replicated, repeated, and mapped tree measures allows the examination of true changes in spatial pattern processes through time in this forest type.

Mean Intersection-over-Union and IoU per class results for three different neural network architectures tested on our maize and tomato point clouds.

The fixed-point time-lapse camera market is projected to reach a value of XXX million by 2033, exhibiting a CAGR of XX% during the forecast period of 2025-2033. Growth in the construction and real estate sectors, particularly in developing economies, is expected to drive demand for time-lapse cameras for project documentation and progress monitoring. Additionally, increasing adoption of time-lapse photography in outdoor photography, wildlife monitoring, and engineering applications is expected to contribute to market growth. Regional analysis indicates that North America is a significant market for fixed-point time-lapse cameras due to high construction spending and technological advancements. Asia Pacific is expected to witness substantial growth, driven by rapid urbanization and infrastructure development. Europe is a mature market with a strong presence of established players, while the Middle East and Africa region is expected to show steady growth due to increasing infrastructure projects. Key players in the market include ATLI Timelapse, OxBlue, TrueLook, EarthCam, and IBEAM Systems, offering a range of fixed-point time-lapse cameras with features such as high-resolution imaging, remote monitoring, and cloud connectivity.

The near-continuous time series of point clouds was acquired in the snow-covered area of the Schneeferner at the Zugspitze mountain in Germany using hourly terrestrial laser scanning (TLS) over a period of five days in April 2018. The dataset comprises around 130 epochs of 10 to 25 million points per scan with centimeter-scale accuracy and point spacing. The 4D geospatial dataset of the experimental near-continuous laser scanning setup can be used for analysis of snow cover dynamics and in general method development for change analysis of natural scenes using laser scanning time series.

ANOVA test for the characteristic scale of clustering among the spatial point patterns based on temporal aggregations.