Space-time density of field trip trajectory: exploring spatio-temporal patterns in movement data

2017 ◽  
Vol 25 (1) ◽  
pp. 141-150 ◽  
Author(s):  
Nahye Cho ◽  
Youngok Kang
Keyword(s):  
Temporal Patterns ◽  
Field Trip ◽  
Space Time ◽  
Movement Data ◽  
Spatio Temporal ◽  
Time Density

Revisitation analysis uncovers spatio-temporal patterns in animal movement data

Ecography ◽  
2018 ◽  
Vol 41 (11) ◽  
pp. 1801-1811 ◽  
Author(s):  
Chloe Bracis ◽  
Keith L. Bildstein ◽  
Thomas Mueller
Keyword(s):  
Animal Movement ◽  
Temporal Patterns ◽  
Movement Data ◽  
Spatio Temporal

scholarly journals Facilitating insights with a user adaptable dashboard, illustrated by airport connectivity data

2018 ◽  
Vol 1 ◽  
pp. 1-6
Author(s):  
Ieva Dobraja ◽  
Menno-Jan Kraak ◽  
Yuri Engelhardt
Keyword(s):  
Temporal Patterns ◽  
Data Representation ◽  
Data Exploration ◽  
Temporal Data ◽  
Movement Data ◽  
Graphical Data ◽  
Multiple Data ◽  
Multi Temporal ◽  
Spatio Temporal ◽  
Heterogeneous Multiscale

Since the movement data exist, there have been approaches to collect and analyze them to get insights. This kind of data is often heterogeneous, multiscale and multi-temporal. Those interested in spatio-temporal patterns of movement data do not gain insights from textual descriptions. Therefore, visualization is required. As spatio-temporal movement data can be complex because size and characteristics, it is even challenging to create an overview of it. Plotting all the data on the screen will not be the solution as it likely will result into cluttered images where no data exploration is possible. To ensure that users will receive the information they are interested in, it is important to provide a graphical data representation environment where exploration to gain insights are possible not only in the overall level but at sub-levels as well. A dashboard would be a solution the representation of heterogeneous spatio- temporal data. It provides an overview and helps to unravel the complexity of data by splitting data in multiple data representation views. The adaptability of dashboard will help to reveal the information which cannot be seen in the overview.

scholarly journals Spatio-Temporal Patterns of Dengue Incidence in Medan City, North Sumatera, Indonesia

2021 ◽  
Vol 6 (1) ◽  
pp. 30
Author(s):  
Ayodhia Pitaloka Pasaribu ◽  
Tsheten Tsheten ◽  
Muhammad Yamin ◽  
Yulia Maryani ◽  
Fahmi Fahmi ◽  
...  
Keyword(s):  
Control Strategies ◽  
Public Health Problem ◽  
Temporal Patterns ◽  
Space Time ◽  
Scan Statistic ◽  
Cold Spots ◽  
Spatio Temporal ◽  
Dengue Control ◽  
And Control ◽  
Time Scan

Dengue has been a perennial public health problem in Medan city, North Sumatera, despite the widespread implementation of dengue control. Understanding the spatial and temporal pattern of dengue is critical for effective implementation of dengue control strategies. This study aimed to characterize the epidemiology and spatio-temporal patterns of dengue in Medan City, Indonesia. Data on dengue incidence were obtained from January 2016 to December 2019. Kulldorff’s space-time scan statistic was used to identify dengue clusters. The Getis-Ord Gi* and Anselin Local Moran’s I statistics were used for further characterisation of dengue hotspots and cold spots. Results: A total of 5556 cases were reported from 151 villages across 21 districts in Medan City. Annual incidence in villages varied from zero to 439.32 per 100,000 inhabitants. According to Kulldorf’s space-time scan statistic, the most likely cluster was located in 27 villages in the south-west of Medan between January 2016 and February 2017, with a relative risk (RR) of 2.47. Getis-Ord Gi* and LISA statistics also identified these villages as hotpot areas. Significant space-time dengue clusters were identified during the study period. These clusters could be prioritized for resource allocation for more efficient prevention and control of dengue.

scholarly journals Exploratory Space–Time Analyses of Reported Lyme Borreliosis Cases in France, 2016–2019

Pathogens ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 444
Author(s):  
Wen Fu ◽  
Camille Bonnet ◽  
Julie Figoni ◽  
Alexandra Septfons ◽  
Raphaëlle Métras
Keyword(s):  
Lyme Borreliosis ◽  
Temporal Patterns ◽  
Space Time ◽  
Mediterranean Coast ◽  
Pathogen Transmission ◽  
Health Concern ◽  
Surveillance Systems ◽  
Spatial Clusters ◽  
Spatial And Temporal Patterns ◽  
Spatio Temporal

In recent decades, the incidence of Lyme borreliosis (LB) in Europe seems to have increased, underpinning a growing public health concern. LB surveillance systems across the continent are heterogeneous, and the spatial and temporal patterns of LB reports have been little documented. In this study, we explored the spatio-temporal patterns of LB cases reported in France from 2016 to 2019, to describe high-risk clusters and generate hypotheses on their occurrence. The space–time K-function and the Kulldorf’s scan statistic were implemented separately for each year to evaluate space–time interaction between reported cases and searching clusters. The results show that the main spatial clusters, of radius size up to 97 km, were reported in central and northeastern France each year. In 2017–2019, spatial clusters were also identified in more southern areas (near the Alps and the Mediterranean coast). Spatio-temporal clustering occurred between May and August, over one-month to three-month windows in 2016–2017 and in 2018–2019. A strong spatio-temporal interaction was identified in 2018 within 16 km and seven days, suggesting a potential local and intense pathogen transmission process. Ongoing improved surveillance and accounting for animal hosts, vectors, meteorological factors and human behaviors are keys to further elucidate LB spatio-temporal patterns.

SPATIO-TEMPORAL PATTERNS OF WORKFORCE IN INDIA DURING 1981-2011

2019 ◽  
Vol 38 (2) ◽  
pp. 239-254
Author(s):  
M.B. SINGH ◽  
◽  
NITIN KUMAR MISHRA ◽  
Keyword(s):  
Temporal Patterns ◽  
Spatio Temporal
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