Attached file provides supplementary data for linked article.

Temperature, solar radiation and water are major important variables in ecosystem models which are measurable via wireless sensor networks (WSN). Effective data analysis is necessary to extract significant spatial and temporal information. In this work, information regarding the long term variation of seasonal field environment conditions is explored using Hilbert-Huang transform (HHT) based analysis on the wireless sensor network data collection. The data collection network, consisting of 36 wireless nodes, covers an area of 100 square kilometres in Yanqing, the northwest of Beijing CBD, in China and data collection involves environmental parameter observations taken over a period of three months in 2011. The analysis used the empirical mode decomposition (EMD/EEMD) to break a time sequence of data down to a finite set of intrinsic mode functions (IMFs). Both spatial and temporal properties of data explored by HHT analysis are demonstrated. Our research shows potential for better understanding the spatial-temporal relationships among environmental parameters using WSN and HHT.

Abstract: INTRODUCTION Currently, dengue fever, chikungunya fever, and zika virus represent serious public health issues in Brazil, despite efforts to control the vector, the Aedes aegypti mosquito. METHODS: This was a descriptive and ecological study of dengue deaths occurring from 2002 to 2013 in São Luis, Maranhão, Brazil. Geoprocessing software was used to draw maps, linking the geo-referenced deaths with urban/social data at census tract level. RESULTS: There were 74 deaths, concentrated in areas of social vulnerability. CONCLUSIONS: The use of geo-technology tools pointed to a concentration of dengue deaths in specific intra-urban areas.

Abstract Objective: to perform spatiotemporal analysis of suicide mortality in the elderly in Brazil. Methods: a mixed ecological study was carried out in which deaths from suicide among the elderly were analyzed using data from the Mortality Information System (MIS) and socio-demographic variables, from 2000 to 2014, with a trend analysis of this period. Univariate and bivariate spatial analysis was performed using the Moran Global and Moran Map index to evaluate the intensity and significance of spatial clusters. Results: there were 19,806 deaths due to suicide among the elderly in Brazil between 2000 and 2014. The ratio of male and female mortality rates was 4:1, with increasing trends for both genders (R2>0.8), but with greater intensity among men (p=0.0293). There was a moderate autocorrelation for men (I>0.40), with clusters forming for both genders in the south of Brazil. Bivariate analysis showed the formation of clusters in the southern region with the Human Development Index and aging variables and in the north and northeast regions based on dependence and illiteracy ratio. Conclusions: mortality due to suicide among the elderly has a tendency to increase and is unequally distributed in Brazil.

This file includes the following data for 25 stream sites in the Little Miami River watershed: diatom operational taxonomic units with their numbers and relative abundances of rbcL gene sequence reads, watershed land cover, and nutrient concentration and conductivity data. This dataset is associated with the following publication: Yuan, L., N. Smucker, C. Nietch, and E. Pilgrim. Quantifying spatial and temporal relationships between diatoms and nutrients in streams strengthens evidence of nutrient effects from monitoring data. Freshwater Science. The Society for Freshwater Science, Springfield, IL, 41(1): 100-112, (2022).

Mean score (standard deviation) by age group on total causal score (max = 21), prior knowledge, description (max = 6), explanation (max = 9) and mechanism (max = 3).

This dataset is built for time-series Sentinel-2 cloud detection and stored in Tensorflow TFRecord (refer to https://www.tensorflow.org/tutorials/load_data/tfrecord).

Each file is compressed in 7z format and can be decompressed using Bandzip or 7-zip software.

Dataset Structure:

Each filename can be split into three parts using underscores. The first part indicates whether it is designated for training or validation ('train' or 'val'); the second part indicates the Sentinel-2 tile name, and the last part indicates the number of samples in this file.

For each sample, it includes:

1. Sample ID;
2. Array of time series 4 band image patches in 10m resolution, shaped as (n_timestamps, 4, 42, 42);
3. Label list indicating cloud cover status for the center \(6\times6\) pixels of each timestamp;
4. Ordinal list for each timestamp;
5. Sample weight list (reserved);

Here is a demonstration function for parsing the TFRecord file:

import tensorflow as tf

# init Tensorflow Dataset from file name
def parseRecordDirect(fname):
  sep = '/'
  parts = tf.strings.split(fname,sep)
  tn = tf.strings.split(parts[-1],sep='_')[-2]
  nn = tf.strings.to_number(tf.strings.split(parts[-1],sep='_')[-1],tf.dtypes.int64)
  t = tf.data.Dataset.from_tensors(tn).repeat().take(nn)
  t1 = tf.data.TFRecordDataset(fname)
  ds = tf.data.Dataset.zip((t, t1))
  return ds

keys_to_features_direct = {
  'localid': tf.io.FixedLenFeature([], tf.int64, -1),
  'image_raw_ldseries': tf.io.FixedLenFeature((), tf.string, ''),
  'labels': tf.io.FixedLenFeature((), tf.string, ''),
  'dates': tf.io.FixedLenFeature((), tf.string, ''),
  'weights': tf.io.FixedLenFeature((), tf.string, '')
    }

# The Decoder (Optional)
class SeriesClassificationDirectDecorder(decoder.Decoder):
 """A tf.Example decoder for tfds classification datasets."""
 def _init_(self) -> None:
  super()._init_()

 def decode(self, tid, ds):
  parsed = tf.io.parse_single_example(ds, keys_to_features_direct)
  encoded = parsed['image_raw_ldseries']
  labels_encoded = parsed['labels']
  decoded = tf.io.decode_raw(encoded, tf.uint16)
  label = tf.io.decode_raw(labels_encoded, tf.int8)
  dates = tf.io.decode_raw(parsed['dates'], tf.int64)
  weight = tf.io.decode_raw(parsed['weights'], tf.float32)
  decoded = tf.reshape(decoded,[-1,4,42,42])
  sample_dict = {
   'tid': tid, # tile ID
   'dates': dates, # Date list
   'localid': parsed['localid'], # sample ID
   'imgs': decoded, # image array
   'labels': label, # label list
   'weights': weight
  }
  return sample_dict

# simple function 
def preprocessDirect(tid, record):
  parsed = tf.io.parse_single_example(record, keys_to_features_direct)
  encoded = parsed['image_raw_ldseries']
  labels_encoded = parsed['labels']
  decoded = tf.io.decode_raw(encoded, tf.uint16)
  label = tf.io.decode_raw(labels_encoded, tf.int8)
  dates = tf.io.decode_raw(parsed['dates'], tf.int64)
  weight = tf.io.decode_raw(parsed['weights'], tf.float32)
  decoded = tf.reshape(decoded,[-1,4,42,42])
  return tid, dates, parsed['localid'], decoded, label, weight

t1 = parseRecordDirect('filename here')
dataset = t1.map(preprocessDirect, num_parallel_calls=tf.data.experimental.AUTOTUNE)

#

Class Definition:

• 0: clear
• 1: opaque cloud
• 2: thin cloud
• 3: haze
• 4: cloud shadow
• 5: snow

Dataset Construction:

First, we randomly generate 500 points for each tile, and all these points are aligned to the pixel grid center of the subdatasets in 60m resolution (eg. B10) for consistence when comparing with other products.
It is because that other cloud detection method may use the cirrus band as features, which is in 60m resolution.

Then, the time series image patches of two shapes are cropped with each point as the center.
The patches of shape \(42 \times 42\) are cropped from the bands in 10m resolution (B2, B3, B4, B8) and are used to construct this dataset.
And the patches of shape \(348 \times 348\) are cropped from the True Colour Image (TCI, details see sentinel-2 user guide) file and are used to interpreting class labels.

The samples with a large number of timestamps could be time-consuming in the IO stage, thus the time series patches are divided into different groups with timestamps not exceeding 100 for every group.

Mean score (standard deviation) by age group on rotation (max = 16), paper folding (max = 14), vocabulary and block design.

This dataset was exctracted from time-lapse microscopy data for spatio-temporal analysis of Cancer Stem Cells in a cell population. Live cells were imaged for 5 days in a atmosphere and temperature controlled top stage incubator. The cell lines were SUM159PT or MDAMB231. The pALDH1a1-mNeptune reporter was used as CSC marker. Highly fluorescent cells are considered as CSC while low fluorescence signal indicate differentiated cell. see Bidan, N., Bailleul‐Dubois, J., Duval, J., Winter, M., Denoulet, M., Hannebicque, K., ... & Lagadec, C. (2019). Transcriptomic analysis of breast cancer stem cells and development of a pALDH1A1: mNeptune reporter system for live tracking. Proteomics, 19(21-22), 1800454. Cells were stained with Hoechst to allow segmenatation and tracking. The proposed dataset comprises : 1) RAW DATA : positions and fluorescent state of all single cell in 7mmx7mm field of view in both .csv and .mat files 2) intermediate analysis in matlab (including analysis scripts) 3) FIGURES : for spatio-temporal analysis of Cancer Stem Cell plasticity in both .csv and .mat files

The result of spatial-temporal analysis of new detected leprosy cases in Yunnan, China, 2011–2020.

Small, publicly available GPS trajectory datasets (e.g., [51, 55, 80]) have varying sampling rates with incomplete trajectories, (ii) are geographically incomplete, (iii) have inconsistent observation times and intervals, and (iv) lack geographical diversity.

Scarcity of information apprehending the current situation and spatial variation of water quality has limited our understanding on to what extent the current intensive human activities in the Central Rift Valley are affecting the natural resource base. This study investigated hydrochemistry, spatial and temporal quality variation of water from different sources, and their implications for agricultural uses. Water samples from rivers (Meki, Ketar, and Bulbula), Lake Zeway, and borehole or hand-dug (BH/HD) wells were analyzed for selected quality parameters following standard procedures. Historical data and current analysis results were used to analyze temporal changes using Mann-Kendall test statistics, while analysis of variance was used to detect spatial variation. The hydrochemistry analysis result showed that Na+ followed by Ca2+, except for Ketar River where Ca2+ followed by Na+, dominates among cations. Bicarbonate dominated among anions in all water samples. In Lake Zeway, no statistically significant spatial variations were evident for sampling locations, while electrical conductivity (EC) and iron showed a statistically significant increasing trend from 2005 to 2016. Iron in Lake Zeway; total dissolved solids, EC and Na+ in BH/HD wells, and K+ in all water sources were partly beyond the maximum permissible limit for drinking. Considering salinity effect on crop water availability, at least 60% of the water samples from rivers and Lake Zeway were in “none” restriction, while it was in “slight to moderate” restriction category in about 50% of water samples from BH/HD wells. Over 37% of the water samples from BH/HD wells in Zeway and Bulbula sub-watersheds showed high to very high alkali hazard. The RSC > 2.5 meq L-1 in most water samples of Lake Zeway, and BH/HD wells in Zeway and Bulbula sub-watersheds hastens sodium hazard rate. The study results suggest the need to adapt compatible management options on use and emplace strong water quality monitoring program to reduce risks.

Western U.S. rangelands have been quantified as six fractional cover (0-100%) components over the Landsat archive (1985-2018) at 30-m resolution, termed the “Back-in-Time” (BIT) dataset. Robust validation through space and time is needed to quantify product accuracy. We leverage field data observed concurrently with HRS imagery over multiple years and locations in the Western U.S. to dramatically expand the spatial extent and sample size of validation analysis relative to a direct comparison to field observations and to previous work. We compare HRS and BIT data in the corresponding space and time. Our objectives were to evaluate the temporal and spatio-temporal relationships between HRS and BIT data, and to compare their response to spatio-temporal variation in climate. We hypothesize that strong temporal and spatio-temporal relationships will exist between HRS and BIT data and that they will exhibit similar climate response. We evaluated a total of 42 HRS sites across the western U.S. with 32 sites in Wyoming, and 5 sites each in Nevada and Montana. HRS sites span a broad range of vegetation, biophysical, climatic, and disturbance regimes. Our HRS sites were strategically located to collectively capture the range of biophysical conditions within a region. Field data were used to train 2-m predictions of fractional component cover at each HRS site and year. The 2-m predictions were degraded to 30-m, and some were used to train regional Landsat-scale, 30-m, “base” maps of fractional component cover representing circa 2016 conditions. A Landsat-imagery time-series spanning 1985-2018, excluding 2012, was analyzed for change through time. Pixels and times identified as changed from the base were trained using the base fractional component cover from the pixels identified as unchanged. Changed pixels were labeled with the updated predictions, while the base was maintained in the unchanged pixels. The resulting BIT suite includes the fractional cover of the six components described above for 1985-2018. We compare the two datasets, HRS and BIT, in space and time. Two tabular data presented here correspond to a temporal and spatio-temporal validation of the BIT data. First, the temporal data are HRS and BIT component cover and climate variable means by site by year. Second, the spatio-temporal data are HRS and BIT component cover and associated climate variables at individual pixels in a site-year.

Dataset

This dataset was created by Takao Shibamoto

Contents

Dataset

This dataset was created by Ru

Contents

An understanding of the spatial dimension of economic and social activity requires methods that can separate out the relationship between spatial units that is due to the effect of common factors from that which is purely spatial even in an abstract sense. The same applies to the empirical analysis of networks in general. We use cross-unit averages to extract common factors (viewed as a source of strong cross-sectional dependence) and compare the results with the principal components approach widely used in the literature. We then apply multiple testing procedures to the de-factored observations in order to determine significant bilateral correlations (signifying connections) between spatial units and compare this to an approach that just uses distance to determine units that are neighbours. We apply these methods to real house price changes at the level of Metropolitan Statistical Areas in the USA, and estimate a heterogeneous spatio-temporal model for the de-factored real house price changes and obtain significant evidence of spatial connections, both positive and negative.

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.

These data describe the spatio-temporal distribution of dabbling duck species across the continental United States during four biologically relevant seasons. This dataset contains two types of distribution models: (1) probability of presence, and (2) abundance. The model type, species, and season depicted in a raster are defined in the file name. File names begin with either abun (indicating that it is an abundance model) or prob (indicating a probability of occurrence model). Following model type is species, for which there are 10 provided: ABDU (American Black Duck), AMEW (American Wigeon), BWTE (Blue-winged Teal), CITE (Cinnamon Teal), GADW (Gadwall), AGWT (Green-winged Teal), MALL (Mallard), MODU (Mottled Duck), NOPI (Northern Pintail), and NSHO (Northern Shoveler). Finally, season is indicated as either Winter, Spring, Summer, or Fall.

Zero-order and partial correlations between measures (significant associations in bold).

Dataset

This dataset was created by Aditya Kumar Barik

Contents

Existing methods for calculating directional relations in polygons (i.e. the directional similarity model, the cone-based model, and the modified cone-based model) were compared to human perceptions of change through an online survey. The results from this survey provide the first empirical validation of computational approaches to calculating directional relations in polygonal spatial data. We have found that while the evaluated methods generally agreed with each other, they varied in their alignment with human perceptions of directional relations. Specifically, translation transformations of the target and reference polygons showed greatest discrepancy to human perceptions and across methods. The online survey was developed using Qualtrics Survey Software, and participants were recruited via online messaging on social media (i.e., Twitter) with hashtags related to geographic information science. In total sixty-one individuals responded to the survey. This survey consisted of nine questions. For the first question, participants indicated how many years they have worked with GIS and/or spatial data. For the remaining eight questions, participants ranked pictorial database scenes according to degrees of their match to query scenes. Each of these questions represented a test case that Goyal and Egenhofer (2001) used to empirically evaluate the directional similarity model; participants were randomly presented with four of these questions. The query scenes were created using ArcMap and contained a pair of reference and target polygons. The database scenes were generated by gradually changing the geometry of the target polygon within each query scene. The relations between the target and reference polygon varied by the type of movement, the scaling change of the polygon, and changes in rotation. The scenarios were varied in order to capture a representative range of variability in polygon movements and changes in real world data. The R statistical computing environment was used to determine the similarity value that corresponds with each database scene based on the directional similarity model, the cone-based model, and the modified cone-based model. Using the survey responses, the frequency of first, second, third, etc. ranks were calculated for each database scene. Weight variables were multiplied by the frequencies to create an overall rank based on participant responses. A rank of one was weighted as a five, a rank of two was weighted as a four, and so on. Spearman’s rank-order correlation was used to measure the strength and direction of association between the rank determined using the three models and the rank determined using participant responses.