Temporal Analysis

Temporal and Spatial Analysis of Neural Tube Defects and Detection of Geographical Factors in Shanxi Province, China

/ Matt Artz

PLOS_ONEPLOS ONE, Published 21 April 2016

By Yilan Liao, Yan Zhang, Lei He, Jinfeng Wang, Xin Liu, Ningxu Zhang, and Bing Xu

“Background: Neural tube defects (NTDs) are congenital birth defects that occur in the central nervous system, and they have the highest incidence among all birth defects. Shanxi Province in China has the world’s highest rate of NTDs. Since the 1990s, China’s government has worked on many birth defect prevention programs to reduce the occurrence of NTDs, such as pregnancy planning, health education, genetic counseling, antenatal ultrasonography and serological screening. However, the rate of NTDs in Shanxi Province is still higher than the world’s average morbidity rate after intervention. In addition, Shanxi Province has abundant coal reserves, and is the largest coal production province in China. The objectives of this study are to determine the temporal and spatial variation of the NTD rate in rural areas of Shanxi Province, China, and identify geographical environmental factors that were associated with NTDs in the risk area.

“Methods: In this study, Heshun County and Yuanping County in Shanxi Province, which have high incidence of NTDs, were selected as the study areas. Two paired sample T test was used to analyze the changes in the risk of NTDs from the time dimension. Ripley’s k function and spatial filtering were combined with geographic information system (GIS) software to study the changes in the risk of NTDs from the spatial dimension. In addition, geographical detectors were used to identify the risk geographical environmental factors of NTDs in the study areas, especially the areas close to the coal sites and main roads.

Cluster areas of Neural Tube Defects in Heshun County and Yuanping County.

Cluster areas of Neural Tube Defects in Heshun County and Yuanping County.

“Results: In both Heshun County and Yuanping County, the incidence of NTDs was significantly (P<0.05) reduced after intervention. The results from spatial analysis showed that significant spatial heterogeneity existed in both counties. NTD clusters were still identified in areas close to coal sites and main roads after interventions. This study also revealed that the elevation, fault and soil types always had a larger influence on the incidence of NTDs in our study areas. In addition, distance to the river was a risk factor of NTDs in areas close to the coal sites and main roads.

“Conclusion: The existing interventions may have played an important role to reduce the incidence of NTDs. However, there is still spatial heterogeneity in both counties after using the traditional intervention methods. The government needs to take more measures to strengthen the environmental restoration to prevent the occurrence of NTDs, especially those areas close to coal sites and main roads. The outcome of this research provides an important theoretical basis and technical support for the government to prevent the occurrence of NTDs.”

Geostatistical interpolation model selection based on ArcGIS and spatio-temporal variability analysis of groundwater level in piedmont plains, northwest China

/ Matt Artz / 1 Comment

SpringerPlus, Published 11 April 2016

By Yong Xiao, Xiaomin Gu, Shiyang YinEmail author, Jingli Shao, Yali Cui, Qiulan Zhang, and Yong Niu

“Based on the geo-statistical theory and ArcGIS geo-statistical module, datas of 30 groundwater level observation wells were used to estimate the decline of groundwater level in Beijing piedmont. Seven different interpolation methods (inverse distance weighted interpolation, global polynomial interpolation, local polynomial interpolation, tension spline interpolation, ordinary Kriging interpolation, simple Kriging interpolation and universal Kriging interpolation) were used for interpolating groundwater level between 2001 and 2013. Cross-validation, absolute error and coefficient of determination (R2) was applied to evaluate the accuracy of different methods.

Groundwater level drawdown during 2001 and 2013.

Groundwater level drawdown during 2001 and 2013.

“The result shows that simple Kriging method gave the best fit. The analysis of spatial and temporal variability suggest that the nugget effects from 2001 to 2013 were increasing, which means the spatial correlation weakened gradually under the influence of human activities. The spatial variability in the middle areas of the alluvial–proluvial fan is relatively higher than area in top and bottom. Since the changes of the land use, groundwater level also has a temporal variation, the average decline rate of groundwater level between 2007 and 2013 increases compared with 2001–2006. Urban development and population growth cause over-exploitation of residential and industrial areas. The decline rate of the groundwater level in residential, industrial and river areas is relatively high, while the decreasing of farmland area and development of water-saving irrigation reduce the quantity of water using by agriculture and decline rate of groundwater level in agricultural area is not significant.”

A GIS-based Relational Data Model for Multi-dimensional Representation of River Hydrodynamics and Morphodynamics

/ Matt Artz / 1 Comment

EMS-S13648152Environmental Modelling & Software, Volume 65, March 2015, Pages 79–93

By Dongsu Kim, Marian Muste, and Venkatesh Merwade

“Highlights

  • Represent river data in a curvilinear coordinate system to support river channel oriented spatial analyses.
  • Represent multidimensional river features through points, lines, polygons, and volumes.
  • Represent simulated gridded data for river channels that can be readily coupled with observed data.
  • Represent spatio-temporal evolution of dynamic river objects using Eulerian or Lagrangian observational frameworks.
  • Efficiently store and retrieve data acquired in-situ along with the ancillary metadata.

“The emerging capabilities of the geo-based information systems to integrate spatial and temporal attributes of in-situ measurements is a long-waited solution to efficiently organize, visualize, and analyze the vast amount of data produced by the new generations of river instruments. This paper describes the construct of a river data model linked to a relational database that can be populated with both measured and simulated river data to facilitate descriptions of river features and processes using hydraulic/hydrologic terminology.

Diagram of the connectivity between multidimensional river objects in a cross-section and the river network: Relationship between the CrossSection3DPoint and CrossSection2DPoint in 3D cross-sections.

Diagram of the connectivity between multidimensional river objects in a cross-section and the river network: Relationship between the CrossSection3DPoint and CrossSection2DPoint in 3D cross-sections.

“The proposed model, labeled Arc River, is built in close connection with the existing Arc Hydro data model developed for water-related features to ensure the connection of the river characteristics with their floodplains and watersheds. This paper illustrates Arc River data model capabilities in conjunction with Acoustic Doppler Current Profiler measurements to demonstrate that essential river morphodynamics and hydrodynamics aspects can be described using data on the flow and its boundaries.”

OGC Seeks Comment on New Working Group Focused on Representing Time Series Spatial Observation Data

/ Matt Artz / 1 Comment

OGC_Logo_Border_Blue_3DThe OGC seeks comment on the charter for a new OGC Standards Working Group (SWG) being formed to develop an OGC standard – TimeSeriesML 1.0 – for the representation of time series observations data. This work is motivated by requirements in hydrology and meteorology, but the standard will be designed to be useful in any application that involves periodic sampling of spatially referenced data. Time scales could range from the geologic time scales of climate to the nanosecond time scales of radiofrequency spectrum. It is intended that the proposed TimeSeriesML 1.0 standard will be implemented as an application schema of the Geography Markup Language version 3.3 and make use of “OMXML,” the OGC XML implementation of the OGC and ISO Observations and Measurements (O&M) conceptual model (OGC Observations and Measurements v2.0 also published as ISO/DIS 19156).

This SWG will develop a TimeSeriesML 1.0 candidate standard submission, coordinate a public comment period, and process any comments received during this period. The final deliverable of the SWG will be a version of the candidate standard for consideration by the OGC membership for approval as an OGC standard.

The draft charter for the OGC TimeSeriesML Standards Working Group is available at https://portal.opengeospatial.org/files/60856. Send comments on the charter to charter-requests [at] opengeospatial.org.

The 30 day public comment period ends 20 November 2014. After the ad hoc group seeking to form the new SWG has addressed comments received in response to this Request for Comments (RFC) the draft charter will be submitted to the OGC Technical Committee and Planning Committee for their review and likely approval.

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The OGC® is an international consortium of more than 495 companies, government agencies, research organizations, and universities participating in a consensus process to develop publicly available geospatial standards. OGC standards support interoperable solutions that “geo-enable” the Web, wireless and location-based services, and mainstream IT. Visit the OGC website at http://www.opengeospatial.org.

[Source: OGC press release]

Spatio-temporal Analysis of Forest Changes in Contrasting Land Use Regimes of Zanzibar, Tanzania

/ Matt Artz / 1 Comment

Applied GeographyApplied Geography, Volume 55, December 2014, Pages 193–202

By Markus Kukkonen and Niina Käyhkö

“Highlights:

  • Deforestation has accelerated in Unguja between 1975 and 2009 and current deforestation rate is 0.46%.
  • There are significant differences in forest changes and drivers of change between community, government and agroforest land use regimes of Zanzibar.
  • Shifting cultivation, urban expansion and spread of permanent agriculture are the main proximate causes of forest clearings.
  • The accelerating deforestation rate sheds negative light on the long-term developments of the forest cover.

“We have estimated forest changes and deforestation trends on the island of Unguja (Zanzibar) over the last three decades based on satellite images, forest cover change trajectory and post-forest land cover analysis. The results show that deforestation has intensified and forest cover change rate has changed from 0.03% to −0.46% between 1975–1996 and 1996–2009. On average 0.88 km2 of forests were lost annually, which makes altogether 29.9 km2 during the 34 year study period. Using three distinctive land use regimes prevailing on the island, we are able to show that in reality the changes and their causes were unique in each region. The community forest land use regime was dominated by shifting cultivation related cyclical changes combined with growing deforestation rates. The deforestation rates were also high in agroforest land use regime, but here forest clearings were associated with urban sprawl. Opposite to these two regimes, the cover increased in government forest areas, due to large tree planting schemes. However, forest clearings increased significantly since 1996 in government areas and currently all regimes are facing decreasing forest cover. Population growth, in-migration, urbanization, tourism and increasing demand of agricultural and forestry products were the main underlying causes behind the deforestation. Although, the long-term developments of the forest cover are dictated by these relatively uncontrollable underlying causes, we suggest few actions to restrain deforestation and its effects. These actions include establishment of protected area network with forest corridors, heeding trees in urban and agricultural land use planning, replanting cleared governmental plantations and extending plantations outside the Island.”

Adoption of Cropping Sequences in Northeast Montana: A Spatio-temporal Analysis

/ Matt Artz / 1 Comment

AEEBy John A. Long, Rick L. Lawrence, Perry R. Miller, Lucy A. Marshall, and Mark C. Greenwood

Agriculture, Ecosystems & Environment, Volume 197, 01 December 2014, Pages 77–87, Published Online 07 August 2014

“Highlights

  • Study was a spatio-temporal analysis of management practices in northeast Montana.
  • We examined cereal–pulse sequences and strip-cropping conversions during 2001–2012.
  • Both practices were spatially clustered in the region.
  • Neither practice was strongly associated with spread due to diffusion of innovation.
  • Both practices were strongly associated with the availability of water.

“Producers make the decision to adopt a particular agricultural practice within a range of social, economic, environmental, and agronomic constraints. The semiarid regions of the US northern Great Plains are dominated by dryland farming practices and the traditional practice has been to rotate small-grain cereals with summer fallow; however, producers are moving away from this practice. The area of fallow in northeastern Montana decreased by one-third and the area of pulse crops increased nearly six-fold during 2001–2012. We previously identified two key practices that are indicative of regionally changing agricultural practices: (1) the broad-scale adoption of cereal–pulse sequences, and (2) the conversion from continuous strip-cropping to block managed cereal-based sequences. Here, we examined the adoption of these two practices from a spatio-temporal perspective to determine if the observed patterns were consistent with those expected from a priori processes: random occurrence, spread and adoption of the practices due to social interaction as described in innovation diffusion theory, or adoption based on environmental factors. Our results suggest that the adoption and spread of both practices were likely constrained by the suitability of the physical environment. Available water, in particular, exerts a fundamental control on the decision whether or not to adopt either practice. We also found evidence for the expansion of these practices due, in part, to social factors, particularly during the early period of adoption. We conclude that producers made the decision whether or not to adopt these practices primarily as a function of environmental suitability and, to a lesser extent, within the context of social interactions.”

Mapping Sleeping Bees within Their Nest: Spatial and Temporal Analysis of Worker Honey Bee Sleep

/ Matt Artz / 1 Comment

PLOS_ONEPLOS One, Published 16 July 2014

Barrett Anthony Klein, Martin Stiegler, Arno Klein, and Jürgen Tautz

“Patterns of behavior within societies have long been visualized and interpreted using maps. Mapping the occurrence of sleep across individuals within a society could offer clues as to functional aspects of sleep. In spite of this, a detailed spatial analysis of sleep has never been conducted on an invertebrate society. We introduce the concept of mapping sleep across an insect society, and provide an empirical example, mapping sleep patterns within colonies of European honey bees (Apis mellifera L.). Honey bees face variables such as temperature and position of resources within their colony’s nest that may impact their sleep.

Infrared images revealing thermal activity across beehives. (A) Sequence of colony-scale changes across the entrance side of Colony 1. In clockwise order from the upper left corner, 1700, 0400, 0900 and 1500 h, respectively. Entrance/exit is in the lower left corner of the hive, leading out tube at left of each image. Brood comb is most easily seen as the glowing warm area at 0400 h. (B) Observation hive containing Colony 2, with filter-covered lamp at upper right, and bees visibly exiting hive tunnel at lower right. (C) Exposed nest composed of parallel sheets of comb, set up by Dirk Ahrens-Lagast to induce bees to construct a more natural nest architecture; not used in study. B.A.K. took all images with FLIR thermal cameras on non-experiment days under different ambient temperature conditions. Temperature scale values (°C) were adjusted for thermal camera settings.

Infrared image revealing thermal activity across beehives. Exposed nest composed of parallel sheets of comb, set up by Dirk Ahrens-Lagast to induce bees to construct a more natural nest architecture; not used in study. B.A.K. took all images with FLIR thermal cameras on non-experiment days under different ambient temperature conditions. Temperature scale values (°C) were adjusted for thermal camera settings.

“We mapped sleep behavior and temperature of worker bees and produced maps of their nest’s comb contents as the colony grew and contents changed. By following marked bees, we discovered that individuals slept in many locations, but bees of different worker castes slept in different areas of the nest relative to position of the brood and surrounding temperature. Older worker bees generally slept outside cells, closer to the perimeter of the nest, in colder regions, and away from uncapped brood. Younger worker bees generally slept inside cells and closer to the center of the nest, and spent more time asleep than awake when surrounded by uncapped brood. The average surface temperature of sleeping foragers was lower than the surface temperature of their surroundings, offering a possible indicator of sleep for this caste. We propose mechanisms that could generate caste-dependent sleep patterns and discuss functional significance of these patterns.”

Spatial Distribution and Risk Factors of Influenza in Jiangsu Province, China, based on Geographical Information System

/ Matt Artz / 1 Comment

ghGeospatial Health, Volume 8, Number 2, May 2014, Pages 429-435

By Jia-Cheng Zhang, Wen-Dong Liu, Qi Liang, Jian-Li Hu, Jessie Norris, Ying Wu, Chang-Jun Bao, Fen-Yang Tang, Peng Huang, Yang Zhao, Rong-Bin Yu, Ming-Hao Zhou, Hong-Bing Shen, Feng Chen, and Zhi-Hang Peng

“Influenza poses a constant, heavy burden on society. Recent research has focused on ecological factors associated with influenza incidence and has also studied influenza with respect to its geographic spread at different scales. This research explores the temporal and spatial parameters of influenza and identifies factors influencing its transmission.

Spatial clusters of annual incidence of influenza (hotspots) in Jiangsu province, P.R. China, for the years 2004 (a), 2 006 (b), 2009 (c) and 2011 (d).

Spatial clusters of annual incidence of influenza (hotspots) in Jiangsu province, P.R. China, for the years 2004 (a), 2006 (b), 2009 (c) and 2011 (d).

“A spatial autocorrelation analysis, a spatial-temporal cluster analysis and a spatial regression analysis of influenza rates, carried out in Jiangsu province from 2004 to 2011, found that influenza rates to be spatially dependent in 2004, 2005, 2006 and 2008. South-western districts consistently revealed hotspots of high-incidence influenza. The regression analysis indicates that railways, rivers and lakes are important predictive environmental variables for influenza risk. A better understanding of the epidemic pattern and ecological factors associated with pandemic influenza should benefit public health officials with respect to prevention and controlling measures during future epidemics. ”

A Spatial and Temporal Analysis of Japanese Encephalitis in Mainland China, 1963–1975

/ Matt Artz / 1 Comment

PLOS_ONEPLOS ONE, Published Online June 09, 2014

By Xiaolong Li, Xiaoyan Gao, Zhoupeng Ren, Yuxi Cao, Jinfeng Wang, Guodong Liang

“More than a million Japanese encephalitis (JE) cases occurred in mainland China from the 1960s to 1970s without vaccine interventions. The aim of this study is to analyze the spatial and temporal pattern of JE cases reported in mainland China from 1965 to 1973 in the absence of JE vaccination, and to discuss the impacts of climatic and geographical factors on JE during that period. Thus, the data of reported JE cases at provincial level and monthly precipitation and monthly mean temperature from 1963 to 1975 in mainland China were collected. Local Indicators of Spatial Association analysis was performed to identify spatial clusters at the province level. During that period, The epidemic peaked in 1966 and 1971 and the JE incidence reached up to 20.58/100000 and 20.92/100000, respectively. The endemic regions can be divided into three classes including high, medium, and low prevalence regions.

Japanese encephalitis in mainland China during 1963–1975. (A) China experienced a natural JE epidemic period with no vaccine interventions from 1963 to 1975. The bar graph of JE incidence in mainland China from 1951 to 2008 is cited from reference 10. (B) During the JE epidemic in 1971, JE incidence in 11 provinces distributed in the coastal areas of eastern China (red color) was higher than the national average (20.92/100,000). (C) LISA cluster map for JE incidence during 1963–1975 shows the center of cluster in color. High-High indicates a significant (P<0.05) spatial cluster of high JE incidence values; Low-Low represents a spatial cluster of low JE incidence values.

Japanese encephalitis in mainland China during 1963–1975. (A) China experienced a natural JE epidemic period with no vaccine interventions from 1963 to 1975. The bar graph of JE incidence in mainland China from 1951 to 2008 is cited from reference 10. (B) During the JE epidemic in 1971, JE incidence in 11 provinces distributed in the coastal areas of eastern China (red color) was higher than the national average (20.92/100,000). (C) LISA cluster map for JE incidence during 1963–1975 shows the center of cluster in color. High-High indicates a significant (P<0.05) spatial cluster of high JE incidence values; Low-Low represents a spatial cluster of low JE incidence values.

“Through spatial cluster analysis, JE epidemic hot spots were identified; most were located in the Yangtze River Plain which lies in the southeast of China. In addition, JE incidence was shown to vary among eight geomorphic units in China. Also, the JE incidence in the Loess Plateau and the North China Plain was showed to increase with the rise of temperature. Likewise, JE incidence in the Loess Plateau and the Yangtze River Plain was observed a same trend with the increase of rainfall. In conclusion, the JE cases clustered geographically during the epidemic period. Besides, the JE incidence was markedly higher on the plains than plateaus. These results may provide an insight into the epidemiological characteristics of JE in the absence of vaccine interventions and assist health authorities, both in China and potentially in Europe and Americas, in JE prevention and control strategies.”

Analyzing Spatial Clustering and the Spatiotemporal Nature and Trends of HIV/AIDS Prevalence using GIS: The Case of Malawi, 1994-2010

/ Matt Artz

bmcidBMC Infectious Diseases 2014, 14:285, Published online 23 May 2014

By Leo C Zulu, Ezekiel Kalipeni, and Eliza Johannes

“Background
Although local spatiotemporal analysis can improve understanding of geographic variation of the HIV epidemic, its drivers, and the search for targeted interventions, it is limited in sub-Saharan Africa. Despite recent declines, Malawi’s estimated 10.0% HIV prevalence (2011) remained among the highest globally. Using data on pregnant women in Malawi, this study 1) examines spatiotemporal trends in HIV prevalence 1994-2010, and 2) for 2010, identifies and maps the spatial variation/clustering of factors associated with HIV prevalence at district level.

Methods
Inverse distance weighting was used within ArcGIS Geographic Information Systems (GIS) software to generate continuous surfaces of HIV prevalence from point data (1994, 1996, 1999, 2001, 2003, 2005, 2007, and 2010) obtained from surveillance antenatal clinics. From the surfaces prevalence estimates were extracted at district level and the results mapped nationally. Spatial dependency (autocorrelation) and clustering of HIV prevalence were also analyzed. Correlation and multiple regression analyses were used to identify factors associated with HIV prevalence for 2010 and their spatial variation/clustering mapped and compared to HIV clustering.

malawi

Results
Analysis revealed wide spatial variation in HIV prevalence at regional, urban/rural, district and sub-district levels. However, prevalence was spatially leveling out within and across ‘sub-epidemics’ while declining significantly after 1999. Prevalence exhibited statistically significant spatial dependence nationally following initial (1995-1999) localized, patchy low/high patterns as the epidemic spread rapidly. Locally, HIV “hotspots” clustered among eleven southern districts/cities while a “coldspot” captured configurations of six central region districts. Preliminary multiple regression of 2010 HIV prevalence produced a model with four significant explanatory factors (adjusted R2 = 0.688): mean distance to main roads, mean travel time to nearest transport, percentage that had taken an HIV test ever, and percentage attaining a senior primary education. Spatial clustering linked some factors to particular subsets of high HIV-prevalence districts.

Conclusions
Spatial analysis enhanced understanding of local spatiotemporal variation in HIV prevalence, possible underlying factors, and potential for differentiated spatial targeting of interventions. Findings suggest that intervention strategies should also emphasize improved access to health/HIV services, basic education, and syphilis management, particularly in rural hotspot districts, as further research is done on drivers at finer scale.”

Read the paper [PDF]