scholarly journals Effects of Different Spatial Precipitation Input Data on Crop Model Outputs under a Central European Climate

Atmosphere ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 290 ◽  
Author(s):  
Sabina Thaler ◽  
Luca Brocca ◽  
Luca Ciabatta ◽  
Josef Eitzinger ◽  
Sebastian Hahn ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Data ◽  
Crop Production ◽  
Input Data ◽  
Spring Barley ◽  
Regional Scale ◽  
Temporal Analysis ◽  
Spatial Data Analysis ◽  
Distributed Model ◽  
Precipitation Data

Crop simulation models, which are mainly being utilized as tools to assess the consequences of a changing climate and different management strategies on crop production at the field scale, are increasingly being used in a distributed model at the regional scale. Spatial data analysis and modelling in combination with geographic information systems (GIS) integrates information from soil, climate, and topography data into a larger area, providing a basis for spatial and temporal analysis. In the current study, the crop growth model Decision Support System for Agrotechnology Transfer (DSSAT) was used to evaluate five gridded precipitation input data at three locations in Austria. The precipitation data sets consist of the INtegrated Calibration and Application Tool (INCA) from the Meteorological Service Austria, two satellite precipitation data sources—Multisatellite Precipitation Analysis (TMPA) and Climate Prediction Center MORPHing (CMORPH)—and two rainfall estimates based on satellite soil moisture data. The latter were obtained through the application of the SM2RAIN algorithm (SM2RASC) and a regression analysis (RAASC) applied to the Metop-A/B Advanced SCATtermonter (ASCAT) soil moisture product during a 9-year period from 2007–2015. For the evaluation, the effect on winter wheat and spring barley yield, caused by different precipitation inputs, at a spatial resolution of around 25 km was used. The highest variance was obtained for the driest area with light-textured soils; TMPA and two soil moisture-based products show very good results in the more humid areas. The poorest performances at all three locations and for both crops were found with the CMORPH input data.

Multi-scale spatial analysis of human alveolar echinococcosis risk in China

Parasitology ◽  
2003 ◽  
Vol 127 (S1) ◽  
pp. S133-S141 ◽  
Author(s):  
F. M. DANSON ◽  
A. J. GRAHAM ◽  
D. R. J. PLEYDELL ◽  
M. CAMPOS-PONCE ◽  
P. GIRAUDOUX ◽  
...  
Keyword(s):  
Spatial Data ◽  
Disease Transmission ◽  
Spatial Scales ◽  
Regional Scale ◽  
Central China ◽  
Spatial Data Analysis ◽  
Transmission Risk ◽  
Spatial Proximity ◽  
Human Populations ◽  
Remotely Sensed Data

Risk factors for the transmission of Echinococcus multilocularis to humans operate at a range of spatial scales. Over a large area, such as China, regional scale risk is correlated with variation in climatic conditions because of its effect on the spatial distribution of landscapes that can support E. multilocularis transmission in wildlife hosts and the probability of egg survival. At a local scale of a few kilometres, or tens of kilometres, transmission risk is related to the spatial proximity of human populations and landscapes with active transmission. At the patch scale, when considering individual villages or households, human behavioural factors are important and for individuals genetic and immunological factors play a role. Satellite remote sensing can provide landscape information at a range of spatial scales and provide a spatial framework within which to examine transmission patterns. This paper reviews the application of remotely sensed data and spatial data analysis to develop a better understanding of disease transmission and shows how such data have been used to examine human alveolar echinocossosis infection patterns, at a range of spatial scales, in an endemic area in central China.

Spatial data analysis and integration for regional-scale geothermal potential mapping, West Java, Indonesia

Geothermics ◽  
2008 ◽  
Vol 37 (3) ◽  
pp. 267-299 ◽  
Author(s):  
Emmanuel John M. Carranza ◽  
Hendro Wibowo ◽  
Sally D. Barritt ◽  
Prihadi Sumintadireja
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Regional Scale ◽  
Spatial Data Analysis ◽  
West Java ◽  
Geothermal Potential ◽  
Potential Mapping

scholarly journals Measuring Spatial Cluster for Leading Industries in Surakarta with Exploratory Spatial Data Analysis (ESDA)

2017 ◽  
Vol 18 (1) ◽  
pp. 64
Author(s):  
Muhammad Arif ◽  
Didit Purnomo
Keyword(s):  
Data Analysis ◽  
Spatial Autocorrelation ◽  
Spatial Data ◽  
Regional Scale ◽  
Industrial Sector ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
Spatial Approach ◽  
Leading Industry ◽  
The City

Economic clusters are significant to support the economic growth, particularly at regional scale. The approach in the analysis has evolved from the emphasis on the comparison between the intra and extra regional into the spatial approach that is capable to detect the prevailing movement and concentration pattern in particular economic activity, hence the generated data is more informative and analyzable. This paper concentrates in identifying the location and assessing the economic clusters of leading industries in Surakarta City, Indonesia based on the number of units and labor absorption by using the Exploratory Spatial Data Analysis (ESDA). In association with the first objective, ArcGis was employed to find out how the concentration of leading industries in Surakarta was formed. The analysis revealed that the industries in Surakarta City have a propensity to be remote from downtown and concentrated in the northern part of the city. The second objective was revealed by performing the Moran’s index on GeoDa software to determine the spatial autocorrelation among the observed areas as the basis in finding the leading industrial cluster. The analysis indicated that all leading industries have relatively low Moran’s index meaning there was no dominant leading industry in Surakarta. These results have been confirmed by the LISA method to reveal the areas having spatial autocorrelation for each industrial sector.

From the field-scale Aquacrop model to a regional gridded crop model: initial evaluation over Europe

2020 ◽  
Author(s):  
Shannon de Roos ◽  
Gabriëlle de Lannoy ◽  
Dirk Raes
Keyword(s):  
Soil Moisture ◽  
Crop Production ◽  
Cropping Systems ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Crop Model ◽  
Crop Models ◽  
Soil Database ◽  
Aquacrop Model ◽  
Spatially Distributed

<p>The pressure on soil and water resources to support the demand for crop production calls for effective water management at the regional scale and a need for regional crop models.</p><p>In our study, the field-based Aquacrop v.6.1 is modified to a gridded crop model that is run spatially over the main part of Europe at 1-km resolution.</p><p>The gridded model simulates spatially distributed soil moisture, crop biomass and yield, given spatial input of meteorological forcings extracted from the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) and 1-km soil texture information from the Harmonized World Soil Database v1.2 (HWSD v1.2). For the first model evaluation, a hypothetical and uniform crop is implemented, and field management and irrigation practices are not included. We will present preliminary results over Europe by comparing the spatial soil moisture and biomass simulations with remote sensing data.</p><p>This work is part of the SHui project, a H2020 project that aims at improving stakeholder decision-making for water scarcity management in European and Chinese cropping systems.</p>

Spatial data analysis for exploration of regional scale geothermal resources

2013 ◽  
Vol 266 ◽  
pp. 69-83 ◽  
Author(s):  
Majid Kiavarz Moghaddam ◽  
Younes Noorollahi ◽  
Farhad Samadzadegan ◽  
Mohammad Ali Sharifi ◽  
Ryuichi Itoi
Keyword(s):  
Data Analysis ◽  
Spatial Data ◽  
Regional Scale ◽  
Spatial Data Analysis ◽  
Geothermal Resources

scholarly journals Spatial-Temporal Analysis of the Economic and Environmental Coordination Development Degree in Liaoning Province

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Hui Wang ◽  
Fan Liu ◽  
Ying Yuan ◽  
Liang Wang
Keyword(s):  
Spatial Data ◽  
Temporal Analysis ◽  
Spatial Dimension ◽  
Spatial Data Analysis ◽  
Liaoning Province ◽  
Time Dimension ◽  
Temporal Dimension ◽  
Northwestern Region ◽  
Data Points ◽  
Coordination Development

This study selects 20 indices of economic and environmental conditions over 15 years (1996–2010) for 14 cities in Liaoning province, China. We calculate the economic score and environmental score of each city by processing 4200 data points through SPSS 16.0 and establish synthesis functions between the economy and the environment. For the time dimension, we study the temporal evolution of the economic and environmental coordination development degree . Based on Exploratory Spatial Data Analysis (ESDA) techniques and using GeoDa, we calculate Moran's index of local spatial autocorrelation and explore the spatial distribution character of in Liaoning province through a LISA cluster map. As we found in the temporal dimension, the results show that of the 14 cities has been rising for 15 years and that increases year by year, which indicates that the economic and environmental coordination development condition has been improving from disorder to highly coordinated. A smaller gap between economic strength and environmental carrying capacity in Liaoning province exists, which means that economic development and environmental protection remain synchronized. In the spatial dimension, the highly coordinated cities have changed from a scattering to a concentration in the middle-south region of Liaoning province. Poorly coordinated cities are scattered in the northwestern region of Liaoning province.

scholarly journals Event Detection and Spatio-temporal Analysis of Low-Altitude Unstable Approach

2020 ◽  
Vol 10 (14) ◽  
pp. 4934
Author(s):  
Huabo Sun ◽  
Jiayi Xie ◽  
Yang Jiao ◽  
Rongshun Huang ◽  
Binbin Lu
Keyword(s):  
Spatial Distribution ◽  
Correlation Coefficient ◽  
Spatial Data ◽  
Mainland China ◽  
Temporal Analysis ◽  
Spatial Data Analysis ◽  
Technical Program ◽  
Low Altitude ◽  
Spatio Temporal ◽  
Bad Weather

Low-altitude unstable approach (UA) is one of the crucial risks that threaten flight safety. In this study, we proposed a technical program for detecting low-altitude UA events. The detection logic was to optimize the step-wise regression model with iterative surveys with more than 20 experienced pilots. Accordingly, the frequencies of UA events occurring around each airport in January 2018 were calculated for all the airports within mainland China. Finally, the spatial distribution characteristics of UA events were analyzed via exploratory spatial data analysis. In addition, Pearson’s correlation coefficient and the geographically weighted correlation coefficient were used to explore the correlations between UA frequency and the altitude elevation, wind level, and bad weather. The experimental results revealed that the proposed method can accurately detect the occurrence of low-altitude UA and quantitatively characterize risks. It was found that UA exhibits obvious differences in spatial distribution. Moreover, significantly strong correlations were found between UA and altitude elevation, wind level, and bad weather, and correlation differences were also reflected in different regions in China.

scholarly journals Social Disorganization and Urban Homicide Rates: A Spatial-Temporal Analysis in São Paulo, Brazil 2000 to 2015

2021 ◽  
pp. 108876792110108
Author(s):  
Amy Nivette ◽  
Maria Fernanda Tourinho Peres
Keyword(s):  
Spatial Data ◽  
Social Disorganization ◽  
Temporal Patterns ◽  
Temporal Analysis ◽  
Sao Paulo ◽  
Spatial Data Analysis ◽  
São Paulo ◽  
Spatial And Temporal Patterns ◽  
Homicide Rates ◽  
Regression Techniques

This study aims to contribute to understanding urban spatial and temporal patterns of social disorganization and homicide rates in São Paulo, Brazil (2000–2015). Using exploratory spatial data analysis and spatial panel regression techniques, we describe spatial-temporal patterns of homicide rates and assess to what extent social disorganization can explain between-district variation in homicide trajectories. The results showed some variation in the pattern of homicide decline across districts, and less disorganized communities experienced earlier, more linear declines. However, we found no evidence to suggest that changes in social disorganization are associated with differences in the decline in homicide rates.

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