Modeling Crop Yield in West‐African Rainfed Agriculture Using Global and Local Spatial Regression

Muhammad Imran; Raul Zurita‐Milla; Alfred Stein

doi:10.2134/agronj2012.0370

Leveraging insurance customer data to characterize socioeconomic indicators of Swiss municipalities

PLoS ONE ◽

10.1371/journal.pone.0246785 ◽

2021 ◽

Vol 16 (3) ◽

pp. e0246785

Author(s):

Lorenzo Donadio ◽

Rossano Schifanella ◽

Claudia R. Binder ◽

Emanuele Massaro

Keyword(s):

Public Transportation ◽

Wide Spectrum ◽

Spatial Regression ◽

User Profile ◽

Location Based Services ◽

Socioeconomic Indicators ◽

Customer Data ◽

Spatial And Temporal Scales ◽

Socioeconomic Data ◽

Global And Local

The availability of reliable socioeconomic data is critical for the design of urban policies and the implementation of location-based services; however, often, their temporal and geographical coverage remain scarce. We explore the potential for insurance customers data to predict socioeconomic indicators of Swiss municipalities. First, we define a features space by aggregating at city-level individual customer data along several behavioral and user profile dimensions. Second, we collect official statistics shared by the Swiss authorities on a wide spectrum of categories: Population, Transportation, Work, Space and Territory, Housing, and Economy. Third, we adopt two spatial regression models exploring both global and local geographical dependencies to investigate their predictability. Results show consistently a correlation between insurance customer characteristics and official socioeconomic indexes. Performance fluctuates depending on the category, with values of R2 > 0.6 for several target variables using a 5-fold cross validation. As a case study, we focus on predicting the percentage of the population using public transportation and we discuss the implications on a regional scope. We believe that this methodology can support official statistical offices and it could open up new opportunities for the characterization of socioeconomic traits at highly-granular spatial and temporal scales.

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From rainfed agriculture to stress-avoidance irrigation: II. Sustainability, crop yield, and profitability

Advances in Water Resources ◽

10.1016/j.advwatres.2010.11.011 ◽

2011 ◽

Vol 34 (2) ◽

pp. 272-281 ◽

Cited By ~ 25

Author(s):

Giulia Vico ◽

Amilcare Porporato

Keyword(s):

Crop Yield ◽

Rainfed Agriculture ◽

Stress Avoidance

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COVID-19 in Toronto: A Spatial Exploratory Analysis

Sustainability ◽

10.3390/su13020498 ◽

2021 ◽

Vol 13 (2) ◽

pp. 498

Author(s):

Eric Vaz

Keyword(s):

Geographical Information Systems ◽

Spatial Regression ◽

Open Data ◽

Global Scale ◽

Geographical Information ◽

Disease Spread ◽

Modeling Framework ◽

Large Cities ◽

Monitoring Strategies ◽

Global And Local

COVID-19 has had a significant impact on a global scale. Evident signs of spatial-explicit characteristics have been noted. Nevertheless, publicly available data are scarce, impeding a complete picture of the locational impacts of COVID-19. This paper aimed to assess, confirm, and validate several geographical attributes of the geography of the pandemic. A spatial modeling framework defined whether there was a clear spatial profile to COVID-19 and the key socio-economic characteristics of the distribution in Toronto. A stepwise backward regression model was generated within a geographical information systems framework to establish the key variables influencing the spread of COVID-19 in Toronto. Further to this analysis, spatial autocorrelation was performed at the global and local levels, followed by an error and lag spatial regression to understand which explanatory framework best explained disease spread. The findings support that COVID-19 is strongly spatially explicit and that geography matters in preventing spread. Social injustice, infrastructure, and neighborhood cohesion are evident characteristics of the increasing spread and incidence of COVID-19. Mitigation of incidents can be carried out by intertwining local policies with spatial monitoring strategies at the neighborhood level throughout large cities, ensuring open data and adequacy of information management within the knowledge chain.

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Multi-Reservoir Water Quality Mapping from Remote Sensing Using Spatial Regression

Sustainability ◽

10.3390/su13116416 ◽

2021 ◽

Vol 13 (11) ◽

pp. 6416

Author(s):

Hone-Jay Chu ◽

Yu-Chen He ◽

Wachidatin Nisa’ul Chusnah ◽

Lalu Muhamad Jaelani ◽

Chih-Hua Chang

Keyword(s):

Water Quality ◽

Goodness Of Fit ◽

Spatial Regression ◽

Local Model ◽

Estimation Accuracy ◽

Local Models ◽

Spatially Varying ◽

Global And Local ◽

Water Quality Mapping ◽

Chla Concentration

Regional water quality mapping is the key practical issue in environmental monitoring. Global regression models transform measured spectral image data to water quality information without the consideration of spatially varying functions. However, it is extremely difficult to find a unified mapping algorithm in multiple reservoirs and lakes. The local model of water quality mapping can estimate water quality parameters effectively in multiple reservoirs using spatial regression. Experiments indicate that both models provide fine water quality mapping in low chlorophyll-a (Chla) concentration water (study area 1; root mean square error, RMSE: 0.435 and 0.413 mg m−3 in the best global and local models), whereas the local model provides better goodness-of-fit between the observed and derived Chla concentrations, especially in high-variance Chla concentration water (study area 2; RMSE: 20.75 and 6.49 mg m−3 in the best global and local models). In-situ water quality samples are collected and correlated with water surface reflectance derived from Sentinel-2 images. The blue-green band ratio and Maximum Chlorophyll Index (MCI)/Fluorescence Line Height (FLH) are feasible for estimating the Chla concentration in these waterbodies. Considering spatially-varying functions, the local model offers a robust approach for estimating the spatial patterns of Chla concentration in multiple reservoirs. The local model of water quality mapping can greatly improve the estimation accuracy in high-variance Chla concentration waters in multiple reservoirs.

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Advances in precision agriculture in south-eastern Australia. II. Spatio-temporal prediction of crop yield using terrain derivatives and proximally sensed data

Crop and Pasture Science ◽

10.1071/cp08348 ◽

2009 ◽

Vol 60 (9) ◽

pp. 859 ◽

Cited By ~ 17

Author(s):

N. J. Robinson ◽

P. C. Rampant ◽

A. P. L. Callinan ◽

M. A. Rab ◽

P. D. Fisher

Keyword(s):

Crop Yield ◽

Precision Agriculture ◽

Spatial Regression ◽

Spatial Modelling ◽

Yield Data ◽

Temporal Prediction ◽

Eastern Australia ◽

Elevation Data ◽

Γ Ray ◽

Spatio Temporal

The effects of seasonal as well as spatial variability in yield maps for precision farming are poorly understood, and as a consequence may lead to low predictability of future crop yield. The potential to utilise terrain derivatives and proximally sensed datasets to improve this situation was explored. Yield data for four seasons between 1996 and 2005, proximal datasets including EM38, EM31, and γ-ray spectra for 2003–06, were collected from a site near Birchip. Elevation data were obtained from a Differential Global Positioning System and terrain derivatives were formulated. Yield zones developed from grain yield data and yield biomass estimations were included in this analysis. Statistical analysis methods, including spatial regression modelling, discriminant analysis via canonical variates analysis, and Bayesian spatial modelling, were undertaken to examine predictive capabilities of these datasets. Modelling of proximal data in association with crop yield found that EM38h, EM38v, and γ-ray total count were significantly correlated with yield for all seasons, while the terrain derivatives, relative elevation, slope, and elevation, were associated with yield for one season (1996, 1998, or 2005) only. Terrain derivatives, aspect, and profile and planimetric curvature were not associated with yield. Modest predictions of crop yield were established using these variables for the 1996 yield, while poor predictions were established in modelling yield zones.

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Sunspot Groups as Tracers of Sub-Surface Processes

International Astronomical Union Colloquium ◽

10.1017/s0252921100064393 ◽

2000 ◽

Vol 179 ◽

pp. 155-160

Author(s):

M. H. Gokhale

Keyword(s):

Magnetic Flux ◽

Indian Institute ◽

Surface Processes ◽

The Sun ◽

Global And Local ◽

Sunspot Groups

AbstractData on sunspot groups have been quite useful for obtaining clues to several processes on global and local scales within the sun which lead to emergence of toroidal magnetic flux above the sun’s surface. I present here a report on such studies carried out at Indian Institute of Astrophysics during the last decade or so.

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