From Spatiotemporal Smoothing to Functional Spatial Regression: a Penalized Approach

2021 ◽  
pp. 403-423
Author(s):  
Maria Durban ◽  
Dae‐Jin Lee ◽  
María del Carmen Aguilera Morillo ◽  
Ana M. Aguilera
Keyword(s):  
Spatial Regression

Spatiotemporal clustering, social inequities and the risk of leptospirosis in an endemic area of Brazil: a retrospective spatial modelling

2020 ◽  
Author(s):  
Katyucia O C de Souza ◽  
José Augusto P Góes ◽  
Matheus S Melo ◽  
Paula M G Leite ◽  
Lucas A Andrade ◽  
...  
Keyword(s):  
Urban Areas ◽  
Spatial Regression ◽  
Temporal Trends ◽  
Spatial Dynamics ◽  
Secondary Data ◽  
The State ◽  
Scan Statistics ◽  
Northeastern Brazil ◽  
Metropolitan Region ◽  
Social Inequities

Abstract Background Leptospirosis is an endemic disease in Brazil of social and economic relevance related to behavioural and socioenvironmental factors. This study aimed to analyse the spatiotemporal distribution of the incidence of leptospirosis and its association with social determinants in health in a state of northeastern Brazil. Methods An ecological study of temporal series with techniques of spatial analysis using secondary data of the cases of leptospirosis notified in the Information System of Notifiable Diseases of the state of Sergipe (2008–2017) was conducted. The analysis of temporal trends was performed using Poisson regression. Spatial analyses were performed using the Moran index, the local empirical Bayesian model, scan statistics and spatial regression. Results The incidence rate decreased from 3.66 to 1.44 cases per 100 000 inhabitants in 2008 and 2017, respectively. Leptospirosis was associated with social inequities, mostly affecting males aged 20–49 y living in urban areas. The space-time scan indicated the formation of a risk cluster in municipalities in the metropolitan region of the state. Conclusions The data indicated the persistence of leptospirosis transmission, maintaining a pattern of high endemicity in some municipalities associated with social inequities. The study showed the temporal and spatial dynamics of the disease to better target specific actions for prevention and control.

scholarly journals The Impacts of the Geographic Distribution of Manufacturing Plants on Groundwater Withdrawal in China

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1158
Author(s):  
Yanting Zheng ◽  
Huidan Yang ◽  
Jinyuan Huang ◽  
Linjuan Wang ◽  
Aifeng Lv
Keyword(s):  
Manufacturing Industry ◽  
Tap Water ◽  
Demand Management ◽  
Spatial Regression ◽  
High Water ◽  
Groundwater Withdrawal ◽  
Ecological Environment ◽  
Manufacturing Plants ◽  
Water Users ◽  
Water Transmission

The overexploitation of groundwater in China has raised concern, as it has caused a series of environmental and ecological problems. However, far too little attention has been paid to the relationship between groundwater use and the spatial distribution of water users, especially that of manufacturing factories. In this study, a factory scatter index (FSI) was constructed to represent the spatial dispersion degree of manufacturing factories in China. It was found that counties and border areas between neighboring provinces registered the highest FSI increases. Further non-spatial and spatial regression models using 205 provincial-level secondary river basins in China from 2016 showed that the scattered distribution of manufacturing plants played a key role in groundwater withdrawal in China, especially in areas with a fragile ecological environment. The scattered distribution of manufacturing plants raises the cost of tap water transmission, makes monitoring and supervision more difficult, and increases the possibility of surface water pollution, thereby intensifying groundwater withdrawal. A reasonable spatial adjustment of manufacturing industry through planning and management can reduce groundwater withdrawal and realize the protection of groundwater. Our study may provide a basis for water-demand management through spatial adjustment in areas with high water scarcity and a fragile ecological environment.

scholarly journals Measuring Highway Impacts on House Prices Using Spatial Regression

2015 ◽  
Vol 7 (1) ◽  
pp. 83-98
Author(s):  
Marcus T. Allen ◽  
Grant W. Austin ◽  
Mushfiq Swaleheen
Keyword(s):  
House Prices ◽  
Spatial Regression

scholarly journals Spatial analysis for political scientists

2021 ◽  
pp. 1-17
Author(s):  
Jessica Di Salvatore ◽  
Andrea Ruggeri
Keyword(s):  
Comparative Politics ◽  
Spatial Data ◽  
Spatial Clustering ◽  
Statistical Tests ◽  
Spatial Regression ◽  
Spatial Models ◽  
Building Blocks ◽  
Spatial Relationships ◽  
Electoral Studies ◽  
Data Points

Abstract How does space matter in our analyses? How can we evaluate diffusion of phenomena or interdependence among units? How biased can our analysis be if we do not consider spatial relationships? All the above questions are critical theoretical and empirical issues for political scientists belonging to several subfields from Electoral Studies to Comparative Politics, and also for International Relations. In this special issue on methods, our paper introduces political scientists to conceptualizing interdependence between units and how to empirically model these interdependencies using spatial regression. First, the paper presents the building blocks of any feature of spatial data (points, polygons, and raster) and the task of georeferencing. Second, the paper discusses what a spatial matrix (W) is, its varieties and the assumptions we make when choosing one. Third, the paper introduces how to investigate spatial clustering through visualizations (e.g. maps) as well as statistical tests (e.g. Moran's index). Fourth and finally, the paper explains how to model spatial relationships that are of substantive interest to some of our research questions. We conclude by inviting researchers to carefully consider space in their analysis and to reflect on the need, or the lack thereof, to use spatial models.

scholarly journals Inequities in access to mammographic screening in Brazil

2019 ◽  
Vol 35 (6) ◽  
Author(s):  
Mário Círio Nogueira ◽  
Vívian Assis Fayer ◽  
Camila Soares Lima Corrêa ◽  
Maximiliano Ribeiro Guerra ◽  
Bianca De Stavola ◽  
...  
Keyword(s):  
Health Care ◽  
Access To Health Care ◽  
Spatial Regression ◽  
Healthcare Services ◽  
Mammographic Screening ◽  
Services Utilization ◽  
Units Of Analysis ◽  
Marked Variability ◽  
Screening Coverage ◽  
Multivariable Analyses

Abstract: Our objectives with this study were to describe the spatial distribution of mammographic screening coverage across small geographical areas (micro-regions) in Brazil, and to analyze whether the observed differences were associated with spatial inequities in socioeconomic conditions, provision of health care, and healthcare services utilization. We performed an area-based ecological study on mammographic screening coverage in the period of 2010-2011 regarding socioeconomic and healthcare variables. The units of analysis were the 438 health micro-regions in Brazil. Spatial regression models were used to study these relationships. There was marked variability in mammographic coverage across micro-regions (median = 21.6%; interquartile range: 8.1%-37.9%). Multivariable analyses identified high household income inequality, low number of radiologists/100,000 inhabitants, low number of mammography machines/10,000 inhabitants, and low number of mammograms performed by each machine as independent correlates of poor mammographic coverage at the micro-region level. There was evidence of strong spatial dependence of these associations, with changes in one micro-region affecting neighboring micro-regions, and also of geographical heterogeneities. There were substantial inequities in access to mammographic screening across micro-regions in Brazil, in 2010-2011, with coverage being higher in those with smaller wealth inequities and better access to health care.

Governance and Regional Variation of Homicide Rates

2016 ◽  
Vol 61 (1) ◽  
pp. 25-45 ◽  
Author(s):  
Liqun Cao ◽  
Yan Zhang
Keyword(s):  
Spatial Regression ◽  
Sub Saharan Africa ◽  
Development Economic ◽  
Data Sets ◽  
Asian Countries ◽  
Ceteris Paribus ◽  
Region Data ◽  
Sub Saharan ◽  
Criminological Theories ◽  
Cross National

Criminological theories of cross-national studies of homicide have underestimated the effects of quality governance of liberal democracy and region. Data sets from several sources are combined and a comprehensive model of homicide is proposed. Results of the spatial regression model, which controls for the effect of spatial autocorrelation, show that quality governance, human development, economic inequality, and ethnic heterogeneity are statistically significant in predicting homicide. In addition, regions of Latin America and non-Muslim Sub-Saharan Africa have significantly higher rates of homicides ceteris paribus while the effects of East Asian countries and Islamic societies are not statistically significant. These findings are consistent with the expectation of the new modernization and regional theories.

Field level digital soil mapping of cation exchange capacity using electromagnetic induction and a hierarchical spatial regression model

Soil Research ◽  
2009 ◽  
Vol 47 (7) ◽  
pp. 651 ◽  
Author(s):  
John Triantafilis ◽  
Scott Mitchell Lesch ◽  
Kevin La Lau ◽  
Sam Mostyn Buchanan
Keyword(s):  
Regression Model ◽  
Cation Exchange Capacity ◽  
Spatial Regression ◽  
Soil Management ◽  
Exchange Capacity ◽  
Ancillary Data ◽  
Trend Surface ◽  
Spatial Regression Model ◽  
Field Level ◽  
Mlr Model

At the field level the demand for spatial information of soil properties is rapidly increasing owing to its requirements in precision agriculture and soil management. One of the most important properties is the cation exchange capacity (CEC, cmol(+)/kg) because it is an index of the shrink–swell potential and hence is a measure of soil structural resilience to tillage. However, CEC is time-consuming and expensive to measure. Various ancillary datasets and statistical methods can be used to predict CEC, but there is little scientific literature which implements this approach to map CEC or addresses the issue of the amount of ancillary data required to maximise precision and minimise bias of spatial prediction at the field level. We compare a standard least-squares multiple linear regression (MLR) model which includes 2 proximally sensed (EM38 and EM31), 3 remotely sensed (Red, Green and Blue spectral brightness), and 2 trend surface (Easting and Northing) variables as ancillary data or independent variables, and a stepwise MLR model which only includes the statistically valid EM38 signal data and the Easting trend surface vector. The latter is used as the basis for developing a hierarchical spatial regression model to predict CEC. The reliability of the model is analysed by comparing prediction precision (root mean square error) and bias (mean error) using degraded EM38 transect spacing (i.e. 96, 144, 192, 240, and 288 m) and comparing these with predictions achieved with the 48-m spacing. We conclude that the EM38 data available on the 96- and 144-m spacing are suitable at a reconnaissance level (i.e. broad-scale farming) and 24- or 48-m spacing are suitable at smaller levels where detailed information is necessary for siting the location of water reservoirs. In terms of soil management, CEC predictions determine where suitable subsoil exists for the purpose of soil profile inversion to improve the structural resilience of a topsoil that is susceptible to dispersion and surface crusting.

scholarly journals Three-dimensional modelling using spatial regression machine learning and hydrogeological basement VES

2021 ◽  
Vol 156 ◽  
pp. 104907
Author(s):  
Gastón M. Mendoza Veirana ◽  
Santiago Perdomo ◽  
Jerónimo Ainchil
Keyword(s):  
Machine Learning ◽  
Spatial Regression ◽  
Three Dimensional
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