The spatial dynamics of neighborhood change: exploring spatial dependence in neighborhood housing value change

2016 ◽  
Vol 32 (6) ◽  
pp. 717-741 ◽  
Author(s):  
Hee-Jung Jun
Keyword(s):  
Spatial Dependence ◽  
Spatial Dynamics ◽  
Neighborhood Change ◽  
Value Change ◽  
Housing Value

scholarly journals Spatial variability of soil attributes in an experimental basin in the semi-arid region of Pernambuco, Brazil

2018 ◽  
Vol 22 (1) ◽  
pp. 38-44 ◽  
Author(s):  
Diego C. dos S. Araújo ◽  
Suzana M. G. L. Montenegro ◽  
Abelardo A. de A. Montenegro ◽  
Valdemir de P. e Silva Junior ◽  
Sylvana M. dos Santos
Keyword(s):  
Bulk Density ◽  
Spatial Dependence ◽  
Arid Region ◽  
Spatial Dynamics ◽  
Chemical Properties ◽  
Soil Attributes ◽  
Semi Arid Region ◽  
Physical And Chemical ◽  
Experimental Basin ◽  
Semi Arid

ABSTRACT This study aimed to evaluate the spatial patterns of physical and chemical properties of a soil in an experimental basin with an area of 149 km2, located in the municipality of Pesqueira, in the semi-arid region of Pernambuco state (Brazil). Samples were taken in the layers of 0-0.10 and 0.10-0.20 m, at 35 monitoring points distributed in an irregular grid, encompassing the three soil classes present in the basin and commonly found in the semi-arid region. For the attributes of soil texture, organic carbon and bulk density in the layer of 0.10-0.20 m, spatial dependence from medium to strong was detected, ranging from 0.03 to 49.98%. Bulk density in the layer 0-0.10 m, as well as the electrical conductivity, in both layers, showed pure nugget effect, possibly due to insufficient sampling distance to capture spatial dependence. However, geostatistics represented well the spatial dynamics of soil attributes at points where the mapping is possible.

Exploratory data analysis to identify factors influencing spatial distributions of weed seed banks

Weed Science ◽  
2004 ◽  
Vol 52 (6) ◽  
pp. 936-947 ◽  
Author(s):  
L. Wiles ◽  
M. Brodahl
Keyword(s):  
Cluster Analysis ◽  
Seed Bank ◽  
Spatial Dependence ◽  
Management Practices ◽  
Spatial Dynamics ◽  
Seed Banks ◽  
Spatial Distributions ◽  
Weed Seed ◽  
Explanatory Variables ◽  
Natural Dispersal

Comparing distributions among fields, species, and management practices will help us understand the spatial dynamics of weed seed banks, but analyzing observational data requires nontraditional statistical methods. We used cluster analysis and classification and regression tree analysis (CART) to investigate factors that influence spatial distributions of seed banks. CART is a method for developing predictive models, but it is also used to explain variation in a response variable from a set of possible explanatory variables. With cluster analysis, we identified patterns of variation with direction of the distance over which seed bank density was correlated (range of spatial dependence) with single-species seed banks in corn. Then we predicted patterns of the seed banks with CART using field and species characteristics and seed bank density as explanatory variables. Patterns differed by magnitude of variation in the range of spatial dependence (strength of anisotropy) and direction of the maximum range. Density and type of irrigation explained the most variation in pattern. Long ranges were associated with large seed banks and stronger anisotropy with furrow than center pivot irrigation. Pattern was also explained by seed size and longevity, characteristics for natural dispersal, species, soil texture, and whether the weed was a grass or broadleaf. Significance of these factors depended on density or type of irrigation, and some patterns were predicted for more than one combination of factors. Dispersal was identified as a primary process of spatial dynamics and pattern varied for seed spread by tillage, wind, or natural dispersal. However, demographic characteristics and density were more important in this research than in previous research. Impact of these factors may have been clearer because interactions were modeled. Lack of data will be the greatest obstacle to using comparative studies and CART to understand the spatial dynamics of weed seed banks.

scholarly journals SPATIAL DISTRIBUTION OF SPONTANEOUS PRODUCTION OF Attalea maripa (Aubl) Mart. IN THE LOW TOCANTINS

2017 ◽  
Vol 39 (3) ◽  
Author(s):  
RAIMUNDO DIONÍZIO PINTO BRITO ◽  
PAULO ROBERTO DA SILVA FARIAS ◽  
NARA ELISA LOBATO RODRIGUES ◽  
FABIO JÚNIOR DE OLIVEIRA ◽  
RENATO ALVES TEXEIRA
Keyword(s):  
Spatial Distribution ◽  
Spatial Dependence ◽  
Alternative Energy ◽  
Crop Production ◽  
Visual Analysis ◽  
Spatial Dynamics ◽  
Spherical Model ◽  
Spontaneous Growth ◽  
Alternative Energy Sources ◽  
Palm Trees

ABSTRACT The production of biofuels from native Amazonian palm trees, belonging to the family of Arecaceae, like Attalea maripa (Aubl) Mart. (Maripa palm), are objects of studies of alternative energy sources for the biofuels production. Thus, the aim of this research was to evaluate the spatial dynamics of agronomic variables of Maripa palm crop production associated with the characteristics of the sampling sites in a spontaneous growth area in the Low Tocantins region. 100 palms of A. maripa were selected, of spontaneous growth and in production phase, on a 10 ha area in the municipality of Cametá / PA, in which the morpho-agronomical variables were collected (MIN, FIN, ABN, LN, DBH, AWAB and AFW), from January 2014 to June 2014. The semivariograms were produced for each variable, which presented a spatial dependence range of 75 meters to MIN, FIN, AWAB and AFW, 80 meters to LN and DBH and 98 meters to ABN. The area of reach found was 17,671.5 m² for MIN, FIN, AWAB and AFW variables, 20,106.2 m² for LN and DBH and 30,171.9 m² for ABN. The data of the variables were adjusted to the spherical model presenting determination coefficient (R²) that ranged from 0.57 to 0.99. The spatial dependence index (SDI) for MIN and FIN variables was 0.49 and 0.31, respectively, indicating moderate spatial dependence. For ABN, LN, DBH and AWAB variables ranged from 0.17 to 0.22, resulting in poor spatial dependence. The AFW variable presented SDI of 0.77. For the visual analysis of the spatial distribution of the studied variables, kriging maps were created.

Bells in Space

2016 ◽  
Vol 41 (2) ◽  
pp. 152-182 ◽  
Author(s):  
Sergio J. Rey
Keyword(s):  
Spatial Dependence ◽  
Spatial Dynamics ◽  
Us States ◽  
Income Inequalities ◽  
Interregional Inequality ◽  
Cold Spots ◽  
Per Capita ◽  
Spatial Outliers ◽  
Global Spatial Autocorrelation ◽  
Shaped Pattern

Social and interregional inequality patterns across US states from 1929–2012 are analyzed using exploratory space–time methods. The results suggest complex spatial dynamics for both inequality series that were not captured by the stylized model of Alonso. Interpersonal income inequalities of states displayed a U-shaped pattern ending the period at levels that exceeded the alarmingly high patterns that existed in the 1920s. Social inequality is characterized by greater mobility than that found for state per capita incomes. Spatial dependence is also distinct between the two series, with per capita incomes exhibiting strong global spatial autocorrelation, while state interpersonal income inequality does not. Local hot and cold spots are found for the per capita income series, while local spatial outliers are found for state interpersonal inequality. Mobility in both inequality series is found to be influenced by the local spatial context of a state.

Acculturation and value change: Moroccan immigrants in Spain

2005 ◽  
Author(s):  
Veronica Benet-Martinez ◽  
Maria Jose Sotelo ◽  
Manolo Munoz
Keyword(s):  
Value Change

Sozial-räumliche Dynamiken der Agrartreibstoffe.

2016 ◽  
Vol 46 (184) ◽  
pp. 423-440 ◽  
Author(s):  
Kristina Dietz ◽  
Bettina Engels ◽  
Oliver Pye
Keyword(s):  
Political Economy ◽  
Capital Flows ◽  
Spatial Dynamics ◽  
Financial Sector ◽  
Comprehensive Analysis ◽  
Spatial Theory ◽  
Global Political Economy

This article explores the spatial dynamics of agrofuels. Building on categories from the field of critical spatial theory, it shows how these categories enable a comprehensive analysis of the spatial dynamics of agrofuels that links the macro-structures of the global political economy to concrete, place-based struggles. Four core socio-spatial dynamics of agrofuel politics are highlighted and applied to empirical findings: territorialization, the financial sector as a new scale of regulation, place-based struggles and transnational spaces of resources and capital flows.

Convergence of regional population density in Russia over 120 years

2020 ◽  
pp. 133-158
Author(s):  
K. A. Kholodilin ◽  
Y. I. Yanzhimaeva
Keyword(s):  
Population Density ◽  
Spatial Dependence ◽  
Population Distribution ◽  
Regional Level ◽  
Capital City ◽  
County Level ◽  
Spatial Effects ◽  
Russian Regions ◽  
Regional Population ◽  
Regional Population Density

A relative uniformity of population distribution on the territory of the country is of importance from socio-economic and strategic perspectives. It is especially important in the case of Russia with its densely populated West and underpopulated East. This paper considers changes in population density in Russian regions, which occurred between 1897 and 2017. It explores whether there was convergence in population density and what factors influenced it. For this purpose, it uses the data both at county and regional levels, which are brought to common borders for comparability purposes. Further, the models of unconditional and conditional β-convergence are estimated, taking into account the spatial dependence. The paper concludes that the population density equalization took place in 1897-2017 at the county level and in 1926—1970 at the regional level. In addition, the population density increase is shown to be influenced not only by spatial effects, but also by political and geographical factors such as climate, number of GULAG camps, and the distance from the capital city.

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