4 edition of Estimation of changes in spatial interaction using incremental growth found in the catalog.
Estimation of changes in spatial interaction using incremental growth
Thesis (M.A.Sc.) -- University of Toronto, 1994.
|Series||Canadian theses = -- Thèses canadiennes|
|The Physical Object|
|Pagination||2 microfiches : negative. --|
As is well‐known, the spatial lag variable is endogenous to the model since it implies simultaneous spatial interaction. However, it is surprising to note that the analysis of the effects of other endogenous variables has been rather neglected. 1 1 Some exceptions include Kelejian and Prucha (, ), Fingleton and Le Gallo ( Urban spatial planning is described one of the crucial tools in attaining economic development of a place (Fainstein, ; Faludi, ). Healey et. al. ( 4) summarize the interaction between spatial planning and socio-economic development by explaining the double-edged interaction between spatial planning and dynamics.
Spatial variation studies of soil hydraulic properties in a part of Pavanje river basin using ordinary kriging method 2 Soil plays a key role in water retention and storage. Water movement in soils occurs as a liquid flow in saturated soils, and as a liquid and vapour flow in unsaturated soils. Past studies have shown that changes in the house price of a region may transmit to its neighbouring regions. The transmission mechanism may follow spatial and temporal diffusion processes. This paper investigates such regional housing market dynamics and interactions among local housing sub-markets in Taipei. The analysis is based on a panel data framework and spatial panel models using.
Abstract. This paper uses Bayesian model comparison methods to simultaneously specify both the spatial weight structure and explanatory variables for a spatial growth regression involving NUTS 2 regions across 25 European countries. Storm, H., and T. Heckelei (): Using Multiple Neighboring Interaction Effects in Spatial Regression Specifications to Reduce Omitted Variable Bias. Paper presented at the 56th Annual Conference of the German Association of Agricultural Economists, Bonn, Germany, September ,
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Autoregressive models for spatial interaction have been proposed by several authors (Whittle  and Mead , for example). In the past, computational difficulties with the ML approach have led to the use of alternative estimators.
In this article, a simplified computational scheme is given and extended to mixed regressive-autoregressive by: 5. Estimation of spatial interaction models and calculation of The low parameter values suggest that a change and without competition eﬀect are presented, by using the inverse of the spatial interaction models’ balancing factors as discussed in chapter 4.
Downloadable. This paper presents an environmental application, investigating land use changes of forests and semi-natural areas in the Greek region of Western Attica. Its objective is to estimate the spatial equilibrium distribution of individual deforestation actions and determine the degree of coordination in individual behaviour.
For this purpose, the paper starts by creating a virtual. It is desirable to estimate the importance of each auxiliary variable endogenously.
Given this background, this section introduces the results of Murakami and Yamagata (), which estimates fine-scale socioeconomic scenarios using a spatial econometric approach considering spatial and economic interactions.
In this chapter, we downscaled the Author: Daisuke Murakami, Yoshiki Yamagata. Spatial interaction involves a wide range of ﬂ ows between nodes: these include human movements (e.g., migration, commuting) and movement of.
Modeling the Change of Urban Spatial Structure: Use Interzonal Travel Data to Estimate Urban Growth and Expansion by Hierarchical Cluster Analyses: /ch The Urban spatial structure is affected by spatial interactions among various activity locations, and land uses in the city over the transportation system.
Estimate Urban Growth and Expansion by Modeling Urban Spatial Structure Using Hierarchical Cluster Analyses of Interzonal Travel Data: /ch Estimating the spatial organization of cities yields insights into interactions over a spatial structure, and thus creating efficient subcenters with more.
The spatial interaction model applied in the case study is based on empirically obtained parameters and, amongst others, the assumptions that: (1) increasing interaction opportunities cause growth. 2 A Unified Approach to the Spatial Growth Regression Model Specification.
In Section we provide a theoretical motivation for use of the SDM specification based on work by Pace & LeSage (). Specifics relating the SDM model to non-spatial growth regressions as well as theoretical implications are set out in Section Property booms have caused residential spatial distribution changes in many metropolitan areas.
To understand the impact of surging housing prices on the low-to-moderate income group, this study uses transit smart card data to explore whether and to what extent increasing housing affordability pressure impacts the pattern of residential spatial distribution.
Methods for the parameter estimation for a spatio-temporal marked point process model, the so-called growth-interaction model, are investigated. Least squares estimation methods for this model found in the literature are only concerned with fitting the mark distribution observed in the data.
These methods are unable to distinguish between models which have the same birth, death, interaction. SPATIAL INTERACTION IS A dynamic flow process from one location to another. It is a general concept that may refer to the movement of human beings such as intraurban commuters or intercontinental migrants, but may also refer to traffic in goods such as raw.
This paper presents a new spatial interaction modelling framework for estimating subnational, international migration flows within Europe.
We introduce a several-stage model which incorporates constraints at two geographical levels and produces estimates for a full matrices of interregional flows which adhere to known flows between countries in the EU system between and Estimation and Interpretation JAMES P.
LeSAGE & MANFRED M. FISCHER (Received November ; accepted May ) ABSTRACT We attempt to clarify a number of points regarding use of spatial regression models for regional growth analysis.
We show that as in the case of non-spatial growth regressions, the effect of. Downloadable (with restrictions). Domestic energy policies destined to foster the use of end-use electric technologies could cause rapid penetration of new residential loads and, consequently, this could cause a significant increase in the demand for electricity in urban areas.
This paper presents a spatial-temporal growth model for estimating the adoption of new end-use electric technologies. Estimation of GDP from traffic flows.
The relationship between the fitted city GDP values using the multiple linear regression (MLR) models of transportation features and the actual city GDP in three provinces (i.e., Liaoning, Jiangsu, and Shaanxi) are summarized in Fig. shows that simple transportation flow features (i.e., intra-city and inter-city flows of cars, buses, and trucks.
sense that any asymmetric interaction table can be made to yield a unique distance estimate to be used in further computations involving locations. Two difficulties remain. First, only one interaction model has been examined. Secondly, can a reasonable interpretation be provided for the c ij when the interaction consists of, say, telephone.
Land-use data For spatial interaction models land-use related data primarily concerns the amount of activities per zone.
These are the main determinants for the zones’ trip production and attraction. In some transport models other land-use characteristics are included in trip generation models as well, such as the type of land-use. disturbances (SARAR) model using a generalized spatial two-stage least squares to understand cross-unit interactions in a spatial dimension.
I demonstrate that there exists positive, large, and significant spatial autoregressive dependence and knowledge spillover in soybean yields among smallholder female farmers within spatial networks.
Moreover, by discovering the club convergence that spatial interactions and regional economic growth have different behaviors.
Some empirical works use Spatial Durbin Model (SDM) in the Bayesian statistics framework to investigate the per capita income convergence in the regions of Japan in time period of .
The empirical results. Spatial Interaction model. Based on their supply, demand and distance, we computed a value of potential interaction for each pair of cities, using a gravity model. This model has been used in geography since E. G. Ravenstein. It estimates accurately the expected flows between places, because it captures some obvious properties of spatial.quences of a change in a set of elements in conceptual terms may be affected by low-level changes.
Finally, a more practical benefit of incremental processing is that it dis-R. W. Ferguson, J. L. Bokor, R. L. Mappus, IV, and A. Feldman, "Maintaining spatial relations in an incremental diagrammatic reasoner," in COSITSpatial Informa-tion.Non-spatial land use models are specialized in estimating the amount of change per land use type as country or regional aggregates, while spatially-explicit models are also able to reproduce where land use changes are likely to occur, and which local land use conversions (from one land use type to another) are expected to take place.