Spatial Modeling

Long-term modeling of agricultural land use is central in global scale assessments of climate change, food security, biodiversity, and climate adaptation and mitigation policies. We present a global-scale dynamic land use allocation model and show that it can reproduce the broad spatial features of the past 100 years of evolution of cropland and pastureland patterns. The modeling approach integrates economic theory, observed land use history, and data on both socioeconomic and biophysical determinants of land use change, and estimates relationships using long-term historical data, thereby making it suitable for long-term projections. The underlying economic motivation is maximization of expected profits by landowners within each grid cell. The model predicts fractional land use for cropland and pastureland within each grid cell based on socioeconomic and biophysical driving factors that change with time. The model explicitly incorporates the following key features: (1) land use competition, (2) spatial heterogeneity in the nature of driving factors across geographic regions, (3) spatial heterogeneity in the relative importance of driving factors and previous land use patterns in determining land use allocation, and (4) spatial and temporal autocorrelation in land use patterns.

We show that land use allocation approaches based solely on previous land use history (but disregarding the impact of driving factors), or those accounting for both land use history and driving factors by mechanistically fitting models for the spatial processes of land use change do not reproduce well long-term historical land use patterns. With an example application to the terrestrial carbon cycle, we show that such inaccuracies in land use allocation can translate into significant implications for global environmental assessments. The modeling approach and its evaluation provide an example that can be useful to the land use, Integrated Assessment, and the Earth system modeling communities.

According to specialist literature, the current development of agglomerations is by large
urban areas evolving by the expansion and structural transition of larger urban zones,
where “the formerly hierarchic division of settlements with a different size and role is
replaced by horizontal cooperation, linking into a network” (Enyedi 2012, p. 17). The
aim of the present paper is to examine, how equal the domestic large cities and their
agglomeration can be considered by their development and competitiveness, and if there
is an economic basis for them to cooperate horizontally in a network in the present case.
The authors use different methodological approaches to examine the development and
competitiveness of cities and their agglomeration, and spatial autocorrelation
circumstances to model the economic base of outlined cooperation.

The paper is concerned with the theoretical and practical approaches to administrative
regionalisation. The authors attempt to present a new methodology, which on one hand is
theoretical and from the other, enables the design of an optimal spatial system in terms of
the demands of the population. This is presented on through the example of Hungarian
districts (the level between municipalities and counties in the administrative system).

This research is concerned with a statistical method that has recently become widespread
in the international literature; although, it is still limited in Hungarian research. The method
is geographically weighted regression (GWR), which is demonstrated through an example
of its application. GWR is a local model that is founded on the basis of regression,
prominently taking into consideration the geographical distance. Since it does not calculate
the global relations of the whole data, but concentrates on the relationship of the dependent
and independent variables locally within a determined search area, it allows consideration
of the spatially varying processes. Simply, GWR is a developed version of the global
regression model, since, through its use, it is possible to take into account the local features
that are hidden by the global approach.

The research paper emphasizes the importance of export from the aspect of economic
development. In the first part, the theoretical background of the regional development
connected to export-led growth is introduced. As a bridge between the theoretical and
empirical part, the territorial sense of export data and economic spaces is highlighted. In
connection to the research, actual domestic research examining spatial differences and
inequalities in Hungary is presented. The reviewed researches (Dusek–Lukács–Rácz
2014, Nemes Nagy–Tagai 2011, Obádovics 2013, Pénzes 2012) concur on the main
territorial features in Hungary: territorial hegemony of the capital, increasing differences
between the capital and rural areas, emergence of the ‘West–East decline’, and varied
development patterns of micro-regions and settlements. Dusek–Lukács–Rácz (2014)
describe the regional disparities in the country on NUTS-2 level, pointing out that Central
Hungary is the most advanced region of Hungary, followed by Western Transdanubia
and Central Transdanubia, and with significant lag, Southern Transdanubia, Southern
Great Plain, Northern Great Plain and Northern Hungary trail the list. In the primary
research, the aim is to explore the conditions of export-led growth in Hungary, and
through an examination of the spatial distribution of the 500 companies with the best
sales performance (TOP 500 list) an attempt is made to demonstrate the reasons for
regional inequalities in Hungary. The other scope of the research is to demonstrate the
concentration of different sectors in NUTS-3 level, as well as highlighting their export
orientation. Furthermore research examines associations between different measures. The
pool of data used for carrying out the research is derived from HVG (Heti Világgazdaság
– a dominant economic weekly review in Hungary) about the first 500 companies with
the best sales performance in 2012 (the TOP 500 companies) and from the Hungarian
Central Statistical Office. Our research findings emphasize the importance of export for
regional development (export-led growth), and resembles the secondary research outcomes
about the development of NUTS-2 and NUTS-3 regions.

The study examines how the recent economic crisis and the related unfavourable economic
features affect poverty. As economic crisis is usually associated with many economic and
social problems, it tries to determine to what extent it influences poverty. The paper
attempts to prove that economic recession contributes not only to the impoverishment of a
significant section of society, but also increases the depth of poverty significantly. If the
research supports this hypothesis, it is worth examining to what extent one percent
economic growth or economic decline can decrease or increase the rate of the poor and the
depth of poverty. Besides the effect of economic growth on the given area, the paper also
analyses the effect of the economic growth of the neighbouring areas. The initial
hypothesis states that the economic growth of the neighbouring regions can also alleviate
poverty. As for spatial effects, spatial autocorrelation is examined in the average income
level to reveal how the economic growth of the neighbouring areas affects a given region.
The study examines Northern Hungary, one of the most backward regions in Hungary
(based on GDP per capita). Eurostat (2010) reports this region is among the poorest twenty
regions within the European Union (based on GDP per capita PPP, Northern Hungary is
the 259th among the 271 regions of the European Union).

Knowledge of whether, and over what range of output, there are economies or
diseconomies of scale in providing local communal services is an important question from
a theoretical, practical and regional political point of view. The theoretical side of the
question is connected to the primary research regarding the optimal city size. If an optimal
city size can actually be established, then a valid policy argument can be made for fostering
its approximation. However, theoretical considerations are sometimes based on those types
of assumptions, which are often not valid in reality. Therefore, empirical investigations are
essential in this research area.
After a short theoretical overview, the paper initially gives a general outline of previous
controversial empirical evidence on economies of scale regarding community size and the
provision of local communal services. In the second part, the empirical findings are
presented; these concern economies of scale in local communal services based on a large
and detailed database, which consist of almost 300 Hungarian settlements. During the
research, a number of methodological questions have occurred. For example, there are
several solutions for the organisational structure providing local services, from a large
holding to the smaller individual companies. Considering this and some other issues, the
main results suggest that on a community level, there are moderate economies of scale in
water supply, sewage disposal and district heating and a moderate diseconomy of scale in
refuse collection until 5 thousand inhabitants. Above this level there is no connection
between the settlement size and the average cost of services. The results are mainly
consistent with the previous findings: there are economies of scale under a certain
threshold, but after this, unit cost reduction is not feasible. This threshold can be different
in the different types of services.

In this study, we propose a method to estimate the mean annual energy production of a wind farm with a Geographic Information System (GIS). GIS allows for spatial modelling in many fields and has recently been applied in the field of renewable energy. The geographic features of a wind park are represented using spatial data such as topography, land cover, and wind resource. Wind resource layers contain data of the 16 wind directions, in which the wind rose is divided, including the frequency of the wind direction, the mean annual wind speed and the annual Weibull parameters k and C) estimated at 50m height. The wind turbines are represented by points including information about the roughness of the surrounding terrain. Roughness is calculated within a GIS process that models the variation of the land cover over the year around the wind turbine position. The mean annual energy production is calculated coupling the technical characteristics of the wind turbines models with the wind resource. In addition, the wake effect between wind turbines has been included. A parameter called "reduced efficiency coefficient" has been introduced to assess the impact of the layout of wind farm on the annual energy production in respect to the change of the wind direction. The reduced efficiency coefficient shows that relatively regular wind farm layouts designed for exploiting the wind speed blowing from the prevailing wind direction can cause significant energy losses. In particular, when the wind comes from directions perpendicular to the prevailing one, the wind turbines waste up to 60% of the available energy. The method has been tested, comparing the actual annual energy production of four wind farms in Kansas (U.S.) with the estimated mean annual energy production. The validation demonstrated an average underestimation of 3.56% of the annual energy production and an average underestimation of 1.11% of the capacity factor. The results are encouraging and the developed process enables the quantification of the annual energy production with low uncertainties.

We analyzed whether the spatial variation in mammal species richness reflects the southern boundaries of the Nearctic region as previously established by endemism patterns. Records from 710 mammal species were drawn on a map of North America (from Canada and Alaska to Panama) gridded at 4° latitude-longitude. We evaluated the probable existence of unknown species through three richness estimators (Chao2, ICE, and Jack1), modeled the potential distribution of species, and mapped the predicted pattern of species richness through the number of coexisting potential distributions. The poorest grid cells are in the northern areas, whereas the richest ones are in the southern areas, coinciding with the pattern of collecting points. The average richness of 4° grid cells comprising the Nearctic region was 18 species, and the richest 4° grid cells had 150 species, coinciding with the 26° latitude. From the 406 mammal species of the Nearctic region, 104 are restricted to it, and 305 species situated south of it are not distributed in the region. The map of predicted richness shows the classical latitudinal diversity gradient, with the number of species increasing to the tropics. We conclude that the Nearctic region has a low mammal richness, with a richness pattern corresponding with previously described patterns of endemism, with a boundary situated at 26°–30° latitude.

this study, we propose a method to estimate the mean
annual energy production of a wind farm with a Geographic
Information System (GIS). GIS allows for spatial modelling in
many fields and has recently been applied in the field of
renewable energy. The geographic features of a wind park
are represented using spatial data such as topography, land
cover, and wind resource. Wind resource layers contain
data of the wind speed frequency, the wind direction and
the annual Weibull parameters at 50m height. The wind
turbines (WTs) are represented by points including
information about the roughness of the terrain. Roughness
is calculated within a GIS process that models the variation
of the land cover over the year around the wind turbine
position. The mean annual energy production is calculated
using the method of bins. In addition, the wake effect
between wind turbines has been included. The method has
been tested, comparing the actual annual energy
production of four wind farms in Kansas (U.S.) with the
estimated energy production. The validation demonstrated
an average underestimation of 0.15% of the AEP and an
average underestimation of 0.32 of the capacity factor. The
results are encouraging and the developed process enables
quantification of the AEP with very low uncertainties.

This study investigates the multiple and complex relationship between transport and
tourism by various methods. Features of the relationship between the transport distance
and tourism intensity will be identified and the connections between the accessibility of
European regions and their tourism will be analysed.

In this paper, the authors wish to introduce an application of the gravitational model
through a concrete example. In their investigation, the gravitational model was transformed
to analyse the impact of accessibility in a way that not only the size of gravitational forces
but their direction can also be measured. Displacements were illustrated by a bidimensional
regression, which gives a new perspective to the investigation of the
Hungarian spatial structure.

This study investigates the multiple and complex relationship between transport and
tourism by various methods. Features of the relationship between the transport distance
and tourism intensity will be identified and the connections between the accessibility of
European regions and their tourism will be analysed.

The population projections of László Hablicsek (1953–2010) have made a major scientific
contribution to understanding the demographic processes of Hungary. Its social and
economic usefulness cannot be questioned; although, trends of the past ten years indicate
the need for some corrections. The present analysis shows that, for the past ten years, the
population estimates were very close to reality. There was no significant difference
between the estimated and real data; only last year’s data showed some variation (however,
it should be noted that the projections for smaller territories are in need of significant
corrections). In recent years, regional disparities in the country rather increased. The
migration towards large cities from undeveloped areas to more developed ones raise
dissonance among regions. The population concentration proved to be stronger than the
forecast, and the analysis showed that the regional concentration of the population, as well
as the loss of population, has further reinforced the unfavourable position of
underdeveloped regions at micro-regional level. Immigration from abroad further enhances
regional differences since immigrants presumably do not prefer rural, disadvantaged areas;
they most probably favour economically developed micro-regions, which are close to big
cities. The observed changes in the socio-economic environment require corrections in the
demographic projections for both the professional audience as well as actors in social
policy.

A network representation is introduced for visualizing hierarchical region structures on
various spatial scales. The method is based on a spring-block model approach borrowed
from physics; it was previously used successfully to detect regions in any geographical
space. Transylvania, USA and Hungary are used to demonstrate the network construction
method.

The aim of this paper is to adapt related variety calculations to the special case of
Hungarian regional development in the late post-socialist transition period. First, we test
regional employment growth in rising and declining regions separately, in order to
distinguish those areas that could cope with economic transition and those that could not.
We find that related variety speeded up growth in the dynamic regions but at the same time
pushed lagging regions onto a downhill path; this may have been due to their inflexible
industry structure. Following this, regional variety measures are decomposed into domestic
and foreign subsets, and a new variable, ownership variety, is introduced. Findings suggest
that regional employment growth is due to related variety in the domestic set in earlier
phases, whereas the economy has evolved into a stage in which relatedness among foreign
firms enhances regional employment growth significantly.