Location Choice and Urban Growth in a Rent-Seeking Society

1988 ◽  
Vol 16 (2) ◽  
pp. 158-177 ◽  
Author(s):  
Bruce L. Benson ◽  
M. D. Faminow
Keyword(s):  
Urban Growth ◽  
Specific Growth ◽  
Growth Models ◽  
Empirical Test ◽  
Location Theory ◽  
Rent Seeking ◽  
Growth Patterns ◽  
Growth Theory ◽  
Complete Understanding ◽  
Urban Growth Model

Gordon Tullock suggested that as rent-seeking becomes increasingly important, location choices and urban growth patterns will be affected. Resources should be diverted to cities where government units are most able to grant rents. The implications of this argument are expanded upon using principles of location theory and location-specific growth theory. An empirical test of an urban growth model provides support for Tullock's contentions. By considering rent seeking in the context of location and urban growth models, the implications of the rent-seeking paradigm are extended. Simultaneously, a more complete understanding of relative urban growth rates is gained.

scholarly journals A Review of Simulation Urban Growth Model

2018 ◽  
Vol 7 (4.11) ◽  
pp. 17 ◽  
Author(s):  
Feri Nugroho ◽  
Omar Ismael Al-Sanjary ◽  
. .
Keyword(s):  
Growth Model ◽  
Urban Growth ◽  
Growth Models ◽  
Model Identification ◽  
Urban Modeling ◽  
Rapid Population Growth ◽  
Urban Growth Model ◽  
Urban Growth Modeling ◽  
Growth Planning ◽  
The City

Urban development has become a problem in many cities, especially in developing countries. The availability of areas for development is needed to deal with rapid population growth and urbanization. The purpose of this study was to identify urban growth models. Due to urban growth planning, the city will be more manageable and organized. From the conclusions of urban modeling identification can provide an idea of what model is appropriate for use in urban growth studies. The results of this urban growth model identification could be a reference in urban growth modeling in better urban planning.  

scholarly journals Improvement of an Urban Growth Model for Railway-Induced Urban Expansion

2020 ◽  
Vol 12 (17) ◽  
pp. 6801
Author(s):  
Alvin Christopher G. Varquez ◽  
Sifan Dong ◽  
Shinya Hanaoka ◽  
Manabu Kanda
Keyword(s):  
Urban Growth ◽  
Urban Areas ◽  
Historical Data ◽  
Growth Models ◽  
Urban Expansion ◽  
Grid Spacing ◽  
Urban Growth Model ◽  
Sleuth Model ◽  
The Right ◽  
Better Than

Increasing population in urban areas drives urban cover expansion and spatial growth. Developing urban growth models enables better understanding and planning of sustainable urban areas. The SLEUTH model is an urban growth simulation model which uses the concept of cellular automata to predict land cover change using six spatial inputs of historical data (slope, land use, exclusion, urban, transportation, and hill-shade). This study investigates the potential of SLEUTH to capture railway-induced urban growth by testing methods that can consider railways as input to the model, namely (1) combining the exclusion layer with a station map; (2) creating a new input layer representing stations in addition to the default six inputs. Districts in Tsukuba, Japan and Gurugram, India which historically showed evidence of urban growth by railway construction are investigated. Results reveal that both proposed methods can capture railway impact on urban growth, while the former algorithm under the right settings may perform better than the latter at finer resolutions. Coarser resolution representation (300-m grid-spacing) eventually reduces the differences in accuracy among the default SLEUTH model and the proposed algorithms.

scholarly journals Monitoring of Urban Growth with Improved Model Accuracy by Statistical Methods

2019 ◽  
Vol 11 (20) ◽  
pp. 5579 ◽  
Author(s):  
Ayazli
Keyword(s):  
Urban Growth ◽  
Growth Models ◽  
Simulation Models ◽  
Growth Simulation ◽  
Urban Growth Model ◽  
Growth Properties ◽  
Force Calibration ◽  
Improved Model ◽  
Best Fit

While the rural population is decreasing day by day, the urban population is increasing rapidly. Urban growth, which occurs as a result of this increase, is sprawling toward natural and environmental areas in urban fringes, and constitutes the main source of many environmental, physical, social, and economic problems. In order to overcome these problems, the direction and rate of urban growth should be determined with simulation models. In this context, many urban growth models have been developed since the 1990s; the SLEUTH urban growth model is one of the most popular among them and has been used in many projects around the world. The brute force calibration process in which the best fit values of growth coefficients are determined is the most important stage of simulation models. The coefficient ranges are initially defined as being between 0 and 100 and are then narrowed in this step according to 13 separate regression scores, which are used to specify the characterization of urban growth. Consensus has not yet been reached as to which metrics should be used for calculating the best fit values, but the Lee–Sallee and Optimum SLEUTH Metric (OSM) methods have been mostly used in past studies. However, in rapidly growing study areas, these methods cannot truly explain urban growth properties. The main purpose of this paper is to precisely calibrate urban growth simulation models. Therefore, Exploratory Factor Analysis (EFA) was used to calculate the growth coefficients, as a new statistical approach for calibration, in this study. The district of Sancaktepe, Istanbul, which experienced population growth of 80% between 2008 and 2018, was selected as the study area in order to test the achievement of the EFA method, and two urban growth simulation models were generated for the years 2030 and 2050. According to the results, despite the fact that there is little effect of urban growth in the short term, more than 70% of forests and agricultural lands are at risk of urbanization by 2050.

scholarly journals Analyzing the Spatiotemporal Uncertainty in Urbanization Predictions

2021 ◽  
Vol 13 (3) ◽  
pp. 512
Author(s):  
Jairo Alejandro Gómez ◽  
ChengHe Guan ◽  
Pratyush Tripathy ◽  
Juan Carlos Duque ◽  
Santiago Passos ◽  
...  
Keyword(s):  
Urban Growth ◽  
Geographical Information Systems ◽  
Growth Models ◽  
A Priori ◽  
Remote Sensing Data ◽  
Public Utility ◽  
Town Planning ◽  
Geographical Information ◽  
New Town ◽  
Spatiotemporal Uncertainty

With the availability of computational resources, geographical information systems, and remote sensing data, urban growth modeling has become a viable tool for predicting urbanization of cities and towns, regions, and nations around the world. This information allows policy makers, urban planners, environmental and civil organizations to make investments, design infrastructure, extend public utility networks, plan housing solutions, and mitigate adverse environmental impacts. Despite its importance, urban growth models often discard the spatiotemporal uncertainties in their prediction estimates. In this paper, we analyzed the uncertainty in the urban land predictions by comparing the outcomes of two different growth models, one based on a widely applied cellular automata model known as the SLEUTH CA and the other one based on a previously published machine learning framework. We selected these two models because they are complementary, the first is based on human knowledge and pre-defined and understandable policies while the second is more data-driven and might be less influenced by any a priori knowledge or bias. To test our methodology, we chose the cities of Jiaxing and Lishui in China because they are representative of new town planning policies and have different characteristics in terms of land extension, geographical conditions, growth rates, and economic drivers. We focused on the spatiotemporal uncertainty, understood as the inherent doubt in the predictions of where and when will a piece of land become urban, using the concepts of certainty area in space and certainty area in time. The proposed analyses in this paper aim to contribute to better urban planning exercises, and they can be extended to other cities worldwide.

scholarly journals Modelling growth curves of the normal infant’s mandible: 3D measurements using computed tomography

2021 ◽  
Author(s):  
Jan Aart M. Schipper ◽  
Manouk J. S. van Lieshout ◽  
Stefan Böhringer ◽  
Bonnie L. Padwa ◽  
Simon G. F. Robben ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Growth Models ◽  
Normal Growth ◽  
Growth Patterns ◽  
Ct Scans ◽  
Mandibular Ramus ◽  
Linear Measurements ◽  
Mandibular Growth ◽  
Gonial Angle ◽  
Mandibular Body

Abstract Objectives Data on normal mandibular development in the infant is lacking though essential to understand normal growth patterns and to discriminate abnormal growth. The aim of this study was to provide normal linear measurements of the mandible using computed tomography performed in infants from 0 to 2 years of age. Material and methods 3D voxel software was used to calculate mandibular body length, mandibular ramus length, bicondylar width, bigonial width and the gonial angle. Intra- and inter-rater reliability was assessed for these measurements. They were found to be sufficient for all distances; intra-class correlation coefficients were all above 0.9. Regression analysis for growth modelling was performed. Results In this multi-centre retrospective study, 109 CT scans were found eligible that were performed for various reasons (e.g. trauma, craniosynostosis, craniofacial abscesses). Craniosynostosis patients had larger mandibular measurements compared to non-craniosynostosis patients and were therefore excluded. Fifty-one CT scans were analysed. Conclusions Analysis showed that the mandible increases more in size vertically (the mandibular ramus) than horizontally (the mandibular body). Most of the mandibular growth occurs in the first 6 months. Clinical relevance These growth models provide insight into normal mandibular development in the first 2 years of life. This reference data facilitates discrimination between normal and abnormal mandibular growth.

scholarly journals A spatial zoning approach to calibrate and validate urban growth models

2016 ◽  
Vol 31 (4) ◽  
pp. 763-782 ◽  
Author(s):  
Ali Kazemzadeh-Zow ◽  
Saeed Zanganeh Shahraki ◽  
Luca Salvati ◽  
Najmeh Neisani Samani
Keyword(s):  
Urban Growth ◽  
Growth Models

scholarly journals Modeling Past, Present, and Future Urban Growth Impacts on Primary Agricultural Land in Greater Irbid Municipality, Jordan Using SLEUTH (1972–2050)

2021 ◽  
Vol 10 (4) ◽  
pp. 212
Author(s):  
Rana N. Jawarneh
Keyword(s):  
Land Use ◽  
Growth Model ◽  
Urban Growth ◽  
Agricultural Land ◽  
Urban Land Use ◽  
Agricultural Area ◽  
Grand Challenge ◽  
Negative Consequences ◽  
Urban Growth Model ◽  
Rate Of Increase

Urban expansion and loss of primarily agricultural land are two of the challenges facing Jordan. Located in the most productive agricultural area of Jordan, Greater Irbid Municipality (GIM) uncontrolled urban growth has posed a grand challenge in both sustaining its prime croplands and developing comprehensive planning strategies. This study investigated the loss of agricultural land for urban growth in GIM from 1972–2050 and denoted the negative consequences of the amalgamation process of 2001 on farmland loss. The aim is to unfold and track historical land use/cover changes and forecast these changes to the future using a modified SLEUTH-3r urban growth model. The accuracy of prediction results was assessed in three different sites between 2015 and 2020. In 43 years the built-up area increased from 29.2 km2 in 1972 to 71 km2 in 2015. By 2050, the built-up urban area would increase to 107 km2. The overall rate of increase, however, showed a decline across the study period, with the periods of 1990–2000 and 2000–2015 having the highest rate of built-up areas expansion at 68.6 and 41.4%, respectively. While the agricultural area increased from 178 km2 in 1972 to 207 km2 in 2000, it decreased to 195 km2 in 2015 and would continue to decrease to 188 km2 by 2050. The district-level analysis shows that from 2000–2015, the majority of districts exhibited an urban increase at twice the rate of 1990–2000. The results of the net change analysis of agriculture show that between 1990 and 2000, 9 districts exhibited a positive gain in agricultural land while the rest of the districts showed a negative loss of agricultural land. From 2000 to 2015, the four districts of Naser, Nozha, Rawdah, and Hashmyah completely lost their agricultural areas for urbanization. By 2050, Idoon and Boshra districts will likely lose more than half of their high-quality agricultural land. This study seeks to utilize a spatially explicit urban growth model to support sustainable planning policies for urban land use through forecasting. The implications from this study confirm the worldwide urbanization impacts on losing the most productive agricultural land in the outskirts and consequences on food production and food security. The study calls for urgent actions to adopt a compact growth policy with no new land added for development as what is available now exceeds what is needed by 2050 to accommodate urban growth in GIM.

scholarly journals Urban Growth: From pixel to reality

2019 ◽  
Vol 1 ◽  
pp. 1-1
Author(s):  
Shyamantha Subasinghe
Keyword(s):  
Pattern Recognition ◽  
Urban Growth ◽  
Growth Pattern ◽  
Open Space ◽  
Spatial Configuration ◽  
Urban Land ◽  
Growth Patterns ◽  
Zone Model ◽  
Spatial Pattern Analysis ◽  
Ancillary Data

<p><strong>Abstract.</strong> Urban growth is a complex process created through the interaction of human and environmental conditions. The spatial configuration and dynamic process of urban growth is an important topic in contemporary geographical studies (Thapa and Murayama, 2010). However, urban growth pattern recognition is a challengeable task and it has become one of the major fields in Cartography. Since classical era of cartography, several methods have been employed in modelling and urban growth pattern recognition. It shows that there is no agreement among cartographer or any other spatial scientists on how to map the diverse patterns of urban growth.</p><p>Typical urban theories such as von Thünen’s (1826) bid-rent theory, Burgess’s (1925) concentric zone model, Christaller’s (1933) central place theory, and Hoyt’s (1939) sector model explain the urban structure in different manner. Most of them do not contribute to visualize the urban growth pattern spatiotemporally. Recently, by addressing this limitations, several sophisticated methods are used in urban growth visualization. Among them, morphological spatial pattern analysis (MSPA) is one of emerging raster data analysis methods which allows us to integrate neighbourhood interaction rules in urban growth pattern recognition and visualization. Angel et al. (2010) developed urban land classification (urban, suburban, rural, fringe open space, exterior open space, and rural open space) based on built and non-built land categories and detected three major types of urban growth (infill, extension, and leapfrog). However, developing urban land classifications using binary land use type and recognising only three types of urban growth pattern may be insufficient due to the existence of a higher complexity of urban growth. In such context, the present study introduce a geovisualization approach to map spatial patterns of urban growth using multiple land categories and develops three sub-levels of urban growth pattern for each major urban growth pattern.</p><p>The entire process of urban growth pattern recognition developed in this study can be summarized into three steps (Figure 1): (1) urban land mapping &amp;ndash; Landsat imageries representing two time points (2001 and 2017) were classified into two land categories (built and non-built) and developed into multiple classes using ancillary data, (2) recognizing three major patterns of urban growth (infill, extension, and leapfrog) &amp;ndash; the raster overlay method based on neighbourhood interaction rules, (3) development of sublevels of urban growth &amp;ndash; major three patterns were further developed and visualized nine urban growth patterns, namely low infill (LI), moderate infill (MI), high infill (HI), low extension (LE), moderate extension (ME), high extension (HE), low leapfrog (LL), moderate leapfrog (ML), and high leapfrog (HL). The developed procedure of this study in urban growth pattern recognition was tested using a case study of Colombo metropolitan area, Sri Lanka.</p>

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