Spatial consequences of urban densification policy: Floor-to-area ratio policy in Tehran, Iran

2017 ◽  
Vol 46 (4) ◽  
pp. 626-647 ◽  
Author(s):  
Mostafa Ghadami ◽  
Peter Newman
Keyword(s):  
Spatial Structure ◽  
Hot Spot ◽  
Distribution Patterns ◽  
City Centre ◽  
Master Plan ◽  
Urban Spatial Structure ◽  
Activity Distribution ◽  
Financial Benefits ◽  
Arc Gis ◽  
The City

The purpose of this paper is to investigate the effect of the urban densification policies made after the Islamic Revolution on the urban spatial structure of Tehran as the most important metropolis in Iran. The Hot Spot approach based on the Getis Ord Local G statistical test and Arc GIS 10.2 software was employed in this study. The advantage of the Geo-statistic technique used in this study is that this model does not require the exact location of the city centre to map and determine its spatial structure. The results show that the spatial structure of Tehran was affected by the non-spatial densification policies for 30 years (until Tehran’s Master Plan in 2007). Furthermore, these policies were greatly dependent on the financial benefits from the sale of the FAR permission and fines related to the ignorance of the lawful regulations. There is a spatial imbalance between the population and activity distribution patterns in the structure of Tehran. However, the negative spatial consequences of the densification policies are declining capacity of the city centre and the inner wards in retaining the population, and growing population density in the northern outer wards of Tehran.

scholarly journals Lack of Spatial Approach in Urban Density Policies: The Case of the Master Plan of Tehran

2020 ◽  
Vol 12 (18) ◽  
pp. 7285
Author(s):  
Mostafa Ghadami ◽  
Andreas Dittmann ◽  
Taher Safarrad
Keyword(s):  
Spatial Structure ◽  
Statistical Tests ◽  
Area Ratio ◽  
Master Plan ◽  
Urban Spatial Structure ◽  
Floor Area Ratio ◽  
Spatial Approach ◽  
Arc Gis ◽  
Geographic Weighted Regression ◽  
The City

This paper aims to investigate the approach of density policies in the Tehran Master Plan and the consequences of ignoring the macro spatial scale in density policymaking. In this study, the floor area ratio (FAR) regulations of the Master Plan of Tehran (which are defined by specific land use zones) are used as one of the main densification tools. Then, employing the Getis–Ord Local G and geographic weighted regression (GWR) statistical tests, Arc GIS 10.3 software, and population and employment variables, the spatial outcomes of the Master Plan density policies were modeled. In this research, both population and employment (job) variables and their relationship were utilized to depict the urban spatial structure of the city. The model will show the resulting spatial structure of Tehran if the densification policies of the plan are realized. The findings of the research are surprising, as they indicate that the Master Plan’s densification policies would worsen the current spatial structure by disrupting the current population and employment spatial structure and neglecting their logical relationships. In fact, the Master Plan would change the current polycentric structure into a highly dispersed structure due to its densification approach, which is mainly based on the neighborhood micro scale.

scholarly journals Headquarters of Large Enterprises in the Spatial Structure of Major Polish Cities

2014 ◽  
Vol 25 ◽  
pp. 178-193
Author(s):  
Przemysław Śleszyński
Keyword(s):  
Public Sector ◽  
Spatial Structure ◽  
Ownership Structure ◽  
High Order ◽  
City Centre ◽  
Spatial Concentration ◽  
The Public ◽  
Large Company ◽  
Central Business Districts ◽  
The City

The paper is based on the author’s monograph (Śleszyński, 2008). It presents the analysis of enterprise headquarters’ locations in eight largest Polish cities (Warsaw, Szczecin, The Tricity [Gdańsk–Gdynia–Sopot], Poznań, Łódź, Wrocław, Katowice and Krakow). The study primarily involved data from the Hoppenstedt Bonnier database for the year 2004, concerning 3810 entities whose overall revenue exceeded the minimum of 15.6 million PLN. The businesses were analysed in terms of their location with respect to the city centre, as well as the differentiation of revenues, line of business and ownership structure. The analyses helped in the formulation of the basic regularities of the location distribution. For instance, it was found that spatial concentration is most significant in the case of the largest enterprises, the public sector and more advanced businesses, particularly high-order services.The location of large company headquarters, as well as their mutual connections, performs one of the key roles (or even the most important one) in the development of Central Business Districts in Polish cities during transformation.

scholarly journals Research on the Simulation Application of Data Mining in Urban Spatial Structure

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jun Zhang ◽  
Xin Sui ◽  
Xiong He
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Spatial Structure ◽  
Urban Space ◽  
Simulation Analysis ◽  
Sustainable Urban Development ◽  
Urban Spatial Structure ◽  
Urban Centers ◽  
Urban Research ◽  
The City

Data mining and simulation of the Internet of things (IOT) have been applied more and more widely in the rapidly developing urban research discipline. Urban spatial structure is an important field that needs to be explored in the sustainable urban development, while data mining is relatively rare in the research of urban spatial structure. In this study, 705,747 POI (Point of Interest) were used to conduct simulation analysis of western cities in China by mining the data of online maps. Through kernel density analysis and spatial correlation index, the distribution and aggregation characteristics of different types of POI data in urban space were analyzed and the spatial analysis and correlation characteristics among different functional centers of the city were obtained. The spatial structure of the city is characterized by “multicenters and multigroups”, and the distribution of multicenters is also shown in cities with different functional types. The development degree of different urban centers varies significantly, but most of them are still in their infancy. Data mining of Internet of things (IOT) has good adaptability in city simulation and will play an important role in urban research in the future.

States of Urban Spatial Structure

1979 ◽  
Vol 11 (1) ◽  
pp. 23-34 ◽  
Author(s):  
G I Thrall
Keyword(s):  
Spatial Structure ◽  
Equilibrium Model ◽  
Urban Fringe ◽  
Urban Spatial Structure ◽  
Spatial Equilibrium ◽  
Numerical Illustration ◽  
Spatial Equilibrium Model ◽  
Two Factors ◽  
Public Economy ◽  
The City

A spatial-equilibrium model of a local public economy is developed in four settings. Each setting is distinguished by two factors: whether the city is ‘open’ or ‘closed’, and the method used to determine the urban fringe. The four settings are contrasted by use of a numerical illustration.

scholarly journals Influence of the Spatial Distribution Pattern of Buildings on the Distribution of PM2.5 Concentration

2021 ◽  
Author(s):  
Zhixuan Zhang ◽  
Baoyan Shan ◽  
Qikai Lin ◽  
Yanqiu Chen ◽  
Xinwei Yu
Keyword(s):  
Spatial Distribution ◽  
Standard Deviation ◽  
Spatial Structure ◽  
Distribution Pattern ◽  
Distribution Patterns ◽  
Spatial Distribution Pattern ◽  
Urban Spatial Structure ◽  
Coverage Ratio ◽  
The Mean ◽  
Pm2.5 Concentration

Abstract The spatial distribution pattern of buildings is an entry point for controlling the diffusion of pollution particles at an urban spatial structure scale. In this study, we adopted ordinary kriging interpolation and other methods to study the spatial distribution pattern of PM2.5 and constructed urban spatial structure indexes based on building distribution patterns to reveal the influence of building spatial distribution patterns on PM2.5 concentration across the study area and at different elevations. The present study suggests that: (1) Topographic elevation is an important factor influencing the distribution of PM2.5; the correlation coefficient reaches −0.761 and exceeds the 0.001 confidence level. As the elevation increases, the urban spatial structure indexes show significant correlations with PM2.5, and the regularity becomes stronger. (2) The PM2.5 concentration is negatively correlated with the mean and standard deviation of the DEM, the mean and maximum absolute building height, the outdoor activity area, and the average distance between adjacent buildings; and is positively correlated with the sum of the building base area, the building coverage ratio, the space area, the building coverage ratio, the space occupation ratio, and the sum of the building volume. These urban spatial structure indexes are important factors affecting PM2.5 concentration and distribution and should be considered in urban planning. (3) Spatio-temporal differences in PM2.5 concentration and distribution were found at different elevation and time ranges. Indexes, such as the average building height, the average building base area, the sum of the building volume, and the standard deviation of building volume experienced significant changes. Higher PM2.5 concentration yielded a more significant influence of urban spatial structure indexes on PM2.5 distribution. More discrete spatial distributions of PM2.5 yielded weaker correlations between PM2.5 concentrations and the urban spatial structure indexes.

scholarly journals URBAN CONVERSION IN THE CONTEXT OF VILNIUS CITY DEVELOPMENT ACCORDING TO SOLUTIONS OF MASTER PLAN FOR 2015

2007 ◽  
Vol 31 (3) ◽  
pp. 131-139
Author(s):  
Rūta Leitanaitė
Keyword(s):  
City Centre ◽  
Urban Structure ◽  
Master Plan ◽  
Single Family ◽  
Social Infrastructure ◽  
City Development ◽  
Family Housing ◽  
Industrial Zones ◽  
And Control ◽  
The City

Following the principles of sustainable development, one of the priorities, set in the new Master Plan of Vilnius, is development of a compact city. One of the instruments to achieve it is urban conversion. A convertible territory is a territory, which doesnt correspond to city‘s development priorities, its urban structure, or is injurious to the environment. There are two types of convertible territories defined in Vilnius: the former or is existing industrial zones and territories of collective gardens. Convertible territories are set after analysing them by criteria describing their impact on the city’s urban, environmental, economic, social qualities. When setting the regulations of conversion and future function of a territory, future sustainable connections with the adjacent urban and functional structures are the essential thing. The main principle of urban conversion is the multifunctional use, accentuating the necessity of public, social infrastructure. The regulations for convertible areas are analogous to the ones set for newly developing areas. The main part of the former industrial territories is to be converted into multifunctional (residential, commercial, public) zones, giving priority to the ones located in the city centre or local centres. All the territories of collective gardens are to be converted into single-family housing areas. The process of implementation of the regulation and control of urban conversion isn’t unquestionable. Improvements of the method are suggested referring to the experience of other European cities. Urbanistinė konversija Vilniaus miesto plėtros kontekste pagal bendrojo plano 2015 metams sprendinius Santrauka Pateikta Vilniaus BP konvertuojamų teritorijų samprata ir tipai, aptariamas jų potencialo nustatymo būdas. Pristatoma konvertuotinų teritorijų Vilniaus mieste identifikavimo metodika; apžvelgta teikiama Vilniaus miesto savivaldybės teritorijos bendrajame plane iki 2015 metų teritorijų konversijos reglamentų nustatymo metodika bei konversijos reglamentų siūlymai konkrečioms miesto teritorijoms. Aptariami teritorijų konversijos sprendinių įgyvendinimo ir reguliavimo Lietuvoje mechanizmo trūkumai bei Europos miestų patirtis šioje srityje, išskiriant optimalius metodus. Apžvelgiama urbanistinės konversijos reguliavimo problema žemesnio nei bendrasis planas rango teritorijų planavimo dokumentuose.

Modeling the Change of Urban Spatial Structure: Use Interzonal Travel Data to Estimate Urban Growth and Expansion by Hierarchical Cluster Analyses

2018 ◽  
pp. 376-408
Author(s):  
Darcin Akin ◽  
Serdar Alasalvar
Keyword(s):  
Spatial Structure ◽  
Urban Growth ◽  
Hierarchical Cluster ◽  
Circulation Pattern ◽  
Transportation System ◽  
Urban Spatial Structure ◽  
Economic Activities ◽  
The City ◽  
Hierarchical Cluster Analyses

The Urban spatial structure is affected by spatial interactions among various activity locations, and land uses in the city over the transportation system. Each city has its unique circulation pattern of passengers and freight due to its unique geographic conditions and the distribution of locations of economic activities. In that sense, it is claimed in this chapter per the authors that urban spatial structure can be modeled using interzonal (O/D) travel data. Thus, the chapter presents a case study of modeling spatial structures developed by employing Hierarchical Cluster Analysis (HCA) using travel pattern data for current and future scenarios. As a result, urban growth and expansion were estimated based on the level of interaction (represented by distance or similarity modeled based on trip interchanges) over the transportation system in terms of population and/or employment increases. The interaction was described by a measure of distance or similarity, modeled with respect to trip interchanges.

scholarly journals Using Nighttime Light Data and POI Big Data to Detect the Urban Centers of Hangzhou

2019 ◽  
Vol 11 (15) ◽  
pp. 1821 ◽  
Author(s):  
Ge Lou ◽  
Qiuxiao Chen ◽  
Kang He ◽  
Yue Zhou ◽  
Zhou Shi
Keyword(s):  
Spatial Structure ◽  
Urban Space ◽  
Space Structure ◽  
Master Plan ◽  
Urban Centers ◽  
Evaluation Report ◽  
Large Cities ◽  
Nighttime Light Data ◽  
Nighttime Light ◽  
The City

The worldwide development of multi-center structures in large cities is a prevailing development trend. In recent years, China’s large cities developed from a predominantly mono-centric to a multi-center urban space structure. However, the definition and identification city centers is complex. Both nighttime light data and point of interest (POI) data are important data sources for urban spatial structure research, but there are few integrated applications for these two kinds of data. In this study, visible infrared imaging radiometer suite (NPP-VIIRS) nighttime imagery and POI data were combined to identify the city centers in Hangzhou, China. First, the optimal parameters of multi-resolution segmentation were determined by experiments. The POI density was then calculated with the segmentation results as the statistical unit. High–high clustering units were then defined as the main centers by calculating the Anselin Local Moran’s I, and a geographically weighted regression model was used to identify the subcenters according to the square root of the POI density and the distances between the units and the city center. Finally, a comparison experiment was conducted between the proposed method and the relative cut-off_threshold method, and the experiment results were compared with the evaluation report of the master plan. The results showed that the optimal segmentation parameters combination was 0.1 shape and 0.5 compactness factors. Two main city centers and ten subcenters were detected. Comparison with the evaluation report of the master plan indicated that the combination of nighttime light data and POI data could identify the urban centers accurately. Combined with the characteristics of the two kinds of data, the spatial structure of the city could be characterized properly. This study provided a new perspective for the study of the spatial structure of polycentric cities.

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