The Temporal- Spatial Measurement of Urmia Urban Space with Emphasis on Urban Density Indices

Document Type : Research article

Authors

1 PhD Student in Geography and Urban Planning, Ardabil branch, Islamic Azad University, Ardabil, Iran

2 Assistant Professor in Department of Geography and Urban Planning, Ardabil branch, Islamic Azad University, Ardabil, Iran

3 Associate Professor of Geography and Urban Planning, Ardabil branch, Islamic Azad University, Ardabil, Iran

Abstract

Introduction   
Density is one of the important factors in urban studies, and one of the most important elements of city formation. It has a decisive effect on all aspects of the city and measured by specific indicators in urban planning. The density is very important in drawing the city physical and social status, and its monitoring is also important in the urban development analysis. This research purpose is descriptive – analytical, and is about changes and the spatial-temporal distribution evaluation of urban Density indices in Urmia city, using data and statistical methods during 1996- 2016.
Urmia city experienced widespread physical growth in recent years, and consequently caused environmental hazards and the city instability. The requirement of providing suitable urban services for inhabitants of the city, that will be made possible through proper planning of urban congestion and suitable loading, the necessity of analysis of changes in urban densities, showed more attention has been paid to the urban densities distribution for planning to reach the balance in the compressive loading. In order to plan for achieving the equilibrium in the compressive loading, it has more demonstrated that these indices distribution contributed to the balancing of these indices, in order to distribute them into a suitable services and urban infrastructure distribution. The population density, which indicates the relationship between the people number and the space under their occupation, is based on two types: first Net population density, and second gross population density. Gross Residential Density is the best known city's development indicator, and it observes the amount of land that was used for each individual, and measures the amount of land production, and also measures the housing production amount, too.This type of density also is based on two types, which are located in two forms as recognized as Gross Residential Density and Net Residential Density. The Building Density is the area under construction ratio (in all classes) to the total residential land, which is conveyed in percentage. The Building Density is usually the population density planners and practical language. The FAR also indicates the ratio of floor Area to ground surface. Statistical models can be used for the analysis and measurement of the aggregation degree or the compressive and distribution of a city ratio and they are as following: the Moran and Gray coefficient and the various indices that determine the indicators status based on spatial construction.
Methodology
This research is applied, and its research method is descriptive - analytic.  The statistical population is 5 regions of the Urmia city.  Data were obtained from the official census statistics of population and housing in 1996, 2006, and 2016 after the extraction from different sources in the GIS software. The process of change and their behavior were measured by using SPSS software, and the mean, amplitude, standard deviation, skewness, and kurtosis statistical data were also measured.
Results and discussion 
In the GRD index, the district 4 always has the highest value, and district 2 has the highest value in the NRD. The rate of increase in recent decade has been less than that of other regions, and district 1 has the highest increase from 180 to 247 people per hectare in the recent decade. In the Building Density index, has the lowest value for district 2 and the highest in district 1. This indicated that the construction intensity and the ability to attract more population are of greater significance. The district 1 has the highest value and district 4 has the lowest in the capitation floor Area Index. This  indicated the difference between the areas of these two regions.
This study show that the Moran's coefficient has completely distinct pattern of random and dispersion on the cluster in all indices. The net residential capital with a coefficient of - 0.04 in 1996 and 0.4 in  2016 was the highest mutation rate from the dispersed. The Geary correlation has been accompanied with the overall decline, but the highest decrease in coefficient or compression in the population, residential units and infrastructure were ranged from 0.82 to 1.28 and the lowest compression has reduction about0.15 in the area. The Williamson correlation was the most inequality in the area, with population, number of households, structure, and other indices. It has more balanced distribution. The highest inequality in the building density and FAR was in district 2and the  infrastructure structure in zone 4 and in other indicators is the zone 5  because it was more unbalanced and has the most inequality in distribution. The entropy of the indices displays that the city has witnessed the distribution pattern in the city area and residential area with increasing coefficient to 1.57, in the residential density. The coefficients of FAR and Building density are expressed by the polarity and imbalance in distribution.   
Conclusion
In the past 20 years in Urima city, the values of all indices had increasing, and moving towards the limited compression by using the significant models, but inequality and lack of equivalence in most indicators distribution, and their distribution in city.  This is increasing inequality polar distribution explanation in the city. Consequently, district 1 and 2 are the most populated regions in the city, and the first one from the point of building density and infrastructure. The second one from the point of net population density amongst the city's districts is transformation process of the city structure in last decades.  

Keywords

References

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Geographical Urban Planning Research (GUPR)
Volume 7, Issue 3
October 2019
Pages 629-653

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