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Beyond informality:
Traders as space experts in their own informal settlements
Abdelbaseer A. Mohamed
Ain Shams University, Cairo, Egypt abdo121@windowslive.com
Akkelies van Nes
University College Bergen, Bergen, Norway a.vannes@tudelft.nl
Mohamed A. Salheen
Ain Shams University, Cairo, Egypt mohamed_salheen@eng.asu.edu.eg
Abstract
The spatial layout of built environments influences the distribution of commercial activities. As literature has shown, commercial activities can enhance the process of urban consolidation of informal areas (Hillier et al, 2000; Shafiei, 2007). The purpose of this paper is to investigate the correlation between spatial factors and the distribution of internal and edge commercial land use by applying new methodological means, such as a combined space syntax analysis of the street network with Inter-‐visibility (van Nes & López, 2010), and statistical analysis of the economic issues and band analyses. The cases used in this study are three informal areas in Cairo: Ezbet Bekhit, Ezbet Al-‐Nasr and Abu Qatada. These settlements are selected because they are predominantly self-‐grown and have not been influenced by city plans or land use regulations.
This research attempts to underpin the following questions: Are the distribution and rate of commercial activities mainly driven by the local spatial composition of the area itself? Or, is it more related to how the settlements are embedded in the overall structure of the city? As it turns out, this research has demonstrated in detail that the distribution of commercial activities takes place on the plots that are located along the spatially most integrated, most distributed and most inter-‐visible parts of the neighbourhoods in relationship to the whole of the city. The results of this empirical study contribute to further understanding of a theory of an optimal distribution of plots, in which effective land use is defined as an interaction of two core factors: inter-‐visibility and spatial accessibility. This two-‐variable approach can be used strategically as a tool to guide the regeneration of informal settlements and transferring economic integration to deprived areas of the city.
Keywords
Space syntax, commercial activities, informal areas, urban consolidation, inter-‐visibility.
1. Introduction
Cairo, one of the most densely populated cities in both Africa and Middle East, is a polycentric urban region with several socio-‐economic and urban planning challenges. Rapid growth of this metropolitan region contributed to the emergence of informal settlements. It took place on the outskirts of the city on a privately-‐owned ex-‐agricultural land and on a state-‐owned desert land. The inhabitants of unplanned urban areas seek for the spatial opportunities of the neighbourhoods to generate any kind of income for surviving. There is a social logic in both the geographical location of unplanned settlements and the structure of the spaces between buildings inside these settlements.
Importantly, several research approaches and theories concerning urban activities are based on either ecological or economic views (Adedokun, 2011). Some theories are based on economic variables such as urban land rent mechanism (Alonso, 1964 in Adedokun, 2011), while others are mainly built on commuting models such as house-‐work distance (Olatubara, 1996). However, the spatial structure of the street grid and its influence on urban land use are not properly addressed in these research approaches and theories.
Using space syntax method, more recent work has investigated the relationship between pattern of commercial activities and the spatial parameters of built environments in Chile (Hillier et al, 2000; Greene, 2003), Iran (Shafiei, 2007; 2013) and Bahrain (Al-‐Ghatam, 2009). The results have shown a significant correlation between the degree of spatial integration of the street network and the amount of commercial businesses. The next step is to reveal to what extent these outcomes can be found in urban environments of large-‐scale agglomeration such as Cairo.
So far, micro-‐economic analyses are not applied in research on informal settlements. The spatial analysis in this research uses urban micro spatial measurements such as the density of front doors and the degree of inter-‐visibility of buildings. Means of commercial rate band analysis and the Gini coefficient of inequality are employed in statistical analysis to demonstrate how all scales in built environments influence the dispersal of commercial activities.
The three informal settlements Ezbet Bekhit in Mansheit Nasser, Ezbet Al-‐Nasr in Al-‐Basateen, and Abu Qatada in Boulaq El-‐Dakrour district are selected for this study. Abu Qatada is built on private former agricultural land illegally built on the fringe of the city, whilst the other two cases are constructed on a state-‐owned desert land close to the city center. Moreover, the topography is hilly in Ezbet Bekhit, while the other areas are relatively flat. Finally, the three cases are all self-‐organized and relatively similar in terms of size and age.
2. Various approaches and theories on land use distribution and urban space
When revealing the various contributions from various disciplines concerning location of economic activities and analysing the degree of accessibility of urban space, a throughout integration between the various disciplines is missing.
2.1. Insights from geography and economy
Researchers from geography and economics are concerned with several aspects related to human activities in cities. Some studies focus on the relationship between locations of residence and employment (Lowry, 1946), while others focus on market conditions and locational choices (Herbert at al., 1960). Moreover, other approaches focus on the pattern of spatial interaction between places for goods and services and the ease of movement of people between origins and destinations, goods and services, on the basis of distance (Ullman, 1956).
Consequently, economy theorists and geographers have developed several models to understand the location of economic activities. Many of these models are a revival of von Thünen theory (1826) of agricultural location (Fales and Moses, 1972). According to Thünen theory, the model of agricultural land use deals with the relationship between a particular agricultural product and the distance from the market and the impact of these two variables on profits. Discarding other variables produces an isolated state or a single city, an exclusive business central market area containing all products and employment. Based on Thünen's thoughts, location theory focuses on the geographic location of human activities.
Research with an economic perspective aims to explain the relationship between the location and the type of economic activities based on the assumption that human beings choose —based on their own self-‐interest— locations that maximize their profits and utilities. Like location theory, microeconomic theory is concerned with the behaviour of agents in making decisions on the allocation of urban activities. An optimal allocation of activities is the product of market mechanism that involves three main categories: commodities, land and transport. Land in itself is not scarce, but what generates its value is the cost of travel and accessibility (de la Barra, 1989).
Although these thoughts from traditional economic theory provide insights on the influence of spatial conditions on land use distribution in terms of spatial distance and movement cost, they cannot be facilely applied due to a lack of a method for analysing the spatial parameters. In other words, ‘location theoriesʼ miss “the connection with the real word” (Budiarto, 2007: 31). Optimal and equilibrium patterns of economic activities and human activities, in general, cannot be understood only in terms of market mechanism. Moreover, spatial distance is not the only criterion for allocation and organization of economic functions. Crucially, there is a need to unify insights from various fields to develop a comprehensive theory of urban land use.
2.2. Insights from a spatial configurative approach
Researchers taking a spatial configurative approach claim that spatial configuration influences economic related land use pattern as well as ‘natural movement’ (Hillier, 1996). Likewise, movement attracts commerce. In turn, the commercial activities generate a ‘Multiplier effect’ on movement rates which increase a further clustering of more commercial activities that again encourage transporting movement flow into the most spatially integrated streets and roads. This dynamic process of configuration, movement, and attraction is what Hillier called ‘movement economy’ (Hillier et al, 1993; Hillier, 1996). According to this view, urban space— unlike locational theories— influences the location of economic activities.
In their studies on 17 small-‐sized1 informal settlements, Hillier et al (2000) and Greene (2003) found that spatial factors can support or impede the existence of commercial uses and this in turn can improve inhabitants' socio-‐economic conditions and hence physical consolidation, thus self-‐ improvement. Seemingly, the degree of urban consolidation depends on the ratio of commercial buildings on outward edge2 of a settlement. This ratio of commercial activity is called ‘Edge Oriented
1 The average size of each settlement was approximately 4.83 hectares populated by a mean of about 962.9
persons. Actually, classifying the size of a settlement as small or large is somewhat subjective. While urban contexts of inhabitants between 140,000 and 400,000 are identified as large (UNCHS, 2003 in Shafiei, 2007), other cases fall the number of persons to 120,000 (Mora, 2003 in Shafiei, 2007).
2 An edge street is usually a major planned street that traverses or passes by a settlement and if it runs through
it, it should be rather straight “and extends beyond the settlement at least equal to the settlement length” (Shafiei, 2012: 242).
Commercial Activity’ or EOCA3 (Hillier et al, 2000; Shafiei, 2007). However, it is not clear whether the findings of Hillier and his team can be generalized on large size settlements— where internal commercial streets are more likely to be found— or whether they are peculiar to small size areas, where internal markets are not expected to be existent.
Shafiei (2007) argued that it is not just the ratio of EOCA but the overall relation between commercial activities and spatial factors that might enhance consolidation. In his studies on large informal neighbourhoods in the Iranian city Zahidan, Shafiei demonstrated that there is a significant correlation between shops and the spatial configuration of the road network on the metropolitan level. The key factor in fostering consolidation lies in the economic gain that shops get through their influential position along highly accessible streets.
Importantly, Shafiei calculated the actual commercial rate using a banding method to avoid the logarithmic function between the rate of commercial activities on a street segment and the number of dwellings. Simply, all segments that had the same count of dwellings were grouped in a certain band. The segments within each band are patronized as an imaginary one single line where the aggregated number of an event (e.g. shops) is divided by the aggregated number of dwellings for all street segments of that band (Hillier and Sahbaz, 2005; Shafiei, 2007). Notably, Shafiei found that the distribution of commercial activities within large informal settlements is related to the street network at global scale suggesting that internal routes can act as mediators between centers and sub-‐centres.
More recently, Al-‐Ghatam (2009) studied the spatial distribution of edge and internal shops in ten villages engulfed by Manama and Muharraq cities in Bahrain. She found that in seven of these villages, the spatial structure of commercial activities is related to either metropolitan or local spatial structures or sometimes both levels (Al-‐Ghatam, 2009).
3. Methodology
The selected case studies in this research are neither large (over 100,000 Inhabitants) nor small (below 25,000 Inhabitants) but middle-‐sized. Ezbet Bekhit is inhabited by 37,000 people living over 18.5 hectares (Sims, 2003), while Ezbet Al-‐Nasr hosts approximately 60,000 inhabitants occupying 30 hectares (IUSD, 2013). Besides, Abu Qatada has 27,016 people livings in 28 hectares (CAPMAS, 2006). The space syntax method is used to measure the spatial characteristics of the case study areas and to show that residents' choice of where to live and to have their economic activities is influenced by the spatial structure of urban environment.
This paper uses two approaches. The first approach includes analysing the spatial configuration of the three cases at different scales and comparing the results with commercial activities distribution. In the second approach, micro spatial variables are applied. In addition, the banding method (Hillier and Sahbaz, 2005; Shafiei, 2007) is used to calculate the true commercial ratio for a fine-‐grained investigation of the relationship between spatial variables and the distribution of commercial activities.
3.1 Data sources
This study uses two data sources. Firstly, a survey map from GOPP (General Organization for Physical Planning) is used to construct the axial model of Cairo Metropolitan area. Secondly, the retail data
3 The formula for calculating this ratio is as following: EOCA= 10(shops/plots) + 10(edge shops/ plots) + (edge
and entrances' degree of inter-‐visibility is obtained through field surveys (conducted by the authors) in 2013.
3.2 Space syntax
Space syntax analyses are used 1) to investigate how informal areas relate to their surroundings 2) to the whole city and to calculate the degree of spatial accessibility between outdoor spaces in relation to each other. Accessibility can be calculated in two ways; spatial integration and angular choice. Spatial integration shows how a street relates to all other streets in a system in terms of direction changes, while angular choice demonstrates how likely a street segment will be used with respect to all other pairs of segments. The angular weighting is taken into account here (Turner, 2007).
The radius of analysis can be topological in terms of number of direction changes or metric where segments located within a particular metric distance are considered. For example, radius n considers all segments within a certain system, while radius with 2000 meters limits the spatial calculations to only the segments that are located at a distance up to 2000 meters from a particular segment. All segments outside the distance of 2000 meters are excluded from the analysis. As a rule of thumb, the higher the radius is, the more metropolitan the measure is.
3.3 Micro scale spatial tools
Micro spatial relationships, such as the degree of inter-‐visibility of entrances, seem to influence the distribution of commercial land use. They are useful in analysing the morphological relationship between private and public spaces (van Nes and López, 2010). The degree of inter-‐visibility between entrances is about the percentage of entrances that are visible to each other on both sides of a street (van Nes & López, 2010).
In this research, street segments were categorized into four types according to the degree of entrances' inter-‐visibility (van Nes, 2005: 483):
1. Highly inter-‐visible segments have high density of direct entrances and more than 75 percent of them are inter-‐visible to one another.
2. A street is defined as inter-‐visible if the density of direct entrances is low, but more than 75 percent of them are double facing to one another.
3. Low inter-‐visible segments have high density of entrances, but more 75 percent of them are located on one side of the street segment.
4. A street is defined to be non-‐inter-‐visible if it has few entrances and less than 75 percent of them are located on both sides of the street. Similarly, a non-‐inter-‐visible street has no entrances facing it or all entrances are indirectly related to it.
Overlapping the location of shops with degree of distribution and inter-‐visibility of the street network is carried out to show that the ratio of commercial activities is relatively dependent on inter-‐visible streets.
Figure 1: Hypothetic streets with various degrees of inter-‐visibility (van Nes and López, 2010)
3.4 Statistical analysis
3.4.1 Banding method
The rate of commercial activities per street segment is sensitive to the number of dwellings on that segment as the rate is obtained by dividing the number of commercial buildings by the number of dwellings on the segment (for more detail, see Shafiei, 2007). In order to compensate this distortion, the commercial rate is normalised using the banding method. Simply, ‘True Commercial Ratioʼ4 (TCR) for a particular band is the total number of commercial buildings over the sum of dwellings in the band's segments. Once this rate is calculated for each band, it can be plotted against the average of a syntactic attribute of each band of segments (e.g. spatial integration) to show how they relate to each other. The strength of the correlation gives indication on the degree of commercial efficiency.
Segment band Building count on
segment
Segment band Building count on
segment 1 1 12 13-‐ 14 2 2 13 15-‐ 16 3 3 14 17-‐ 18 4 4 15 19-‐ 20 5 5 16 21-‐ 22 6 6 17 23-‐ 25 7 7 18 26-‐ 29 8 8 19 30-‐34 9 9 20 35-‐ 38 10 10 21 39 and more 11 11-‐12
Table 1: The banding scheme of street segments according to the number of buildings on them.
Table 1 shows the banding range applied to the case study areas. Ezbet Bekhit has 13 bands, whilst both Ezbet Al-‐Nasr and Abu Qatada have 18 and 21 bands respectively.
As can be seen in Figure 2, the true commercial ratio (TCR) is plotted for each band of segments in the three case study areas, and it is obvious that a commercial activity rate falls for the three areas while the band number increases.
3.4.2 The accessibility rank
Another quantitative method for examining whether the distribution of commercial activities follows a spatial order (non-‐randomness) or is placed randomly regardless of accessibility is through calculating the percentage of total commercial buildings at the top percentages of highly accessible buildings (those located along highly integrated street segments). The higher concentration of commercial activities in highly accessible buildings, the more spatially ordered the land use is. The point here is to calculate the percentage of commercial activities captured by a particular class interval of accessibility rank. The higher the percentage of shops captured by the most accessible locations, the more the efficiency of such locations can be deduced and the less randomness of their distribution can be indicated (Shafiei, 2013).
Figure 2: The true commercial ratio for each band of segments in Ezbet Bekhit (top left), Ezbet Al-‐Nasr (top
right) and Abu Qatada (bottom).
3.4.3. The Gini coefficient
The Lorenz curve, proposed by Lorenz (1905), is used in economics, ecology and in studies of biodiversity to describe inequality distribution of a variable (e.g. wealth, income, proportion of
species, individuals, etc.). It relates the accumulative proportion of a variable to the accumulative proportion of another (Duclos and Araar, 2006). ‘Gini coefficient’ (also known as Gini-‐index) is a mathematical summery of the ‘Lorenz curve’. It is helpful in investigating whether the distribution of commercial activities follows a spatial order (non-‐randomness) or is placed randomly regardless of accessibility. First we need to get a ‘Lonrenz curveʼ through the cumulative distribution function (CDF) or ‘cumulative frequencyʼ, which is defined by Wolfram MathWorld (Weisstein, 2010 in Shafiei, 2013: 237) as follows:
“Let the absolute frequencies of occurrence of an event in a number of class intervals be denoted f1, f2, …The cumulative frequency corresponding to the upper boundary of any class interval ci in a frequency distribution is the total absolute frequency of all values less than that boundary…”
(source of formula: Ibid)
Then the ratio of the area sandwiched between the line of maximum equality and the Lorenz curve over the gross area under the line equality gives the ‘Gini coefficient’. Simply, if the percentage of commercial activities, captured by a particular class interval of accessibility rank, is calculated, and if the percentage of these commercial uses is plotted on y axis against the percentage of accessibility on the x axis, then a ‘Lorenz curveʼ can be drawn. The higher the percentage of shops is captured by the most accessible locations, the more the efficiency of such locations can be deduced and the less randomness of their distribution can be indicated.
Figure 3: The Lorenz curve of Ezbet Bekhit showing the percentage of commercial shops against the global
accessibility rank (left). The Gini coefficient of Ezbet Bekhit curve calculated as A / (A+B) (right). (source: Author)
For example, Figure 3 (left) shows the distribution of commercial buildings based on their accessibility rank in Ezbet Bekhit. The Figure shows that about 30 percent of commercial buildings are captured by the top 10 percent accessible routes (the considered spatial measure here is global angular choice). About 70 percent of shops are caught by the lowest 90 percent plots in terms of the degree of global angular choice of the street. That is to be compared with two presumptive
situations: 1) the minimum inequality in which all commercial building are placed equally (the blue dotted line) regardless of accessibility. 2) The maximum inequality in which commercial plots are distributed through the most accessible locations (the red dotted line).
The more the Lorenz curve is close to the maximum inequality, the more it is influenced of the degree of spatial accessibility on the clustering of commercial activities. Actually, the Gini coefficient of inequality (or Gini) has a value that ranges between 0, for the minimum inequality, and 1, for the maximum one. In order to calculate the Gini in the example of Ezbet Bekhit, the surface area above the Lorenz curve (marked as A) is divided by the sum of the two areas of A and B (Figure 3 right).
4. Economic activities in the case study areas
Informal micro-‐economic activities, which are mainly shops, cafes, bakeries and handcrafts, are located in the ground floors of dwellings in the three case studies. Shops are mostly concentrated along main internal streets to capture pedestrian movement, whilst light industrial uses (e.g. workshops) are mainly clustered along the outer borders facing inner-‐city highways to get the benefit of through-‐travellers’ vehicular movement (Figure 4).
Figure 4: Land use in Abu Qatada (top left), Ezbet Bekhit ( top right) and Ezbet Al-‐Nasr
Economic activity in the three quarters is classified into two main types:
1) The trade sector comprised of local shops and some street vendors, with the purpose of providing essential daily goods and some other services to local residents.
2) The light manufacturing sector manifesting in workshops for carpentry and car repair. Marble processing can also be observed in Ezbet Al-‐Nasr due to its proximity to the regional marble clearinghouse (the so-‐called Shaa El-‐Te’ban).
5. Morphological analysis
(a) Angular global choice Rn
(b) Angular global integration Rn
Figure 5: Angular segment analysis for the case studies within wider context
Figure 5(a) shows the angular global choice Rn of the three cases within Cairo metropolitan context. The red lines show the highest values, while the blue streets are the lowest values. Apparently, the three areas are situated along high global choice routes, which tend to be a part of inner-‐city highways. Seemingly, these highways give the residents of such informal areas access to work places and to the central business district of the metropolitan area. Further, commercial activities concentrate along these highways to gain the benefit of high spatial accessibility and high movement flow rates.
The angular global integration Rn shows the three neighbourhoods in green and blue patches indicating that they are all generally segregated from the whole urban context (Figure 5b).
Figures 6, 7 and 8 show the angular choice analyses for the neighbourhoods at global, medium and local scales. These Figures show the visual correlations between spatial parameters and commercial uses distribution (shown on the maps as dots). It gives an impression that shops are mainly located along the most accessible segments. Furthermore, residential buildings tend to cluster along the most segregated streets. However, statistical analysis is needed to demonstrate the existence of such relations.
Angular Global Choice Rn
Angular Choice R2000m
Angular Choice R1200m Angular Choice R800m
Figure 6: Ezbet Bekhitʼs angular segment analysis overlapped with the distribution of commercial activities
Angular Global Choice Rn
Angular Choice R2000m
Angular Choice R1200m Angular Choice R800m
Figure 7: Ezbet Al-‐Nasrʼs angular segment analysis overlapped with the distribution of commercial activities
Angular Global Choice Rn
Angular Choice R2000m
Angular Choice R1200m Angular Choice R800m
Figure 8: Abu Qatadaʼs angular segment analysis overlapped with the distribution of commercial activities
Figure 9: The degree of inter-‐visiblity overlapped with the distribution of commercial land use in Abu Qatada
(top left), Ezbet Bekhit ( top right) and Ezbet Al-‐Nasr (bottom) (source: Author)
In order to reveal the influence of micro spatial measurements (e.g. the degree of inter-‐visibility of entrances) on the distribution of commercial land use, the number of inter-‐visible buildings is divided by the total number of buildings for each street segment.
Figure 9 shows the dispersal of commercial activities in the three case studies overlapped with the degree of inter-‐visibility and the distribution of streets. As mentioned in the methodology section, street segments were categorized into four types according to the degree of entrances inter-‐visibility from highly inter-‐visible (in red colour) to non-‐inter-‐visible (shown in blue). In all cases, shops are mostly concentrated along distributed and high inter-‐visible entrances, which also have high local
and global angular choice. Nevertheless, external streets have high values on the spatial analyses concerning both through-‐movement (choice) and to-‐movement (integration). However, these streets tend to be non-‐inter-‐visible, but still entice commercial use due to the advantage of high accessibility. Dwellings not directly connected to the streets or located along dead-‐ends have mostly residential use.
As can be seen in Table 2, the percentage of commercial activities located along highly inter-‐visible street segments is relatively high. However, most commercial uses are situated along no-‐inter-‐visible routes rather than high inter-‐visible ones. As mentioned above, that is because many shops are located along external streets, which are non-‐inter-‐visible but highly accessible. When excluding shops located on outward facing edges in the statistical calculations, the outcomes show that the majority of commercial uses in the three case studies are located along the highest inter-‐visible street segments (Table 3).
Ezbet Bekhit Ezbet Al-‐Nasr Abu Qatada
High inter-‐visible %Com. 30.1105 41.3841 35.8189 Inter-‐visible %Com. 10.2210 10.8108 6.2583 Low inter-‐visible %Com. 7.45856 3.5473 4.2609 Non inter-‐visible %Com. 52.2099 44.2578 53.6619
Table 2: the distribution commercial activities in the case study areas according to the degree inter-‐visibility
Ezbet Bekhit Ezbet Al-‐Nasr Abu Qatada High inter-‐visible % internal Com. 40.37037 52.8135 56.8710
Inter-‐visible % internal Com. 13.7037 13.8528 9.9366 Low inter-‐visible % internal Com. 10.00 4.5455 6.7653 Non inter-‐visible % internal Com. 35.92593 28.7882 26.4271
Table 3: The percentage of internal shops captured by high inter-‐visible street segments
6. The relationship between commercial rate and spatial parameters
For a precise comparison between various street segments, the banding method is applied to the three case studies as one entity and to the individual informal neighbourhoods as well. In all cases, commercial streets tend to have a number of lower bands larger than residential segments. In other words, there is a significant inverse correlation between the true commercial rate and segments bands (Figure 10 a). That could be the result of a high number of smaller blocks, which foster pedestrian movement by virtue of minimizing trip lengths.
Figure 10 (b) shows the relationship between the true commercial ratio for the three areas as a whole and the mean global and local angular choice in each band. The results from the regression analysis reveal a significant positive correlation between commercial activities and angular choice at radii n, 2000m, 1200m and 800m. The higher the values on the mean choice analyses with a high metrical radius, the higher true commercial ratio (TCR) a street has.
a) The relationship between the true commercial
ratio
and segments bands in the whole case studies (top left), Ezbet Bekhit (top right), Abu Qatada (bottom left) and Ezbet Al-‐Nasr (bottom right)b) The true commercial ratio of segments bands for the entire case studies against the mean angular choice at radii n (top left), 2000m (top right), 1200m (bottom left) and 800m (bottom right)
Table 4 shows the relationship between the two previous variables for each individual neighbourhood. The commercial activities of Ezbet Al-‐Nasr and Abu Qatada are related both to local and global angular choice measures. However, Ezbet Bekhit's commercial land use is neither related to local nor to global spatial values. Seemingly, the distribution of commercial activity in Ezbet Bekhit is relatively random and did not take the advantage of the spatial configuration. Nonetheless, another method is needed to complement the findings obtained from banding method.
True commercial ratio
R Square Correlation P-‐value
Ezbet Al-‐Nasr Choice Rn 0.2385 0.4884* 0.0397 Choice R2000 0.2453 0.4953* 0.0366 Choice R1200 0.3050 0.5523* 0.0175 Choice R800 0.2348 0.4846* 0.0415 Abu Qatada Choice Rn 0.5251 0.7246** 0.0002 Choice R2000 0.5329 0.7300** 0.0002 Choice R1200 0.3847 0.6202** 0.0027 Choice R800 0.2964 0.5444* 0.0107 Ezbet Bekhit Choice Rn 0.0069 0.08311 0.7872 Choice R2000 0.007995 0.089413 0.7714 Choice R1200 0.00067 -‐0.02588 0.9331 Choice R800 0.000675 -‐0.02599 0.9328 **. Correlation is significant at the 0.01 level (2-‐tailed).
*. Correlation is significant at the 0.05 level (2-‐tailed).
Table 4: The relationship between the true commercial ratios and the local and global choices in the case study
areas
Seemingly, the banding method is sensitive to the settlement size, specifically to the number of bands. Thus, a method independent of the settlement's size is required to support the findings of banding method. Probably, revealing the percentage of commercial buildings captured by the top decile of accessibility is useful. Table 5 shows the percentage of commercial plots in the top deciles of accessibility at a medium radius (Choice R2000m). In all cases, the results indicate that the distribution of commercial land use is structured along highly accessible streets on various scale levels. The commercial plots are unequally distributed, indicating that they follow the most accessible locations. These findings are also demonstrated by the calculated Gini coefficient values of the three neighbourhoods as the values indicate that the distribution of commercial activity is not random regarding spatial accessibility. Certainly, the impact of accessibility on land use distribution affect the dispersal of commercial activities.
Ezbet Bekhit Ezbet Al-‐Nasr Abu Qatada Top 10% (Choice 2000m) Com. 31.768 13.6824 15.1515 Top 20% (Choice 2000m) Com. 56.630 38.5135 37.1901 Top 30% (Choice 2000m) Com. 73.204 56.0811 61.5702 Gini (Choice 2000m) Com. 53.1447 38.6635 38.8677
Table 5: Gini value and commercial activities distribution in the case study areas according to the upper
percentages of accessibility (Choice R2000m)
7. Towards a theory of an economically optimal plot distribution in urban areas
The spatial layout of the built environment influences the distribution of commercial land use. Commercial activities take place in the spatially accessible and inter-‐visible streets, whilst less-‐ movement-‐seeking functions (e.g. residential buildings) favour segregated spaces. This also accounts in informal areas. The residents of informal areas have, to a certain extent, a local knowledge of their neighbourhoods. Hence, the best locations for the commercial activities are on plots located along locally accessible streets connected to the metropolitan road network for capturing the random passers-‐by.
As in literature, a large size of an informal settlement contributes to an emergence of internal commercial shops, which prefer the most accessible streets. The existence of the commercial use is confined to the borders of a settlement as well within the internal local routes. Furthermore, this paper differentiates between two types of activities with respect to their degree of accessibility. Small local shops in informal areas depend on high pedestrian accessibility. Therefore, they are mainly located along internal routes. Conversely, light industrial activities such as workshops require a big amount of vehicular accessibility. Therefore, most workshops are located along thoroughfares, such as main roads connected to the whole city. Informal areas are not haphazardly placed as one might think. These areas have an internal spatial logic in their geographic locations. Moreover, the spatial structure influences the distribution of land use pattern inside such areas.
Insights from urban economic perspectives show that movement plays an important role in land use distribution. Obviously, the urbanism and economics research disciplines have similar interests in terms of movement cost and its influence on land value and human activities distribution. However, land use with an economic purpose can be understood in the light of the configurational relations whose logic stems from the physical form of the urban environment itself, rather than as a mere vacant land waiting for a planned function. A spatial configurative approach refutes the assumption of location and microeconomic theories, in the way that economic players —based on their own self-‐ interest— make locational choices that maximize their opportunities in terms of profits and utilities. Microeconomic theories claim that people have a certain freedom in their locational choices. However, these decisions are influenced by the spatial structure of a built environment. Furthermore, the microeconomic theories have failed to measure these locational choices objectively, since these theories and approaches are only dealing with economic logics. A spatial configurative approach can bridge the gap between urban and economic disciplines. Further investigations are needed for building a refined theory on the optimal economical distribution of plots. So far, the spatial layout of the street network combined with high inter-‐visibility and accessibility from buildings towards streets influences the location pattern of commercial activities on a local as well as on a metropolitan scale.
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