Variation in the Built Form of Neighborhoods of Different Socio-Economic Groups:

Case study of four neighborhoods of Bhubaneswar

Abstract

Unequal distribution of physical activity resources for the disadvantaged has become a major topic of discussion in today’s world. The resources include parks, urban greenery, and private facilities for recreation to name a few. Although morphological indicators such as street connectivity, block size, and block length are important for walking, limited literature explains how these built forms vary for neighborhoods of different socio-economic groups. The study can be important in Indian conditions since walking is a necessity for work as well as for leisure for a certain group of people who cannot afford to buy motorized vehicles. Assessing spatial inequality for them can contribute to recommendations for sustainable planning. This study compares four neighborhoods of different property values in Bhubaneswar, India to examine the inequality in the built-form distribution. India is specially chosen for this study because standard rules for subdivisions are almost missing there. Seven built-form indicators are compared for the four neighborhoods. The analysis shows that a variation in built form does exist between neighborhoods of different socio-economic groups. Longer blocks, higher block density, and higher plot density are observed for neighborhood of lower socio-economic status. However, neighborhood predominated by informal housing is compact and found to have the best walkability conditions.

Introduction

Literature have investigated the unequal distribution of built environments for neighborhoods of different socio-economic groups widely in different countries (Turrell, Haynes, et al., 2013; van Wijk, Groeniger, et al., 2017). However, these studies mostly focused on built environment factors such as recreational green spaces or food environment which has an influence on population health (Cummins and Macintyre, 2006; Garcia, Garcia-Sierra, et al., 2020; Sikorska, Łaszkiewicz, et al., 2020; Williams, Logan, et al., 2020; Wu, Peng, et al., 2022; You, 2016). The unequal distribution of spatial structure of neighbourhoods are somewhat less explored in spatial justice studies. Literature on western countries has indicated the importance of land use, residential density, and street connectivity on walking behaviour and highlighted that inner-city neighbourhoods, compared to suburban ones, tend to have high population density, greater street connectivity and more sidewalks (Lopez and Hynes, 2006). The studies also explained that inner cities have higher percentage of disadvantaged populations (Weir, Etelson, et al., 2006; Wilson, Kirtland, et al., 2004). Similar studies of China too highlighted that the immigrant population is usually located in the center part of the city and has proximity to parks and green spaces although the study did not explain the spatial structure of the neighborhoods (Xiao, Wang, et al., 2017). However, studies on Indian cities such as Kolkata showed a reverse pattern in terms of the location of the disadvantaged population. As per the literature the poor are usually placed at the periphery where land value is comparatively lower (Mishra, 2018).

Spatial structure of neighborhoods such as street and block morphology lead to better connectivity and can reduce travel distance and travel time, allow public transport use, and lessen vehicle dependency which could be highly beneficial for the poor (Zhang, 2013). However, while in developed countries disadvantaged groups enjoy improved connectivity, the same cannot be said for developing countries such as India. While unplanned developments are prevalent in India, there are instances where the whole neighborhood is designed by the planning authority to create an orderly development. Still, it is unclear whether the planned developments have any intentional bias in terms of their spatial structure and layout. This paper tries to compare the built form of three such planned neighborhoods of different socio-economic statuses (SES) to understand whether there is any inequality in terms of their spatial structure. The neighborhoods are selected based on property values and compared with a neighborhood of informal housing to examine the differences between planned and unplanned development. Bhubaneswar city has been chosen as the study area for two reasons. First, because it is a mid-size city and spatial justice studies so far are not conducted for such cities. Previous literature on spatial justice have considered metropolitan cities such as Mumbai and Kolkata and found out that, the disadvantaged populations are clustered at certain vulnerable locations and located at the periphery where property value is relatively low. Property value difference is much more evident at metropolitan cities because they are the cities of financial importance where the gap between the wealthy and the poor is significantly high. However, it is not clear whether a similar trend follows for mid-size cities. Second, Bhubaneswar city was envisioned for common masses historically where neighborhoods were planned considering all the socio-economic groups during its initial inception. To avoid class and caste distinctions, healthy proportion of population of all social and professional groups constituted the neighborhood. Bhubaneswar thus creates and interesting base for spatial justice studies after 74 years.

This study would contribute by finding a disparity in distribution of morphological variables in neighborhoods of different socio-economic groups. Further research is required to confirm on the same. So far, no spatial justice studies are done considering these aspects. The study could find adverse walking conditions for low SES neighborhoods while considering the street block length and street block density. New neighborhoods should take care of street block length and density criteria during the designing stage as they are important for enhancing walking conditions.

Literature Review

Recent studies agree that built form has immense influence on walking behavior indicating that it should be planned appropriately for active transportation. In fact, ideals of new urbanism and smart growth explain the benefits of compact built forms to promote non-motorized commuting (Handy, Paterson, et al., 2003; Knaap and Talen, 2005). Several pieces of health literature emphasize the influence of built form on physical and mental health. Studies suggest that built form might be contributing to obesity because obesity is prevalent in areas where land use prevents people to walking (Frank, Andresen, et al., 2004). Initial health literature has indicated variables of built form such as residential density, land use diversity and street connectivity have an impact on non-motorized commuting (Frank, Schmid, et al., 2005). Zoning ordinances separate residential land use from other and thus restrict mixed-use developments, subdivisional regulations promote cul-de-sacs over integrated streets and urban sprawls form suburban developments reducing residential density. As a result, distance to work, shop and play increase creating dependency on motorized transport (Frank, Sallis, et al., 2006). Empirical studies have measured residential density, land use diversity, street connectivity and its effect on walking and found out a close correlation between them (Frank, Schmid, et al., 2005). Street intersections provide more choices for a person to reach at its destination and thus is popularly used to measure street connectivity (Frank, Kerr, et al., 2010; Marshall and Garrick, 2010). Studies in western countries have highlighted better connectivity leads to better access to variety of destinations and thus positively associated with walking. Similarly, a higher number of residential units in an area reduces the spread and walking distance. Distribution of commercial, residential and office spaces are used as indicator for land-use mix because it drastically impacts the distance to shop or work (Frank, Schmid, et al., 2005; Rosenberg, Ding, et al., 2009).

Disparities in availability, aesthetics, and safety-related characteristics may make poor neighborhoods less attractive for walking (Weir, Etelson, et al., 2006). Several studies are carried out giving importance to the physical distribution of built forms such as parks and recreational facilities in recent years. The idea of Environmental justice (EJ) also provides a conceptual framework for understanding built-environment disparity for low-income and racially and ethnically diverse communities (Vaughan, Kaczynski, et al., 2013). However, much of the recent attention on literature is to know whether various recreational green spaces are equitably distributed by neighborhood socioeconomic status (SES) or ethnic/racial composition while it is often concluded that lower SES neighborhoods usually contain a significantly lower number of recreational green spaces than their higher SES counterparts (Crawford, Timperio, et al., 2008; Estabrooks, Lee, et al., 2003; Tan and Samsudin, 2017). Physical fitness facilities such as sports and recreation clubs, dance studios and golf courses have also gained momentum in research. Similar to the findings of other research, the studies found significantly fewer numbers of facilities in neighborhoods with higher proportions of African American residents, residents classified in the "other minority" category, and Hispanic residents (Powell, Slater, et al., 2006). The distribution of public right-of-way street trees for disadvantaged neighborhoods in Tampa, Florida supported the inequality hypothesis by observing significantly lower proportion of tree cover in neighborhoods containing higher proportion of African Americans, low-income residents and renters (Landry and Chakraborty, 2009). Similar study conducted in Brisbane, Australia too identified how tree cover and vegetation cover varies across SES within public parklands and residential yards (Shanahan, Lin, et al., 2014). Disparity in distribution of food environment is another area of research which is well explored in literature. Studies found that supermarkets having healthier foods are mostly well accessed from white neighborhoods whereas smaller stores are available in African American and Hispanic-dominated neighbourhoods (Garcia, Garcia-Sierra, et al., 2020). Disproportionate distribution of school catchment is another built-form characteristics identified by researchers. A study in central Beijing found out that there is uneven distribution of quality education and in spite of the school catchment control, natives are given preference in quality public schools than non-natives irrespective of their residential location (Bi and Zhang, 2016). Studies also highlighted accessibility as an important factor for using public spaces as most pleasant spaces in a city are well connected but not always well accessible (Hacini, Bada, et al., 2022).

Unfortunately, literature on disparities in distribution for spatial structure of built environments such as street connectivity are relatively less. A study on developed vrs undeveloped streets of Da Nang explained the irrationality in street development for Vietnam but mostly from the lens of street use than distribution (Do and Mori, 2022). The literature discussed below extracts some of the important variables such as block size, length and density which are good proxy indicators for connectivity at the same time can be linked to wealth and believed to have disparity in terms of their distribution in Indian conditions.

Street intersections

Street intersections can increase the street quantity and thus can be linked to wealth. Street intersection density has direct relationship with wealth because higher coverage to streets implies lower salable area to plots (Handy, Paterson, et al., 2003) which affects the revenue for developers. More number of intersections develop better connectivity in a neighborhood making it more walkable as they increase the number of choices to reach a destination (Handy, Paterson, et al., 2003). In contrast, a smaller number of intersections provide lesser options to reach a destination often increasing the walking distance. In addition, a smaller number of intersections produce longer streets without giving a pause making the streets less walkable and more vehicle dependent as street intersections create discontinuities in movement (Rashid, 2018). 4-way intersections also lead to an unsafe environment and uncontrolled commerce proliferation as suggested by Lee (Lee, Abdel-Aty, et al., 2017).

Street width

Street width is an important indicator for wealth especially in developing countries where the developments are spontaneous and random. Street widths are dependent on infrastructure investment. In addition, wider streets increase the walking distance due to longer crossings (Sevstuk, Kalvo et al., 2016).

Street block

Street block is a fundamental element of the physical structure of a city. A street block is defined as a platted area with one or more plots and completely separated from the adjacent areas by streets, open spaces, or any other form of natural and artificial edges (Rashid, 2018). The length and width of blocks in a neighborhood has immense impact on the streets. Urban planners as well as the Congress of New Urbanism promotes better connectivity with shorter blocks (Handy, Paterson, et al., 2003). Smaller blocks expect to reach more destination plots within a walking range (Sevstuk, Kalvo et al., 2016). In contrast, longer blocks increase walking distances making the neighborhood rely on motorized vehicles. Longer blocks are also linked with higher density of plots or larger plot sizes. Longer and larger blocks since provide better revenues reducing the cost on streets are preferred by developers. However, literature suggest block lengths between 60 and 70 m are highly optimal for pedestrians, 100 m are convenient whereas 200m are inconvenient for pedestrian mobility (Sevtsuk, Kalvo, et al., 2016; Siksna, 1997). Block size is becoming a standard for land-use regulation because of its simplicity. The variations of block-size measure such as block perimeter, mean block area as well as block density are important parameters for deciding the effectiveness of neighborhoods (Stangl, 2015). Block size is used as a measure for connectivity because it is an impenetrable area. Therefore, the larger the block the greater its obstruction.

Plot

A plot is a piece of land having legally defined boundaries which is the smallest unit of land control. It controls the urban process of a city because it is directly linked with the size of the blocks (Bobkova, Marcus, et al., 2019). Plot size also has direct relation with wealth in residential developments. The form and height of buildings are controlled by the municipal rules given for the plot and thus has implication on wealth and expenditure. Thus, plot size is considered as plots and buildings descriptors.

Objective

The aim of the research is to analyze the disparity in spatial structure of neighborhoods. Literature have confirmed that street, street block and plot morphology have immense impact on walking. However, no literature in Indian cities have conducted spatial disparity study considering the above morphological variables. Hence, the research aims to answer the following questions at the end of the study.

1. 1)   Are the streets distributed evenly across neighborhoods of different socio-economic groups leading to uniform walkability conditions?
2. 2)   Are the street blocks distribution is uniform across neighborhoods of different socio-economic groups?
3. 3)   Are the plot system is uniform for neighborhoods of different socio-economic groups?

Data and Methods

Study area

Bhubaneswar has been chosen as the study area. Bhubaneswar is the capital of Odisha state and located in the Eastern zone of India (Figure 1). Bhubaneswar Metropolitan Corporation (BMC) is the local urban governing body for Bhubaneswar and the principal provider of services to it. BMC covers an area of 146.86 square kilometers and having a population of around 856,555 as per Census (CENSUS, 2011). BMC has 67 census tracts or wards which are the smallest territorial entity for which census data are available ( bmc.gov.in). Designed by Otto Koenigsberger in the year 1948, the intention was to cater to housing not more than 40,000 people with administration being city’s primary function. The city at that time was envisaged as a place for the common masses reducing the difference between the rich and the poor (Kalia, 1997). However, the city has expanded significantly after that. The city currently serves as the capital or Odisha and thus contains many government institutions. The residential accommodation provided to the government employees are located at the center of the city and thus allows very little flexibility for expansion there. The capital attracts many migrant workers due to increase in employment opportunities. The present transformed Bhubaneswar now contains 36% of migrant worker population taking 3.9 percent of the total municipal area (Anand and Deb, 2017). On an average 19.6 % of houses do not have toilet facility and 21.4% do not have kitchen facilities (CENSUS, 2011).

Figure 1. Location of Bhubaneswar

Source: BMC, n.d.

Local planning authority acted as a developer for 21 out of 67 wards. They took the responsibility of acquiring the land, sub dividing it and installing site improvements before selling the land to the customers. Eight of these wards belong to the year 1948 planning and cater to government employees. The remaining 13 wards are for the public.

Selected neighborhoods

Literatures suggest the ward size varies from country to country (Vyas and Kumaranayake, 2006). For Bhubaneswar, each ward consists of on an average of 3500 households and around 10,000 population. The ward level map is extracted from open street map (OSM) which is open sourced and available for public use for all most all countries. Previous research has linked wards to characterize neighborhoods in India because they are the smallest statistical subdivision of a city. Three neighborhoods are randomly chosen from the 13 wards which are developed by the local planning authority. Out of the three, one is from the center of the city, one towards the north and another one towards west located at the periphery. Neighborhood 30 located at the center part of the city is developed on a land of 1.2 sq.kms with a national highway running at its north and a railway line towards the east. Neighborhood 14 is located towards north in a land of 1.45 sq.kms, abutted by a forest towards its west and neighborhood 65 located towards the west is abutted by the national highway towards north-west. The fourth neighborhood (neighborhood 21) is predominated by informal housing or slums (Figure 2). The neighborhood 21 is the smallest neighborhood out of all. The three planned neighborhoods 30, 14 and 65 have a grid iron street pattern while the informal housing neighborhood 21 has an organic street pattern.

Figure 2. Location of the selected neighborhoods

Socio-economic status of neighborhoods

Property values are extracted for these neighborhoods considering the real estate prices of apartments with minimum amenity only. Apartments of minimum amenity are usually single or multiple blocks of buildings with a height of five floors or lower. Due to the minimum plot size requirement of 500m2, they could be accommodated in any neighborhood putting limited efforts on plot amalgamation (Bhubaneswar Planning & Building standards, 2008). Since this type of development is available in most of the neighborhoods, it is reasonable to examine its price for comparison. Publicly available data such as name of developer, details regarding the project such as project size, year of construction, number of floors and amenities are extracted from the real estate marketplace. For the study, apartments constructed between the year 2015-2020 are considered for price comparison. The location of the apartment is extracted from the marketplace and is further verified using google maps and the developer’s website whichever is available. Four-Five apartment complexes are studied to get the property price. A descriptive statistic is used for the data sets as shown in Table 1.

Table 1. Property price of the selected neighborhoods (Source: real estate market place)

Details of neighborhood	No. of apartment samples	Mean Rate/sqft	Standard deviation	Min. Rate/sqft	Max. Rate/sqft
Neighborhood 21
Neighborhood 65	06	$41.41	$7.81	$30.41	$50.00
Neighborhood 14	05	$94.05	$13.27	$73.46	$107.14
Neighborhood 30	04	$128.66	$17.39	$114.28	$158.73

Neighborhood 21 since predominated by informal developments, does not contain any apartments for comparison. It is considered under the special category of slums. The other three neighborhoods are classified as low SES, middle SES and high SES based on their respective mean rate/sqft. Since property value is decided based on desirability of a particular location, it fits well with the SES concept. As property value increases for a particular location, a large population is excluded from availing houses in those locations and vice versa. Thus, houses in neighborhoods with low property values can be availed by higher percentage of population than their high property value counterparts and the disparity in wealth becomes evident.

Variables of Spatial Structure

Figure 3 shows the methodological diagram used in this investigation. To evaluate the difference in spatial structure among neighborhoods of different socio-economic groups three morphological factors are selected: streets, blocks and plots (Oliveira, 2016). The morphological factors are broken down further so that they can relate to socio-economic status of a neighborhood as shown below.

Figure 3. Methodological diagram used in the study

Data sets

The street network for Bhubaneswar is downloaded using OSM and QGIS. Both OSM and QGIS are open sourced softwares freely available to download. QGIS is used as the interface to download the street network using its integrated Quick OSM plug-in and clipped in respect to BMC boundary. The map features selected from OSM are highways. Grass v.clean is used to clean the line features and tools such as snap, rmdangle, rmbridge, rmdupl and rmline are been used for the cleaning purpose. While snap tool is used to snap lines to vertex in a particular threshold, rmdangle removes all dangles and rmbridge remove bridges connecting area and islands. Rmdupl removes duplicate geometry features and rmline removes all lines or boundaries of zero length.

After cleaning the line features, Grass’s v.net.centrality and degree feature is used to create and rank the nodes based on their intersections. Degree differentiates between nodes with 4 segments, nodes with 3 segments and dead ends thus it becomes easy to compute total number of nodes of each category. ‘Count points in polygon’ under vector analysis is used to measure the number of nodes of each category. For measuring street intersections only, nodes with four segments and three segments are chosen for analysis. The two segment nodes or turns are not considered they provide comparatively limited options to reach a destination. Street width varies for unplanned developments. Hence, street width is measured (using GIS) for at least three locations in every 500 meters of street length of neighborhood 21. The average of the street widths are considered for further analysis. Other three planned developments showed consistent road width throughout.

Due to unavailability of block level data for Bhubaneswar, street blocks are identified overlapping OSM and google earth maps. Number of street blocks are measured manually from the overlapped image. Street blocks were converted into quintiles to examine the occurrence of each type of block in the neighbourhood. Block lengths are calculated using GIS. Descriptive statistics is used for getting the mean/median block size and mean block length. The block density calculates number of blocks per square kilometers.

Block density = Number of blocks/ area of the corresponding neighbourhood (1)

Plot size seem to be better indicator than FSI for wealth. Due to the unavailability of data on plots for Bhubaneswar, Google earth maps are used to identify the plots. Plots are identified which have definitive boundary and shape. Plots are earmarked and classified based on size reflecting a particular range and expressed in Box-plot diagram. Plot density is measured as:

Plot density = Number of plots/ block (2)

Due to the absence of designated plots in slums, number of buildings are considered for the analysis.

Results

Zoning

A detailed layout of the neighborhoods along with their zoning are shown in Figure 4 and Table 2. The neighborhoods are arranged based on the property value.

Neighborhood 21 has the lowest share of parks and open spaces (5%) and 95% of its area is taken over by informal housing while neighborhood 65 with the lowest property value has the highest percentage of open spaces because of the location of the horticulture institution. Both neighborhood 14 and 30 have almost equal share of open spaces (28.25% and 22.5% respectively). While looking at the plot sizes, Neighborhood 14 with middle SES observed to have a good mix of informal housing or slums and plot size below 120 sqm and 120-240 sqm. In contrast, neighborhood 30 with high SES has the lowest share of slums and highest share of plots above 240 sq.m (50%). Neighborhood 65 with low SES is predominantly consists of plots below 120 sqm (26.7%). Overall, the result indicate that the plot sizes are a good determinant of wealth with higher share of smaller plots with low SES and larger plots with high SES. The slum developments do not contain any specific plots.

Figure 4. Detailed land use of the selected neighborhoods

(A) Neighborhood 21, (B) Neighborhood 65, (C) Neighborhood 14, (D) Neighborhood 30

Table 2. Zoning details of the selected neighborhoods

Description	Slums	Low SES	Middle SES	High SES
Neighbourhood	21	65	14	30
LandArea (Sq.kms)	0.20	1.61	1.45	1.20
Open area, parks %	5%	65%	28.25%	22.5%
% of slums	95%	7.45%	16.60%	0.70%
% of area with plots below 120sqm area	No specific plots	26.70%	19.25%	0%
% of area with plots between 120 sqm-240sqm	No specific plots	0%	33.40%	5.80%
% of area with plots above 240sqm	No specific plots	0%	0%	50%
% of area for apartments	0%	1.37%	2.50%	4.41%

Street Characteristics

The analysis considered seven typical built form variables as mentioned in Figure 5. Descriptive statistics of the street variables for the selected neighborhoods is shown in Table 3. Number of 3-way intersections are the highest for the neighborhood 21(450/sq.km) which has an irregular street layout and caters to informal housing development. Because of the irregular nature, 4-way intersection is the lowest there. However, the higher intersection density does not relate to street widths. Street widths are observed to be extremely low explaining the least amount of attention given to street development. 3-way intersection also increases the connectivity of that neighborhood. Neighborhood 30 has the lowest 3-way intersection (163/sq.km). However, it also has the highest number of 4-way intersections (52/sq.km) reflecting its enhanced approachability. Both neighborhood 65 and neighborhood 14 show similar 3-way and 4-way intersection density. Overall, informal housing showed the best connectivity while considering street intersections measure whereas, neighborhood 30 with high SES has the highest 4-way intersections.

Figure 5. Plot density of selected neighborhoods

Table 3. Street structure

Description	Slums	Low SES	Middle SES	High SES
Neighbourhood	21	65	14	30
Intersection density (Numbers/sq.km)
3- way intersections	450	278	281	163
4-way intersections	5	28	27	52
Street ROW (m)
Secondary street	6.5m	15m	20m	18m
Tertiary street	3.6m	6.5m	10m	10m

Block Characteristics

Block density is the highest for neighborhood 21(135), but the lowest for neighborhood 65 (64) as shown in Table 4. Neighborhood 65 reflects lower block density probably because of the larger chunk of land dedicated for horticulture institution. In contrast, neighborhood 30 seem to have a higher block density (95.83) due to the uniform distribution of land there. Blocks are divided into quintiles for better understanding of the differences in their size. Smaller blocks relate to better connectivity in the neighborhood.

Table 4. Block & Plot structure

Description	Slums	Low SES	Middle SES	High SES
Neighbourhood	21	65	14	30
Block density (Number of blocks/sq.km)	135	64	73.61	95.83
Block size (sq.m)
Mean	6651.58	5729.81	6388.03	6816.80
Standard deviation	5535.18	10225.88	4015.52	4791.47
Minimum	807.71	685.82	516.31	1415.45
Maximum	26125.95	87054.06	19865.09	32118.18
Block size (%)
0-2500	14.81	35.22	9.80	7.07
2500-4000	18.51	26.13	25.49	19.46
4000-7000	33.33	23.86	30.40	45.13
7000 -18500	29.62	7.95	33.33	23.89
18500 and above	3.70	6.81	0.98	4.42
Mean Block length (m)
0-2500	59.5	71.4	67	57
2500-4000	79.4	121	93	80
4000-7000	109	166	135	117
7000 -18500	164	227	182	188
18500 and above	315	296	152	250
Plot density (Plots/Blocks)
0-2500	12-22	13-18	08	08
2500-4000	45	26	12-24	06-08
4000-7000	62	40-50	16-18	10-14
7000 -18500	131	68-82	22-36	10-16
18500 and above		20		17
Plot size (sq.m)
Mean		126.38	236.59	837.23
Standard deviation		39.95	93.89	767.56
Minimum		61.32	93.63	207.88
Maximum		573.86	496.6600	3526.06

Neighborhood 14 showed uniform percentage of 2500-4000, 4000-7000 sqm and 7000-18500 sqm blocks and lower share of 0-2500 sqm blocks (9.80%) making it similar in pattern to neighborhood 30 that has 7.07% of 0-2500 sqm blocks. Neighborhood 65 has the highest share (6.81%) of 18500 and above blocks followed by neighborhood 30 (4.42%) and also has the highest share of 0-2500 sqm blocks (35.22%). The mean block size is the lowest for 65 (5729.81 sqm) and highest for 30 (6816.80 sqm). The larger chunk of land in neighborhood 65 might be the reason for disproportionate distribution of blocks. Overall, low SES neighborhood seem to have lower block density but higher share of smaller blocks (0-2500 sq.m). In contrast, the high SES neighborhood has higher block density but higher percentage of 4000-7000 sqm blocks.

Mean Block length is the highest for neighborhood 65 for almost all the categories of blocks. While examining the shortest block length, block length is observed to be the lowest for neighborhood 30 and neighborhood 21. For 0-2500 sq.m blocks, block length is 57m for neighborhood 30 and 59.5 for neighborhood 21. Block length is related to the walkability of a neighborhood and longer block lengths increase dependency on motorized vehicles. Block lengths seem to be much shorter for slums making the neighborhood compact and walkable (Table 4).

Plot Characteristics

Number of plots/blocks or plot density are the highest for neighborhood 65 and the lowest for neighborhood 30. Their plot distribution is shown in Figure 5. With the increase in block size, the number of plots does not increase significantly for neighborhood 30. For block size 0-2500 and 2500-4000, number of plots per blocks varied between 06-08. Similarly, for block size 4000-7000 and 7000-18500, number of plots varied between 10-16. In contrast, the number of plots varied linearly in respect to the block size in neighborhood 65. While 0-2500 blocks have plots varied between 13-18, 7000-18500 blocks have plot numbers as high as 82. Neighborhood 21 since does not have any designated plots counted the number of buildings per block. The measure expresses very high building density/ blocks (Figure 5).

The plot size is measured only for one block of each quintile. As highlighted in the zoning, plot size for independent housing is the highest for neighborhood 30 and lowest for neighborhood 65. The box-plot of the plot sizes are shown in Figure 6. The highest plot size might be the reason why neighborhood 30 has the lowest plot density. The average plot size for the neighborhood is around 750 sq.m and goes as high as 2000 sq.m. In contrast, the average plot size for neighborhood 65 is around 120sq.m followed by 250 sq.m for neighborhood 14.

Figure 6. Plot size variations of different neighborhoods

Conclusion

The evaluation of spatial configuration of four neighborhoods for different socio-economic groups of Bhubaneswar, India concludes as follows.

Results indicate that that all the built form morphological indicators such as streets, blocks and plots seem to correspond to the SES quite well. When we looked at the formal housing, higher SES neighborhood particularly observed to have highest 4-way intersection density, highest block density, lowest block length, lowest plot density and higher plot size. Higher 4-way intersections increase the potential to reach various destinations. This means, higher number of destinations can be reached with 4-way intersections in high SES neighborhood. This also might be the reason why the neighborhood is getting commercialized fast recently. Increased number of street intersections also implies higher share of land given to streets. In addition, higher block density means a greater number of blocks which contributes to higher connectivity of the neighborhood and corresponds to street quantity. Lower block length reduces the walking distance and provides pause in the movement choices. Larger plots are linked with wealth and spending capacity of the buyers.

In contrast, lower SES neighborhood is observed to have lowest block density, highest block length, highest plot density and lowest plot size. Longer blocks generate better revenues for developers reducing the cost on streets. However, the lowest block density and highest block length contributes to longer walking distance for low SES neighborhood too creating adverse impact on walking conditions.

Future studies need to find out the plot accessibility for both low and high SES since literatures state that smaller blocks with fewer number of plots does not enhance plot accessibility (Sevtsuk, Kalvo, et al., 2016).

The neighborhood with informal housing seems to have the best walkability condition with highest intersection density, highest block density, average block length but having the highest building density. With its compact form, informal housing neighborhood performs the best in terms of walkability. But the neighborhood also has the narrowest street width.

The outcome of the study can help urban designers and planning professional in controlling the built form factors for creating better walkability conditions. 15 percent of the blocks of the low SES neighborhood crosses the maximum block length limit of 200 meters in comparison to 4.5 percent of high SES one which might be impacting on the walking behavior and the quality of life of people who are residing there. Future studies should include a greater number of neighborhoods for comparison to understand whether the disparity exist among all of them or not. Neighborhoods without any subdivisional rules and which are developed spontaneously can be chosen for further analysis to analyze their built form indicators. Pedestrian flow measurement and land use variables can be considered in future studies to understand the walkability in low and high SES neighborhoods and to analyze whether the built form has impact on vehicle ownership and usability.

The study has several limitations. Out of 67, only four neighborhoods are considered for comparison. In addition, neighborhoods which are developed randomly are not studied for comparison. However, the study should be considered as an exploratory one and future studies should compare larger number of neighborhoods to provide guidance on the morphological indicators. Walking behavior of all the neighborhoods could have given a better judgement on the outcome.

Ethics Declaration

The authors declare that they have no conflicts of interest regarding the publication of the paper.

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