Advertisement

Determining Factors Influencing Residents’ Satisfaction Regarding Urban Livability in Pakistan

  • Farrukh Baig
  • Irfan Ahmad RanaEmail author
  • Mir Aftab Hussain Talpur
Original Research Article
  • 448 Downloads

Abstract

Livability is an important and crucial aspect of urbanity which depicts quality of life. The emerging cities have a variety of urban challenges, including poor quality of life that has focused urban planners towards livability measures. Pakistani cities are facing similar challenges, like provision of adequate infrastructural facilities and amenities, due to which cities are losing their livability standards. Hyderabad, the second biggest city of Sindh Province, is an emerging metropolitan facing the same dilemma. The objective of this paper is to measure the livability as perceived by the residents. Six urban settlements were selected through stratified sampling to represent three income groups present in the study area, i.e. upper, middle and lower. Through systematic sampling, a sample size of 290 was procured for the household-based questionnaire survey, and descriptive statistics and multiple regression techniques were used. Results showed a significant relationship between perceived livability and livability attributes. The environmental attribute was found most significant in affecting livability perception of residents. The study also highlights eight livability variables which need to be considered for improving the quality of life in future urban development projects, not just in Pakistan, but also other developing countries.

Keywords

Community perceptions Community well-being Neighborhood facilities Quality of life Residential satisfaction 

Introduction

Livability means good quality of life (Bandarabad and Shahcheraghi 2012), and the standard of well-being of inhabitants in a region or a city (Okulicz-Kozaryn 2013). Livability is a vital part of urban planning as a discipline, and is now discussed widely in the interrelated fields like sustainable development, quality of life and quality of place (National Research Council 2002). The aim of planning and designing of neighborhoods is to offer livable settings to its residents (Pandey et al. 2014a). Therefore, livability has become an important element of focus by urban planners, and governments at all levels (Pandey et al. 2013). With rapid urban growth, good quality of life (QoL) for the public has become a challenging task. Livability assessments and QoL are often interrelated, and improvement in livability assessment can result in better QoL (Grieve and Weinspach 2010). The imperative need for assessing livability has also increased, which will help in ensuring long-term development plans for the city’s planning and management in a sustainable manner (Yin and Yin 2009). The concept of livability entails wider aspects of quality of life, accessibility to facilities, neighborhood design, safety and security and satisfaction. This paper tries to propose a methodology for quantification of perceived livability through five main attributes, i.e., social, economic, cultural, environmental, and infrastructure. This study tries to better understand the multifaceted and multidimensional phenomena of livability through these selected attributes.

The trends of livability in Pakistan vary from urban to urban, urban to rural, and province to province (Parmar and Jalbani 2005). Uncontrolled urban growth has brought forth issues of unequal socioeconomic and infrastructural development (Rana and Bhatti 2018; Rana et al. 2017a, b). Small towns of yesteryears have risen as megacities, and this situation has instigated pressures on provision of amenities, and as a result has aggravated current living situations. Hyderabad, with its urban growth rate of about 2.4%, has an estimated population of 1.7 million residents (Pakistan Bureau of Statistics 2017). Poor urban management in the city has led to loss of public revenue, and severe harms to the well-being of its residents (Qasim and Zaidi 2013). As the cities grow, population increases and more housing schemes are needed to accommodate the inhabitants (Mohit and Iyanda 2016). Resultantly, development of new housing schemes in Hyderabad is increasing tenfold, without proper attention to the needs and satisfaction of residents (Haq 2014). According to Mercer ranking, only one city of Pakistan lies among the top 200 cities with respect to the quality of life. Mercer ranked Islamabad, the capital city of Pakistan at 195, Lahore ranked as 202 and Karachi ranked as 205 (Mercer 2018). Meanwhile, new emerging cities, i.e. Hyderabad, Gujranwala and Faisalabad were not included. The livability measuring scale usually includes metropolitan cities of Pakistan, while the newly emerging cities are neglected by urban planners. This paper makes an attempt to measure the perceived livability satisfaction level for a newly emerging city so that the same or modified methodology can be adopted in the future for other emerging cities. This research aims (i) to analyze the quality of life perceived by the occupants of different neighborhoods in terms of livability, (ii) and investigate the significant factors of livability for improving the livability.

Livability Revisited

Livability is considered as ‘quality of life’ of the inhabitants within an area, i.e. city or region (Okulicz-Kozaryn 2013). Livability is a crucial element of urban environment characteristics that affect the attractiveness of a place, but still, there is no definite definition in literature to describe the whole concept (Zhan et al. 2018). Livable means many things to different people and experts. It is a concept that people seem to recognize, but is difficult to define in a manner that everyone understands (Balsas 2010). Sometimes, the livability concept is also referred to as quality of life and includes the objective living environment with subjective experience of livability (Wei and Chiu 2018). Okulicz-Kozaryn and Valente (2019) claimed that livability is a complex issue and cannot be measured because of innumerable factors, but only a subjective well-being can be measured. Consequently, the concept of livability has become a complex and multifaceted phenomenon. It includes good governance, economic revitalization, environmental quality, the standard of living, cultural vitality, justice and adequacy of infrastructural facilities. In terms of quality of life, it can also include access to food, shelter, and security, and sense of belonging (Okulicz-Kozaryn 2013). ‘Mercer Worldwide Quality of Living Survey’ and ‘The World’s Most Liveable Cities’ has used different criteria, such as access to education, healthcare, housing, public services, recreation, safety and environmental quality (Mercer 2018; The Economist Intelligence Unit (EIU) 2018; Barrette 2015). Zhan et al. (2018) claimed that urban livability and its determinants are beneficial to incorporate in the development of cities. Planners and policymakers consider livability as a guiding principle for the investment and decision-making that shape the urban environment (Ruth and Franklin 2014). Nowadays, various factors like civil society, local businesses, and local and state governments are working towards maintaining and improving the city’s degree of livability (Kaal 2011). Livability can strengthen urban sustainability and help in executing development plans effectively (Godschalk 2017). Hence, urban livability assessment is a useful tool in order to answer the question of “who gets what, where and how” (Saitluanga 2014).

Numerous research studies have tried to measure perceived livability in the Global North. Li analyzed the factors which were responsible for perceived livability of foreign-born and native-born U.S. Residents (Li 2012). Using data from the American Housing Survey, the study summarized the amenities and satisfaction into categories such as infrastructure and physical attributes, safety, business accessibility, public services, and neighborhood housing. Analysis revealed that satisfaction with public transportation was negatively related to perceived neighborhood livability. In contrast, favorable amenities, such as proximity to open space and bodies of water proved to be positively related to perceived neighborhood livability. Okulicz-Kozaryn (2013) investigated relationship between the Mercer city ranking scale and primary data on livability. The study compared quality of life with resident’s satisfaction. In another study, Okulicz-Kozaryn and Valente (2019) measured the subjective well-being and livability across European cities. This study found that Mercer city livability rankings and subjective well-being rankings were very different. For example, Zagreb ranked lower than Athens in city livability, but it had higher subjective well-being ranking. This implies that there is no direct link between actual livability statistics and perceived livability.

In the Global South, Pandey et al. (2014b) explored the perception of livability across various socioeconomic and demographic parameters. It was found that the perceived livability varied from person to person and place to place. Yin and Yin (2009) carried out an in-depth study to understand the city’s livability. They used fourteen indices for eighteen cities to measure livability. Indicators included neighborhood satisfaction, infrastructure and physical attributes, safety amenities, business accessibility, public services, neighborhood housing and household characteristics. In a recent study on assessment of urban livability, Zhan et al. (2018) assessed urban livability satisfaction for Chinese cities through a questionnaire-based survey. The results indicated that moderate level of urban livability satisfaction existed for the urban populace. However, more research is required to see how livability issues are experienced and assessed in urban neighborhoods in developing countries. The assessment of the resident’s livability satisfaction level may bridge this research gap, and guide the policy makers to look upon the housing livability. This study intends to assess the livability perceived by the residents in order to provide guidelines to improve the overall living conditions and quality of life.

Livability has become an emerging issue that needs to be addressed critically, especially in the context of developing countries, where the standard of life is meager in low and middle income neighborhoods (Pandey et al. 2014a). Research studies have been done on the assessment of the residents’ livability satisfaction level in developed countries, however, limited studies were found in the Asian context. As livability is highly qualitative in nature, therefore, its acceptance may differ with the geographical area. The standard of living and lifestyle may vary with culture and norms of an area, which can lead to differences in expectations and demands for services standards and infrastructure. The in-depth review of both academic and grey literature depicts similarities with minor differences in measuring the livability. Previously studies have used subjective or objective indicators within the various dimensions and different methodologies to measure livability. In most of the cases, the selection of indicators varied with the area, and nature of the study. This study measures satisfaction regarding livability in a Pakistani city through the lens of cultural, environmental, social, infrastructural, and economic attributes.

Methodology

Study Area

Hyderabad, the second largest city in Sindh, Pakistan was selected as the study area (Talpur et al. 2016), which is located between 25° 22′ 45″ North & 68° 22′ 6″ East on the globe (Talpur et al. 2017). It is among the top ten major cities of Pakistan on the basis of population, with 1,732,693 residents as per Census 2017 (Pakistan Bureau of Statistics. Goverment of Pakistan 2017). Hyderabad master plan 2007–2027 pointed out a severe housing backlog and miserable living conditions (Osmani and Company (Pvt.) Ltd., 2008). The geographical location of Hyderabad city is shown in Fig. 1.
Fig. 1

Study area

The urban domain in Pakistan can be stratified into low, middle and high-income groups (Pakistan Bureau of Statistics 2016). Thus, stratified sampling was adopted to select urban settlements on the basis of income groups (Jain and Hausman 2006;Yakubu et al. 2014). The study area was divided into three residential categories, i.e. low, middle, and high income. Two neighborhoods were selected randomly from each income group, as advised by the Planning and Development Department of Hyderabad, Pakistan and the Department of City, and Regional Planning, Mehran University of Engineering and Technology Jamshoro Sindh. By using stratified sampling, six different housing settlements were selected for the present study (Table 1). Figure 1 shows location of each housing scheme.
Table 1

Selected housing schemes

Name

Abbreviation

Income Group

Defense Housing Society, Cantonment

DHS

Upper class

Muslim Cooperative Housing Society Qasimabad

MCHS

Upper class

HDA Employees Cooperative Housing Society Phase 1 (Kohsar Extension Housing Scheme)

HDA-ECHS

Middle class

Gulshan-e-Zeel Pak Cooperative Housing Society

GPCHS

Middle class

Bhitai Town

BT

Lower class

Rehman Town

RT

Lower class

Selection of Components and Variables for Livability Satisfaction Level

Cities across the world have been emphasizing on their livability scores to attract people, which requires selection of factors influencing city well-being (Lee and Kim 2018). Sung and Phillips (2018) used concepts of community well-being and quality of life, and explains that the indicators of measuring well-being can be used to develop a tool to gauge community well-being. Hence, a wide range of indicators has been chosen through rigorous literature with the aim of measuring livability (Table 2).
Table 2

Presence of Livability related variables in the literature

Indicators

Empirical Evidences

1. Cultural attributes

Availability of restaurants

Okulicz-Kozaryn 2013; Litman 2015; Pandey et al. 2013; Khan and Javaid 2015 Saaty 1986; National Research Council 2002

Availability of public amenities (cinemas and clubs)

Okulicz-Kozaryn 2013; Pandey et al. 2014a, b, 2013; Mohit and Iyanda 2015; Saitluanga 2014; Khan and Javaid 2015; Li 2012; National Research Council 2002

Availability of worship places

Saitluanga 2014; Khan and Javaid 2015; National Research Council 2002

2. Environmental attributes

Availability of garbage collection services

Pandey et al. 2014a, 2013; Mahmoudi et al. 2015; Mohit and Iyanda 2015; Pandey et al. 2010; Teck-Hong 2012

Regular maintenance of public parks

Okulicz-Kozaryn 2013; Leby and Hashim 2010; Pandey et al. 2014a,b, 2013; Khan and Javaid 2015

Availability of parks and playgrounds

Okulicz-Kozaryn 2013; Leby and Hashim 2010; Pandey et al. 2014a; Mohit and Iyanda 2015; Saitluanga 2014; Khan and Javaid 2015; Li 2012; Shamsuddin et al. 2012; Saaty 1986; Teck-Hong 2012; National Research Council 2002

3. Social attributes

Security

Okulicz-Kozaryn 2013; The Economist Intelligence Unit (EIU) 2014; Litman 2015; United States Department of Transportation 2012; Leby and Hashim 2010; Pandey et al. 2014a, b, 2013; Ilesanmi 2012; Mohit and Iyanda 2015; Saitluanga 2014; Khan and Javaid 2015; Li 2012; Shamsuddin et al. 2012; Pandey et al. 2010; National Research Council 2002

Access to health facilities

Diener and Suh 1997; Saaty 1986; Okulicz-Kozaryn 2013; The Economist Intelligence Unit (EIU) 2014; Litman 2015; United States Department of Transportation 2012; Leby and Hashim 2010; Pandey et al. 2014a, b, 2013; Ilesanmi 2012; Mohit and Iyanda 2015; Saitluanga 2014; Khan and Javaid 2015; Shamsuddin et al. 2012; Pandey et al. 2010; National Research Council 2002

Provision and proximity to schools

Diener and Suh 1997; Okulicz-Kozaryn 2013; The Economist Intelligence Unit (EIU) 2014; Litman 2015; United States Department of Transportation 2012; Leby and Hashim 2010; Pandey et al. 2014a, b, 2013; Mohit and Iyanda 2015; Saitluanga 2014; Khan and Javaid 2015; Li 2012; Shamsuddin et al. 2012; National Research Council 2002

4. Infrastructural attributes

Regular maintenance of streets and neighborhood’s lighting

Okulicz-Kozaryn 2013; Litman 2015; United States Department of Transportation 2012; Leby and Hashim 2010; Pandey et al. 2013; Ilesanmi 2012; Mahmoudi et al. 2015; Mohit and Iyanda 2015; Saitluanga 2014; Khan and Javaid 2015; Li 2012; Shamsuddin et al. 2012; Pandey et al. 2010; National Research Council 2002

Reliability of utilities (electricity, water, gas)

Okulicz-Kozaryn 2013; Pandey et al. 2014a, 2013; Ilesanmi 2012; Mohit and Iyanda 2015; Saitluanga 2014; Khan and Javaid 2015; Li 2012; Pandey et al. 2010; National Research Council 2002

Availability of public transport

Okulicz-Kozaryn 2013; The Economist Intelligence Unit (EIU) 2014; Litman 2015; United States Department of Transportation 2012; Leby and Hashim 2010; Pandey et al. 2014a, 2013; Mahmoudi et al. 2015; Mohit and Iyanda 2015; Saitluanga 2014; Khan and Javaid 2015; Li 2012; Shamsuddin et al. 2012; Pandey et al. 2010; National Research Council 2002

5. Economic attributes

Availability of affordable housing

The Economist Intelligence Unit (EIU) 2014; United States Department of Transportation 2012; Pandey et al. 2014a, 2013; Mohit and Iyanda 2015; Khan and Javaid 2015; Li 2012; Shamsuddin et al. 2012; National Research Council 2002

Access to shops

Litman 2015; Leby and Hashim 2010; Ilesanmi 2012; United States Department of Transportation 2012; Pandey et al. 2014a; Mohit and Iyanda 2015; Khan and Javaid 2015; Li 2012; Shamsuddin et al. 2012; National Research Council 2002

Employment opportunities

Okulicz-Kozaryn 2013; Litman 2015; United States Department of Transportation 2012; Leby and Hashim 2010; Pandey et al. 2014a; Mahmoudi et al. 2015; Mohit and Iyanda 2015; Saitluanga 2014; Khan and Javaid 2015; Li 2012; National Research Council 2002; Diener and Suh 1997

By reviewing the literature on variables that affect the neighborhood livability satisfaction, this research study considers the five components of residential livability satisfaction, each having three variables – (1) cultural attributes; (2) environmental attributes; (3) social attributes; (4) infrastructural attributes; (5) economic attributes.

Sampling and Data Collection

Systematic sampling technique was adopted in order to collect data regarding livability (Hamdan et al. 2014). A sample of 290 households (n = 290) was selected from a total of 2878 households based on systematic sampling with an interval of (10) households (Alnsour and Meaton 2014). Household survey was conducted via face-to-face interviews. Questionnaire was developed using expert inputs from academia and field experts. The questionnaire included the basic demographic profile of respondents, and perception-based questions were asked on the Likert scale. A pre-testing of 10 questionnaires was also done to streamline it. The 5-point Likert scale based questions were arranged for finding the residents perceptions about livability attributes from 1 = not at all satisfied, to 5 = extremely satisfied (Pandey et al. 2014a); Mahmoudi et al. 2015; Pampanga et al. 2015). These attributes were categorized under cultural, environmental, social, infrastructure and economic dimensions. Descriptive statistics (mean score) method was adopted to calculate each attribute, and the results were arranged graphically. Moreover, a scale of 10 points on Likert scale was also structured and responses were recorded about their “perceived livability”. This was correlated with previously identified livability satisfaction variables, Thereafter, regression modeling was done to understand factors influencing perceived livability of residents (Anderson et al. 2012; Mohit and Iyanda 2015; Tao 2015).

Results and Discussion

Respondents of this research were predominantly males (81%) as compared to females (19%). The majority of the respondents (35.2%) belonged to mature age group (31–40 years), while 30.4% respondents were from 18 to 30 years age group, 27.2% belonged to the 41–50 years age group and only 7.2% respondents were of age range 51–60 years. Majority of residents were college graduates or more (70%), while the rest had attained higher secondary (25.9) or secondary education (4.1%) only. Most of the respondent’s income level was in range of 21,000–50,000 PKR, which constituted about 41% of the total population. Most of the inhabitants of selected housing schemes had their own houses (66.6%), against rented ones (33.4%) which means that most of the respondents were permanent residents of that area (Table 3).
Table 3

Respondent’s socio-demographic details

Socio-demographic characteristics

Frequency

Percentage

Gender

Male

Female

235

55

81%

19%

Age

18–30

31–40

41–50

51–60

88

102

79

21

30.4%

35.2%

27.2%

7.2%

Education level

Secondary

Higher secondary

Graduate or more

12

75

203

4.1%

25.9%

70%

Monthly Household Income (In PKR*)

<20,000

21,000–50,000

>50,000

87

119

84

30%

41%

29%

House ownership

Owned

Rented

193

97

66.6%

33.4%

Field Survey, 2016

*1 USD = 123.24 PKR (Sept, 2018)

Cultural Attributes

Availability of restaurants, public amenities and place of worship were considered as components for cultural attributes of livability (Fig. 2). The result showed that satisfaction regarding the availability of worship places had highest mean scores in all six neighborhoods (Upper class; DHS and MCHS, middle class; HDA-ECHS and GPCHS, lower class; BT and RT). In contrast, satisfaction regarding the availability of restaurants was deemed poor by residents of both middle income and the lower income neighborhood. Such score implies lack of availability of restaurants in these neighborhoods. In addition, both upper-class neighborhoods depicted above-average satisfaction related to availability of restaurants (Fig. 2). Regarding availability of public amenities, the lowest satisfaction for this indicator was recorded in upper class neighborhoods, and was slightly higher in lower class neighborhoods. As compared to other four neighborhoods, middle class neighborhoods gave higher score related to the availability of public amenities (Table 4).
Fig. 2

Cultural attributes

Table 4

Descriptive statistics of indicators

Indicators

Upper class income group

Middle class income group

Lower class income group

 

DHS

 

MCHS

 

GPCHS

 

HDA-ECHS

 

RT

 

BT

 
 

M

S.D

M

S.D

M

S.D

M

S.D

M

S.D

M

S.D

1. Cultural Attributes

Availability of restaurants

4.40

.604

3.00

.816

2.26

.979

1.69

.959

2.10

.900

2.48

1.06

Availability of cinemas

4.06

.684

2.77

.832

4.01

.847

1.43

.901

2.03

1.00

2.30

1.07

Availability of place of worship

5.00

.000

4.46

.519

4.89

.312

4.67

.543

4.43

.747

4.61

.556

2. Environmental Attributes

Availability of garbage collection service

4.14

.355

4.08

.277

3.07

1.051

3.16

1.33

1.80

1.11

2.30

1.31

Maintenance of public parks

4.46

.505

4.15

.376

2.09

.733

3.24

1.47

1.93

.888

2.21

1.02

Availability of parks and play grounds

4.49

.507

4.15

.376

2.16

1.092

3.28

1.19

1.98

1.07

2.06

1.08

3. Social Attributes

Security of the neighborhood

4.66

.482

4.23

.599

3.44

.839

3.72

1.03

2.65

1.51

2.36

1.29

Access to health facilities

4.31

.471

4.00

.000

2.89

.835

3.79

1.12

2.40

.871

2.67

.990

Proximity to schools

5.00

.000

4.15

.376

3.20

1.189

3.88

.957

2.40

1.21

2.76

1.14

4. Infrastructural Attributes

Maintenance of streets and neighborhood’s lighting

4.11

.323

3.92

.277

2.98

1.120

3.28

1.32

2.48

1.13

2.18

1.28

Reliability of utilities

4.46

.505

4.15

.555

2.98

.934

3.72

1.07

2.60

.98

2.79

1.05

Availability of utilities

3.14

.430

3.38

.506

2.71

1.131

2.07

1.10

2.93

.971

2.15

1.00

5. Economic Attributes

Affordability of housing

3.31

.471

3.31

.480

2.24

1.259

3.10

1.10

2.07

1.16

2.30

1.13

Access to shops

3.97

.453

3.62

.506

2.59

.780

3.52

1.23

3.03

1.07

3.00

1.27

Employment opportunities

3.77

.877

3.15

.376

2.44

1.165

2.67

1.28

2.48

1.08

2.52

1.22

Perceived liveability

8.23

.690

7.54

.519

5.86

1.116

7.09

1.67

5.33

1.22

5.45

1.46

M Mean value

S.D. Standard deviation

Environmental Attributes

The difference amid resident’s level of satisfaction towards the neighborhood environment resulted in a mixed score regarding environmental indicators (Fig. 3). Residents of upper-class neighborhoods (DHS and MCHS) were most satisfied for all three indicators, including availability of garbage collection service, regular maintenance of public parks and availability of parks and playgrounds. Only one middle class neighborhood (HDA-ECHS) gave high scores for all three indicators, but on the other hand, a middle class neighborhood (GPCHS) gave low satisfaction for the availability of garbage collection, and below-average scores for remaining two indicators. Both of the lower income group neighborhoods (BT and RT) showed poor condition of environmental attributes in their areas (Fig. 3). Generally, it was observed that residents living in high income neighborhoods were most satisfied with their environment, and this satisfaction fell in the middle and low income neighborhoods. This can be due to the fact that residents of high income neighborhoods reside in large dwelling units, and they have ample open spaces (low densities). In lower income neighborhoods, smaller plots result in higher densities, and hence perceived low satisfaction regarding their immediate surroundings.
Fig. 3

Environmental attributes

Social Attributes

Livability assessment regarding social attributes included security, access to health facilities, and provision and proximity to schools (Fig. 4). Both upper-class neighborhoods considerably gave high scores for all three selected social attributes of livability. While, middle-class urban settlements depicted slightly above-average scores for security attribute (Table 4). In contrast, lower class neighborhoods scored the least in the security. Similarly, the upper-class housing units had the easiest access to health facilities (Fig. 4). The middle-income group had mean values of >2.5, implying above average satisfaction regarding access to health facilities. However, residents from lower income settlements gave poor satisfaction regarding the same indicator. Proximity to school attribute represented the comfort level of citizens’ access to school. DHS citizens (upper class) had the easiest access to school, which can also be verified from the results, i.e. “fully satisfied” with a value of 5.00. While low-income residents gave lowest scores in schools accessibility, as compared to middle-income and upper-income groups.
Fig. 4

Social attributes

3.4 Infrastructural Attributes

Infrastructural attributes included regular maintenance of streets and neighborhood’s lighting, reliability of utilities (electricity, water and gas) and availability of public transport (Fig. 5). Lower income settlements secured least scores in regular maintenance of streets. On the other hand, considerably better situation was observed in all other selected housing schemes. Regarding reliability of utilities (electricity, water and gas), lower income neighborhoods depicted poor scores; while, the high income households gave highest score to the parameter, i.e. reliability of infrastructural amenities. Within middle-income groups, one neighborhood (HDA-ECHS) gave good score, while the other did not. Availability of public transports varied with poor scores in all neighborhoods, except the BT (lower class), which had relatively higher scores for this indicator. In all other neighborhoods, mean score indicated lack of availability of public transport (Table 4).
Fig. 5

Infrastructural attributes

Economic Attributes

Economic attributes included availability of affordable housing, access to shops and employment opportunities (Fig. 6). These indicators varied according to stratified neighborhoods in the city. The satisfaction regarding availability of affordable housing in low-income settlements was very low. Only one middle income class neighborhood (HDA-ECHS) showed a better score. This implies that even people lived in upper class neighborhoods, were dissatisfied from the parameter of “housing affordability”. For the indicator of access to shops, all six neighborhoods showed better scores (Fig. 6). Regardless of economic status, GPCHS (middle class) showed the least access to shops as compared to lower class neighborhood, which showed relatively high mean values for access to shops. In terms of access to employment opportunities, upper class neighborhoods scored best. Whereas, the mean scores for employment opportunities in other neighborhoods varied, which implied fluctuated level of satisfaction for employment opportunities (Table 4).
Fig. 6

Economic attributes

Relationship of Attributes with Perceived Livability

Overall Perceived Livability

The overall perceived livability varied among selected neighborhoods. The highest rating was found for the parameter “perceived livability” in DHS (upper class). In addition, it was noted that both lower-income neighborhoods (BT and RT) were least satisfied with respect to livability criterion (Fig. 7). This implied that people in lower class neighborhood perceived poor living conditions or miserable quality of life in their settlements. Likewise, middle-class neighborhoods showed slightly higher satisfaction level as compared to lower class neighborhoods.
Fig. 7

Overall perceived livability on 10 points likert scale

Overall Perceived Livability and Livability Satisfaction Variables

Pearson correlation technique was utilized to find out the relationship between the overall perceived livability and livability satisfaction variables (Mohit et al. 2010). Table 5 indicates that perceived livability was significantly correlated (p < 0.05) with availability of restaurants, garbage collection service, parks and grounds, utilities, employment, and proximity to schools. Whereas, it was highly significantly correlated (p < 0.01) with certain indicators, like maintenance of parks and lighting, security, health facilities, reliability on amenities, housing affordability and commercial accessibility.
Table 5

Pearson correlation coefficients between overall perceived livability and livability satisfaction indicators

Livability Indicators

Pearson Correlation with overall perceived livability

X1

Availability of restaurants

0.259*

X2

Availability of cinemas

0.105

X3

Availability of place of worship

0.104

X4

Availability of garbage collection service

0.492*

X5

Maintenance of public parks

0.534**

X6

Availability of parks and playgrounds

0.523*

X7

Security of the neighborhood

0.343**

X8

Access to health facilities

0.524**

X9

Proximity to schools

0.517*

X10

Maintenance of streets and neighborhood’s lighting

0.372**

X11

Reliability of utilities

0.409**

X12

Availability of utilities

0.209*

X13

Affordability of housing

0.427**

X14

Access to shops

0.401**

X15

Employment opportunities

0.392*

**correlation is significant at 1%

*correlation is significant at 5%

Factors Influencing Overall Perceived Livability

Multiple regression model was estimated to determine the best linear combination of 15 resident livability satisfaction variables for predicting (overall) perceived livability. The multi-linear regression model suggested the selection of eight variables which were influencing perceived livability. Two variables i.e. availability of garbage collection service and maintenance of public parks were greatly influencing perceived livability (p < 0.01), while other six variables were also found significant at p < 0.05, i.e. availability of worship place, availability of parks and playgrounds, access to health facilities, proximity to schools, availability of utilities and employment opportunities (Table 6).
Table 6

Regression analysis of livability satisfaction variables and overall perceived livability

R = 0.718, R Square = 0.516, Adjusted R Square = 0.489, Std. Error of the Estimate = 1.122,

Multiple Regression Model

Unstandardized Coefficients

t

Sig.

β

Std. Error

 

1.184

.695

1.705

.089

X1

Availability of restaurants

.005

.068

.079

.937

X2

Availability of cinemas

−.050

.060

−.835

.405

X3

Availability of place of worship

.277

.141

1.969

.050**

X4

Availability of garbage collection service

.221

.073

3.010

.003***

X5

Maintenance of public parks

.250

.071

3.519

.001***

X6

Availability of parks and playgrounds

.168

.067

2.499

.013**

X7

Security of the neighborhood

−.103

.076

−1.361

.175

X8

Access to health facilities

.151

.086

1.745

.082*

X9

Proximity to schools

.162

.075

2.144

.033**

X10

Maintenance of streets and neighborhood’s lighting

−.009

.074

−.120

.904

X11

Reliability of utilities

.046

.079

.587

.558

X12

Availability of utilities

.146

.066

2.201

.029**

X13

Affordability of housing

.105

.069

1.527

.128

X14

Access to shops

.102

.078

1.311

.191

X15

Employment opportunities

.155

.067

2.326

.021**

a = Dependent Variable: Overall perceived livability

β = Predictors: (Constant); Significance level *p < 0.10, **p < 0.05, ***p < 0.01

The independent variables significantly predicted the dependent variable with F(15, 274) = 19.448, p < 0.05 (i.e. the regression model is a good fit of the data) with all eight variables. The R2 value (0.516) imply that 51.6% of the variance in residential livability satisfaction was explained by the model. The tolerance values of the coefficients of predictor variables were recorded well over 0.484 (1-R2). This shows a low level of multicollinearity among the predictors of the model. The beta weights presented in Table 6 suggested that the resident’s perception of livability in selected housing schemes was greatly influenced by selected indicators. The results showed that all three environmental factors of livability (maintenance of public parks, availability of garbage collection service, availability of parks and playgrounds) are significant in building the overall perception of livability. Moreover, two social factors (proximity to schools, access to health facilities), one economic factor (employment opportunities), one cultural (availability of place of worship) are also significant.

In general, the variables X1, …….,Xp-i in a regression model have to represent different independent variables, therefore the definition of general multiple linear regression model, with normal error terms, simply in terms of X variable is (Qureshi et al. 2016; Schneider et al. 2010)

$$ Y={\beta}_0+{\beta}_1{X}_{i1}+{\beta}_2{X}_{i2}+\bullet \bullet \bullet \bullet \bullet \bullet \bullet \bullet \bullet \bullet \bullet \bullet \bullet \bullet \bullet \bullet \bullet \bullet \bullet +{\beta}_{p-1}{X}_{i,p-i}+{\varepsilon}_i $$
(1)

Where:

β0 is Y- intercept; β1, β2, β3, βp are the parameters of the regression equation; εi is a random error in Y for observations i.

By adopting the values shown in Table 5 we get the regression model as;
$$ Y=1.184+0.005{X}_1-0.050{X}_2+0.277{X}_3+0.221{X}_4+0.250{X}_5+0.168{X}_6-0.103{X}_7+0.151{X}_8+0.162{X}_9-0.009{X}_{10}+0.046{X}_{11}+0.146{X}_{12}+0.105{X}_{13}+0.102{X}_{14}+0.155{X}_{15} $$
(2)

Overall findings of the research indicated that perceived livability by the residents varied from place to place, and attributes of livability are correlated with perceived livability. Regression model predicts that livability perceived by residents is highly affected by availability of garbage collection service and maintenance of public parks. The selected indicators can play vital role in assessment of living conditions in either planned neighborhoods or slums.

In general, the findings of the present study show similarities with those found in literature, although they do not corroborate all of them. Findings support results of previous studies which validated that the residential satisfaction is related with suitable living conditions in neighborhoods (Mohit et al. 2010; Mohit and Iyanda 2015, 2016). The significant relationship of cultural attribute (availability of place of worship) with quality of life supports the claim of Hamdan et al. (2014) that socio-cultural dimension must be considered in augmenting the quality of life in Malaysian neighborhoods. Other variables, like provision and proximity of schools and access to health facilities observed in this study also corroborate previous studies (Leby and Hashim 2010).

Findings of this research also back the South Korean study by Lee and Kim (2018), which revealed that the social and administrative factors (same as environmental attributes for present study) are important for enhancing the overall quality of life. A study by Zhan et al. (2018) revealed the urban livability determinants in Chinese context, which also shows resemblance with results of this study. Overall, lower mean scores for livability satisfaction by lower income neighborhoods were seen, confirming similar situation of housing and neighborhoods in Nigeria and Ghana (Yakubu et al. 2014; Ilesanmi 2012). Variances and disparities in livability satisfaction level among neighborhoods in this study also supports arguments of Saitluanga (2014). However, Leby and Hashim (2010) found security as important predictor of perceived livability in Malaysian neighborhoods, which was in contrast with this study. Tao (2015) found economic attributes are important for better housing and living conditions, which contradicts with the present survey.

Overall, results indicated that environmental attribute is the most important component affecting livability assessment. The standardized coefficients reveal the weighting of the dimensions for overall livability, which can provide a new perspective to look at the key issues of the urban neighborhood planning and design. Among the five different variables of livability, environmental attributes were found the most significant factors influencing perceived livability. Thus, this research suggested the high weightage of environmental dimension of livability for future studies. Besides environmental factor of livability, cultural, social and economic variables also showed promising values in the eyes of residents for a more livable neighborhood. In the light of this study, it is indicated that future neighborhood designs should promote a sense of cultural identity and social cohesion. On the other hand, economic attributes exhibited relative importance about the livability criterion of housing settlements, especially when these are planned near to employment opportunities.

Conclusion

The main goal of this research was to measure the perceived livability by the residents of Hyderabad, Pakistan. The study has tried to enhance our understanding of livability by highlighting indicators which influence perceived livability in urban neighborhoods. In Pakistan, there is no national or provincial urban planning regulatory authority. This makes things difficult for urban planners to follow neighborhood design regulations, which can vary from city to city, and province to province (Ahmad and Anjum 2012; Rana and Bhatti 2018). Empirical evidences suggest that the environmental component must be acknowledged in neighborhood design, so as to improve livability perception of urbanites. Taken together, the study results suggest that the resident’s satisfaction varied moderately with availability of worship places, parks and playgrounds, health facilities, schools, utilities and employment opportunities. In general, the highest satisfaction level was observed among upper-class neighborhoods as compared to the middle-class neighborhoods and lower income neighborhoods. Public and private agencies should pay more attention to the management of services and planning design aspects of housing schemes in order to enhance the livability satisfaction level in every tier of society.

This study must acknowledge its limitations. Livability is a complex phenomenon and mere five attributes (or 15 indicators) on a neighborhood level cannot envelop whole livability or quality of life concept. This can be further enhanced by incorporating more dimensions and indicators for a better reflection of livability concept. This research was also limited as the respondents were predominantly males. However, it has tried its best to highlight the perceived livability in various housing schemes. The attributes and indicators can help urban planners in designing livable neighborhoods. The methodology adopted in this study can be useful for measuring satisfaction of residents across spatial and temporal dimensions. Regression model used in this study on resident’s perceived livability is based on the selected attributes. In the future, more indicators, such as urban noise, air/water quality, climatic conditions, social relationships and frequency of public transportation, can also be added to enhance the livability concept for new emerging Asian cities.

Notes

Acknowledgements

Authors are thankful to Department of City and Regional Planning, MUET, Jamshoro and Hyderabad Development Authority (HDA) Sindh, Pakistan for providing assistance and support in questionnaire based survey. First author would also like to gratefully acknowledge scholarship for postgraduate by China Scholarship Council (CSC) at Dalian University of Technology, China. Authors would like to convey thanks to anonymous reviewers for their valuable suggestions.

Compliance with Ethical Standards

Disclosure Statement

Authors have no conflict of interest.

References

  1. Ahmad, N., & Anjum, G. A. (2012). Legal and institutional perplexities hampering the implementation of urban development plans in Pakistan. Cities, 29(4), 271–277.  https://doi.org/10.1016/j.cities.2011.07.006.Google Scholar
  2. Alnsour, J., & Meaton, J. (2014). Housing conditions in Palestinian refugee camps, Jordan. Cities, 36, 65–73.  https://doi.org/10.1016/j.cities.2013.10.002.Google Scholar
  3. Anderson, D. R., Sweeney, D. j., & Williams, T. A. (2012). Essentials of modern business statistics. IEEE transactions on information theory (Vol. 58). Canada: Cengage Learning Retrieved from http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6071007. Accessed 25 Apr 2019.
  4. Balsas, C. J. L. (2010). Measuring the livability of an urban centre: an exploratory study of key performance indicators. In Measuring the livability of an urban Centre : An exploratory study of key performance indicators measuring the livability of an Urban Centre : An exploratory study of key performance indicators, (July 2013) (pp. 37–41).  https://doi.org/10.1080/0269745042000246603.Google Scholar
  5. Bandarabad, A., & Shahcheraghi, A. (2012). Livable street in urban environment: An adaptive design approach 1. Advances in Environmental Biology, 6(3), 1063–1067.Google Scholar
  6. Barrette, B. F. D. (2015). The World’s Most Liveable Cities - Our World. Retrieved February 28, 2019, from https://ourworld.unu.edu/en/the-worlds-most-liveable-cities.
  7. Diener, E. D., & Suh, E. (1997). Measuring quality of life: Economic, social, and subjective indicators. Social Indicators Research, 40(1/2), 189–216.  https://doi.org/10.1023/A:1006859511756.Google Scholar
  8. Godschalk, D. R. (2017). Land use planning challenges: Coping with conflicts in visions of sustainable development and livable. Communities, 4363(August), 5–13.  https://doi.org/10.1080/01944360408976334.Google Scholar
  9. Grieve, J., & Weinspach, U. (2010). Capturing impacts of leader and of measures to improve quality of life in rural areas. Retrieved from http://literatur.ti.bund.de/digbib_extern/dn049596.pdf. Accessed 28 Nov 2018.
  10. Hamdan, H., Yusof, F., & Marzukhi, M. A. (2014). Social capital and quality of life in urban neighborhoods high density housing. Procedia - Social and Behavioral Sciences, 153, 169–179.  https://doi.org/10.1016/j.sbspro.2014.10.051.Google Scholar
  11. Haq, M. (2014). The Rise of Karachi as a Mega-City : Issues and Challenges, (September). Retrieved from www.mhhdc.org. Accessed 28 Nov 2018.
  12. Ilesanmi, A. O. (2012). Housing, Neighbourhood quality and quality of life in public housing in Lagos. Nigeria. Journal for Housing Science, 36(4), 231–240.Google Scholar
  13. Jain, A. K., & Hausman, R. E. (2006). Stratified multistage sampling. In Encyclopedia of Statistical Sciences. Hoboken: John Wiley & Sons, Inc..  https://doi.org/10.1002/0471667196.ess2604.pub2.Google Scholar
  14. Kaal, H. (2011). A conceptual history of livability. City, 15(5), 532–547.  https://doi.org/10.1080/13604813.2011.595094.Google Scholar
  15. Khan, N. A., & Javaid, S. (2015). A Comparative Analysis of Livability in Bahria Town, Sukh Chayn Gardens and Lake City , Lahore. Lahore: University of Engineering and Technology.Google Scholar
  16. Leby, J. L., & Hashim, A. H. (2010). Liveability dimensions and attributes: Their relative importance in the eyes of Neighbourhood residents. Journal of Construction in Developing Countries, 15(1), 67–91.Google Scholar
  17. Lee, S. J., & Kim, Y. (2018). Economy Doesn’t buy community wellbeing: A study of factors shaping community wellbeing in South Korea. International Journal of Community Well-Being, 1, 33–44.  https://doi.org/10.1007/s42413-018-0004-2.Google Scholar
  18. Li, Y. (2012). Neighborhood amenities, satisfaction, and perceived livability of foreign-born and native-born U.S. Residents. Journal of Identity & Migration Studies, 6(1), 115–137 Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=poh&AN=85526364&site=ehost-live&scope=site. Accessed 28 Nov 2018.
  19. Litman, T. (2015). Well measured: Developing indicators for sustainable and livable transport planning. Retrieved from http://www.worldcat.org/title/well-measured-developing-indicators-for-sustainable-and-livable-transport-planning/oclc/776812630. Accessed 28 Nov 2018.
  20. Mahmoudi, M., Ahmad, F., & Abbasi, B. (2015). Livable streets: The effects of physical problems on the quality and livability of Kuala Lumpur streets. Cities, 43, 104–114.  https://doi.org/10.1016/j.cities.2014.11.016.Google Scholar
  21. Mercer. (2018). Mercer 2018 quality of living survey worldwide quality of living ranking. Retrieved from www.mercer.com. Accessed 25 Apr 2019.
  22. Mohit, M. A., & Iyanda, S. A. (2015). City liveability and housing in Nigeria: a case study of low-income housing in Niger State. In: AicQoL2015Jakarta, 25-27 April 2015, AMER International Conference on Quality of Life The Akmani Hotel, Jakarta, Indonesia Abstracts ASEAN-Turkey ASLI Conferences on QoL 2015 AicQoL2015Jakarta. https://irep.iium.edu.my/43112/. Accessed 28 Nov 2018.
  23. Mohit, M. A., & Iyanda, S. A. (2016). Liveability and low-income housing in Nigeria. Procedia - Social and Behavioral Sciences, 222, 863–871.  https://doi.org/10.1016/j.sbspro.2016.05.198.Google Scholar
  24. Mohit, M. A., Ibrahim, M., & Rashid, Y. R. (2010). Assessment of residential satisfaction in newly designed public low-cost housing in Kuala Lumpur , Malaysia. Habitat International, 34(1), 18–27.  https://doi.org/10.1016/j.habitatint.2009.04.002.Google Scholar
  25. National Research Council. (2002). Community and quality of life. National Academy of Sciences. Washington, D.C.: National Academies Press.  https://doi.org/10.17226/10262.Google Scholar
  26. Okulicz-Kozaryn, A. (2013). City life: Rankings (livability) versus perceptions (satisfaction). Social Indicators Research, 110(2), 433–451.  https://doi.org/10.1007/s11205-011-9939-x.Google Scholar
  27. Okulicz-Kozaryn, A., & Valente, R. R. (2019). Livability and subjective well-being across European cities. Applied Research in Quality of Life, 14(1), 197–220.  https://doi.org/10.1007/s11482-017-9587-7.Google Scholar
  28. Osmani and Company (Pvt.) Ltd.Consulting Engineers Architects and Planners. (2008). Inception Report: Hyderabad Master Plan (2007–2027).Google Scholar
  29. Pakistan Bureau of Statistics. (2016). Pakistan social and living standards measurement survey. Government of Pakistan Statistics Division. Islamabad. Retrieved from http://www.pbs.gov.pk/content/pakistan-social-and-living-standards-measurement. Accessed 28 Nov 2018.
  30. Pakistan Bureau of Statistics. (2017). Population census 2017. Retrieved from http://www.pbscensus.gov.pk/. Accessed 28 Nov 2018.
  31. Pakistan Bureau of Statistics. Goverment of Pakistan. (2017). Population of major cities census - 2017 population top 10 cities. Retrieved from http://www.pbscensus.gov.pk/. Accessed 28 Nov 2018.
  32. Pampanga, D. G., Majid, M. R., & Johar, F. (2015). Appropriate urban livability indicators for metropolitan Johor, Malaysia via expert-stakeholder approach: A Delphi technique. International Journal of Built Environment and Sustainability, 2(4), 301–316.  https://doi.org/10.11113/ijbes.v2.n4.98.Google Scholar
  33. Pandey, R. U., Garg, Y. K., & Bharat, A. (2010). A framework for evaluating residential built environment performance for livability. Institute of Town Planners, India Journal, 7(December), 12–20.Google Scholar
  34. Pandey, R. U., Garg, Y. K., & Bharat, A. (2013). Understanding qualitative conceptions of livability : An Indian perspective. International Journal of Research in Engineering and Technology, 2(12), 374–380.Google Scholar
  35. Pandey, R. U., Garg, Y. K., & Bharat, A. (2014a). Quantitative approach for understanding perspectives on livability in Indian context. International Journal on Emerging Technologies, 5(1), 1–7.Google Scholar
  36. Pandey, R. U., Garg, Y. K., & Bharat, A. (2014b). Understanding dependency of livability on socio-economic and demographic parameters. International Journal of Humanities and Social Sciences (IJHSS)\t, 3(1), 61–68 Retrieved from http://www.iaset.us/view_archives.php. Accessed 28 Nov 2018.
  37. Parmar, V., & Jalbani, A. A. (2005). Investment trends in Hyderabad, Pakistan. Journal of Independent Studies and Research (JISR), 3(2), 29–32 Retrieved from The investment trend in Pakistan fluctuated from province.Google Scholar
  38. Qasim, M., & Zaidi, S. S. (2013). Ensuring sustainable development through urban planning in Pakistan. Mehran University Research Journal of Engineering & Technology, 32(2), 207–220.Google Scholar
  39. Qureshi, N. A., Magsi, W. A., Suthar, V., Lakho, M. H., Bukero, A., & Solangi, B. K. (2016). Comparative study of factors influencing the performance of students in statistics using multiple linear regression approach. Sindh University Research Journal (Science Series), 48(3), 487–490.Google Scholar
  40. Rana, I. A., & Bhatti, S. S. (2018). Lahore, Pakistan – Urbanization challenges and opportunities. Cities, 72(September), 348–355.  https://doi.org/10.1016/j.cities.2017.09.014.Google Scholar
  41. Rana, I. A., Bhatti, S. S., & Arshad, H. S. H. (2017a). Assessing the socioeconomic and infrastructure development disparity – A case study of city districts of Punjab, Pakistan. International Journal of Urban Sustainable Development, 9(3), 346–358.  https://doi.org/10.1080/19463138.2017.1320286.Google Scholar
  42. Rana, I. A., Bhatti, S. S., & e Saqib, S. (2017b). The spatial and temporal dynamics of infrastructure development disparity – From assessment to analyses. Cities, 63, 20–32.  https://doi.org/10.1016/j.cities.2016.12.020
  43. Ruth, M., & Franklin, R. S. (2014). Livability for all? Conceptual limits and practical implications. Applied Geography, 49, 18–23.  https://doi.org/10.1016/j.apgeog.2013.09.018.Google Scholar
  44. Saaty, T. L. (1986). Absolute and relative measurement with the AHP. The most livable cities in the United States. Socio-Economic Planning Sciences, 20(6), 327–331.Google Scholar
  45. Saitluanga, B. L. (2014). Spatial pattern of urban livability in Himalayan region: A case of Aizawl City, India. Social Indicators Research, 117(2), 541–559.  https://doi.org/10.1007/s11205-013-0362-3.Google Scholar
  46. Schneider, A., Hommel, G., & Blettner, M. (2010). Linear regression analysis: Part 14 of a series on evaluation of scientific publications. Deutsches Ärzteblatt International, 107(44), 776–782.  https://doi.org/10.3238/arztebl.2010.0776.Google Scholar
  47. Shamsuddin, S., Hassan, N. R. A., & Bilyamin, S. F. I. (2012). Walkable environment in increasing the Liveability of a City. Procedia - Social and Behavioral Sciences, 50(July), 167–178.  https://doi.org/10.1016/j.sbspro.2012.08.025.Google Scholar
  48. Sung, H., & Phillips, R. G. (2018). Indicators and community well-being: Exploring a relational framework. International Journal of Community Well-Being, 1, 63–79.  https://doi.org/10.1007/s42413-018-0006-0.Google Scholar
  49. Talpur, M. A. H., Chandio, I. A., Jumani, M. S., & Napiah, M. (2016). Planning information system for rural Transport Planning Agencies. Sindh University Research Journal (Science Series), 48(1), 67–70.Google Scholar
  50. Talpur, M. A. H., Chandio, I. A., Baig, F., Shaikh, F., & Napiah, M. (2017). Energy crisis and Household’s Perception about Solar Energy Acceptance: District Hyderabad, Pakistan. Sindh University Research Journal (Science Series), 49(3), 601–604 Retrieved from http://sujo.usindh.edu.pk/index.php/SURJ/article/view/3635. Accessed 28 Nov 2018.
  51. Tao, L. W. (2015). Living conditions—The key issue of housing development in Beijing Fengtai District. HBRC Journal, 11(1), 136–142.  https://doi.org/10.1016/j.hbrcj.2014.07.003.Google Scholar
  52. Teck-Hong, T. (2012). Housing satisfaction in medium- and high-cost housing: The case of greater Kuala Lumpur, Malaysia. Habitat International, 36(1), 108–116.  https://doi.org/10.1016/j.habitatint.2011.06.003.Google Scholar
  53. The Economist Intelligence Unit (EIU). (2014). A Summary of the Liveability Ranking and Overview August 2014. The Economic Intelligence Unit. Retrieved from www.eiu.com. Accessed 25 Apr 2019.
  54. The Economist Intelligence Unit (EIU). (2018). The global Liveability index 2018 a free overview. The Economist Intelligence Unit (EIU). Retrieved from http://www.eiu.com/Handlers/WhitepaperHandler.ashx?fi=The_Global_Liveability_index_2018.pdf&mode=wp&campaignid=Liveability2018. Accessed 28 Nov 2018.
  55. United States Department of Transportation. (2012). The role of transportation systems management & operations in supporting livability and sustainability.Google Scholar
  56. Wei, Z., & Chiu, R. L. H. (2018). Livability of subsidized housing estates in marketized socialist China: An institutional interpretation. Cities, 83(May), 1–10.  https://doi.org/10.1016/j.cities.2018.06.013.Google Scholar
  57. Yakubu, I., Akaateba, M. A., & Akanbang, B. A. A. (2014). A study of housing conditions and characteristics in the tamale metropolitan area, Ghana. Habitat International, 44, 394–402.  https://doi.org/10.1016/j.habitatint.2014.08.003.Google Scholar
  58. Yin, L., & Yin, Y. (2009). Research on Assessment of City Livability Based on Principle Component Analysis - Taking Shandong Province for Example. In 2009 International Conference on Management and Service Science (pp. 1–4). IEEE.  https://doi.org/10.1109/ICMSS.2009.5301952.
  59. Zhan, D., Kwan, M.-P., Zhang, W., Fan, J., Yu, J., & Dang, Y. (2018). Assessment and determinants of satisfaction with urban livability in China. Cities, 79(March), 92–101.  https://doi.org/10.1016/j.cities.2018.02.025.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Transportation and LogisticsDalian University of TechnologyDalianChina
  2. 2.Department of Urban and Regional Planning, School of Civil and Environmental EngineeringNational University of Sciences and Technology (NUST)IslamabadPakistan
  3. 3.Department of City and Regional PlanningMehran University of Engineering and TechnologyJamshoroPakistan

Personalised recommendations