Information and Communications Technology (ICT) affects most of the cultural, social, economic, and physical dynamics of cities and makes the future of urban development difficult to predict. In the digital era, walkability needs to be preserved as a principle in urban design. Tajrish and Velenjak Neighborhoods in District One of Tehran Municipality (TVNDOTM) possess walkable neighborhood qualities and currently, ICT can change these qualities more than before. This paper seeks to find various scenarios that explain the impact of ICT on the walkability in TVNDOTM by studying the future status of major factors. Walkability indices in our case study were refined based on the views of 32 experts selected with the snowball technique. MicMac and ScenarioWizard were used to determine the key drivers and identify scenarios with consistent components, respectively. Results show that the future of walkability in TVNDOTM under the effects of ICT is not precisely predictable and both states of weakening or strengthening the walkability are probable. This research encourages urban planners and designers to make a conscious effort in facilitating the favorable scenario to take place in urban neighborhoods among all the possible ways in which ICT changes urban walkability.
Research Article
Walkability in Digital Era: A ‘Futures Study’ in Tehran
https://doi.org/10.21203/rs.3.rs-1969759/v1
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Walkability
ICT
Futures Study
Scenario Development
Tehran
The rapid evolution of ICT and its fundamental effects on the spatial structure of cities, change urban lifestyle and form and trouble predicting the future (Yousefi and Dadashpoor 2020; Ben-Elia and Zhen 2018; Al-Ghamdi and Al- Harigi 2015). Additionally, core principles of Urban Design such as mixed-use and dense development and walkability are essential for future neighborhoods’ developments (Lehmann 2016; Al-Thani et al. 2018). This necessitates the study of the relationship between ICT and walkability changes in cities.
A Walkable neighborhood has certain characteristics in the perspective of several groups of scientists: in health literature, walkable neighborhoods meet the goal of an "active community" while in terms of sustainability, walkability seeks to reduce environmental impact, vehicle use, and energy consumption (Talen and Koschinsky 2013). Although the term “walkable” has been used since the 18th century, the concept of walkability dates back to recent times, especially to the last two decades (Wang and Yang 2019). There are several definitions of walkability in the literature. For example, walkability can be understood as a ‘‘match’’ between residents’ desires and expectations for types of destinations, their willingness to walk a given distance, and the quality of the required path. Neighborhoods that find this match between built form and residents’ needs will likely have more people walking in them (Manaugh and El-Geneidy 2011).
The main purpose of this study is to develop probable scenarios of ICT's impact on walkability in TVNDOTM based on the key drivers of the subject. The futures study and scenario planning approaches have already been used in urban studies and help to improve traditional methods of urban design (Amin-Tahmasbi and Saraf Jadidin 2019; Stojanović et al. 2014; Chakraborty and McMillan 2015). This paper seeks to answer two questions: What key drivers constitute the various scenarios that can explain the walkability of TVNDOTM under the impacts of ICT? And, which are the probable futures that might happen to the walkability in TVNDOTM due to increased usage of ICT?
In this section, two main domains of “Walkability indices”, and “ICT and its impact on walkability” have been reviewed; concluded by the authors’ insights on the literature review.
2.1. Walkability Indices
Over time, various methods have been proposed to identify a walkable neighborhood. The study of Frank et al. (2010) is as one of the first studies that tried to quantify walkability in cities and provide physical indices. Their criteria include land-use mix, street connectivity, net residential density, and retail Floor Area Ratio (FAR) and among them, the street connectivity is doubly important. Following this trend, many research projects during the last two decades tried to introduce the best and most accurate indices. Some studies examined walkability indices regarding trip types, as some experts believe that there is a relationship between the effectiveness of these indices and the purpose of the trip. Another set of indices examine the behaviour of pedestrians based on the importance and desirability of trip destinations; for example, daily destinations such as pharmacies or grocery stores have a greater impact on walkability than nightclubs and sports facilities (Manaugh and El-Geneidy 2011). A group of experts believes that the urban form has the greatest impact on the walking rate of residents (Lefebvre-Ropars et al. 2017) and it includes Density, Diversity, Design, Destination access, and Distance to transport stations (5Ds) (Ewing and Cervero 2010). This view is also emphasized in the study of Barros et al. (2017) in Lisbon, Portugal from the perspective of space syntax criteria. Another group of scientists introduced the classification of walkability criteria in the form of 7C’s[1] (Ruiz-Padillo et al. 2018).
Although most studies about walkability indices are done in the United States, Canada, Australia, China, and Belgium (Tong et al. 2016; Wang and Yang 2019), there is no complete and standard version of universal indices for all locations and times, and depending on the features of case study, research objectives and details of the study, a set of different indices should be used (Manaugh and El-Geneidy 2011; Wang and Yang 2019). For example, in a study of walkability factors in developing countries, Talaei and Taheri Amiri (2017) added greenness levels, shade, and visibility to urban landmarks to Frank's early indices. Numerous studies in South America showed that public security and traffic safety are particularly important in South America and should be added to walkability measurement metrics in these countries. Urban crime influences other physical and environmental aspects of walkability, therefore, public security is the most important index of walkability in Brazil, and traffic safety is in the second place (Ruiz-Padillo et al. 2018). Similarly, Bailey-Catalán et al. (2019) in their study of the city of Valparaiso in Chile, examined the NEWS-A[2] index and emphasized the importance of social factors such as fear of crime in South American cities. A third study in Latin America, after a ten-year review of the impact of artificial environment on walkability in a moderate city, concludes that for providing a walkable environment, traffic safety and security in South America are more important indices than the physical conditions of sidewalks and their attractiveness (Arellana et al. 2020). Recent studies attempted to complement existing indices with context-specific factors. For example, the question may arise about the factors that affect walkability in immigrant-friendly and multinational cities. Battista and Manaugh (2018) in their study in Quebec, Canada claim that apart from environmental factors, multicultural and social policies have a great impact on walking in the neighborhood and these indices should also be considered as important. While the European cities are generally more walkable than North American cities (Bödeker et al. 2018), another study in Norway (2019) based on the urban context in the country of case study, considers indices such as ‘infrastructure and traffic, urban planning and environment, and activities’ as a set of main indices to assess the level of walkability (Knapskog et al. 2019).
Over time, viewpoints on walkability grew more specialized, and its measurement parameters fluctuate in a range of small to large-scale, quantitative to qualitative (Talen and Koschinsky 2013), and leisure trips to business trips (Manaugh and El-Geneidy 2011). However, walkability studies overlook an important fact that, people's perceptions of neighborhood walkability may not always be in line with measurements results (Talen and Koschinsky 2013). For example, the Walk-Score index sparked a lot of controversy over the accuracy of scoring in American cities. The important point is that the score given to walkability in each neighborhood varies depending on the definition and how the indices are calculated. Therefore, walkability indices cannot be used comprehensively in all cities, but depending on the context, researchers need to select the best indices of pedestrians’ behavioral and social aspects. Another point is that walkability indices change over time, depending on the definition of walkability. For example, if we define walkability from the viewpoint of people's health, the impacts of environmental factors on it are more important than when we consider it as a mode of transportation. In the secondary viewpoint, accessibility factors become more important. Even the concepts and formulas for measuring indices vary depending on the researcher's definition of walkability (Shashank and Schuurman 2019).
2.2. ICT and Its Impact on Walkability
ICT services change the relationships between humans and their living environment while affecting social and economic development (Dadashpoor and Yousefi 2018). ICT may affect walkability in two general ways: first; technology as a problem that reduces walkability by transferring functions to the virtual world, and second; technology as a solution; a tool for strengthening walkability (Hankel et al. 2018). Due to the overlap among urban elements such as density, mixed land-use, access and transportation network, urban layout (how to connect urban spaces to each other) and housing characteristics, understanding the impacts of ICT on the spatial structure of cities will only be possible by considering all elements of this structure (Dempsey et al. 2010). In the following paragraphs, we have reviewed the impacts of ICT on various urban aspects that affect the walkability of neighborhoods.
ICT’s impact on urban public spaces: Increasing use of communication technologies reduced activity in public spaces and affected the way people interact with each other, and with physical spaces around them. Thus, public spaces are neglected and their attractiveness decreases resulting in declined social life. Social interactions through telecommunications on social media reduce the number of citizens' activities in the physical spaces (Abdel-Aziz et al. 2016). However, the real experience is irreplaceable, and the use of ICT devices (smartphones, computers, etc.) leads to the flexibility of urban nodes. In other words, citizens continue to use urban nodes, but their usage changes to leisure activities (Wang et al. 2015).
Information Technology (IT) affects the mental image of citizens and adds new dimensions to the legibility of the urban spaces (Al-ghamdi and Al-Harigi 2015). Digital tools allow their users to customize the urban spaces to their liking by applying software settings and in this way, they strengthen the sense of place. Urban designers should be able to use the potential of technological tools to enhance interactions in public spaces at the same time of following the principles of good design (Abdel-Aziz et al. 2016). Technology is capable of interacting with users of urban spaces, improving the quality of life and sustainability, and creating a sense of belonging by supporting neighborhoods’ independence (Al-Thani et al. 2018).
ICT can change face-to-face relationships or complement them by virtual communications (Konrad and Wittowsky 2018). Additionally, ICT allows people to develop their social relationships. Some researchers believe ICT caused virtual communication to replace face-to-face ones. Others believe reversed. While the third group thinks that substitutions and incremental effects of ICT on human communications neutralize each other and there is no significant relationship between ICT and face-to-face communications. Variety of Internet-based entertainments (computer games, virtual tourism, leisure in the virtual world, etc.) reduces people's activity in other types of leisure (Yousefi and Dadashpoor 2020) and the e-city reduces the presence of people in city centres by changing cultural and recreational spaces -from real to virtual spaces- (Ahani and Pourmohammadi 2019). After the transformation of IT into ICT, social networks were created with the ability to maintain social relations without the need for geographical proximity (Ben-Elia and Zhen 2018), and people socialize through cyberspace, instead of meeting each other in public spaces (Abdel-Aziz et al. 2016). Overall, ICT adds digital spaces to open spaces, and both of them compete for social interactions (Djukic et al. 2019). New communications opportunities and virtual communication media allow citizens to make better use of public spaces by saving their time (Abdel-Aziz et al. 2016). Despite the emergence and development of the IT network, face-to-face interactions in quality public spaces will not lose significance. Although modern societies do not depend on walking to provide their basic needs, they need suitable public spaces to ensure their mental and social health (Lehmann 2016). Cyberspace is effective in shaping real space and has made the concept of space more complex by overcoming temporal and spatial constraints.
ICT’s impact on urban land uses: ICT has changed traditional urban functions (work, transportation, housing, and leisure) (Yousefi and Dadashpoor 2020). E-city components have direct impacts on urban land use which can be an increase or decrease in the required spaces or decentralization from the city centre (Ahani and Pourmohammadi 2019). While ICT outspreads, existing urban land-uses are replaced by those with new features and significant changes occur in the performance of urban spaces (Yin et al. 2011). New features of urban land-uses are more visible in central and dense areas accommodating short trips (de Abreu e Silva and Melo 2018). E-city reduces the need for office and commercial spaces and disperses them from the city centre (Ahani and Pourmohammadi 2019).
Telecommuting increases the compatibility of urban land-uses and provides the opportunity to develop urban areas with mixed land-use (Wang et al. 2015).
ICT’s impact on urban mobility: ICT affects transportation infrastructures and people's travel behaviour (Snellen and De Hollander 2017). It increases the use of public transport since citizens are aware of the advantage that they can do other things during their commute (Konrad and Wittowsky 2018). The impacts of ICT on travel have been discussed since 1980 (PC Lila and MVLR Anjaneyulu 2016) and four modes of switching, amplifying, modifying, and neutralizing are considered to explain the quantitative impacts of ICT on transportation (Konrad and Wittowsky 2018).
The relationships between ICT, access, and mobility are complex (Vale et al. 2016). Today, with the extensive use of the internet, the concept of access has changed (a new concept has emerged as virtual access) and accessibility cannot be measured by calculating the time, distance, and cost of trip (Yousefi and Dadashpoor 2020). ICT provides a range of trip patterns (Jamal et al. 2017). The results of previous studies show that online shopping does not necessarily reduce shopping-related movements as it complements traditional shopping and traditional shopping is associated with other activities (Yousefi and Dadashpoor 2020). Also, digital marketing introduces new retailers to citizens who are used to searching for information, and frequent shopping (Jamal et al. 2017).
ICT provides new opportunities for travel planning (Konrad and Wittowsky 2018). The use of ICT in the public transportation system will improve the quality of public transportation services and will increase its users (Feikie et al. 2017). Even, telecommuting is not an effective strategy for managing travel demand and may lead to a higher rate of car ownership (de Abreu e Silva and Melo 2018). Promoting ICT along with the development of walking, cycling, and bicycle parking softwares and applications provides more opportunities for transportation and communications, and reduces unnecessary uses of private vehicles (Feikie et al. 2017; Jamal et al. 2017).
It can be inferred from reviewing the literature that walkability indices during these two decades of studies have moved from major urban subjects such as mixed land use, network connectivity, and population density toward minor subjects like the width of sidewalk, and psychological subjects such as experienced safety and attractiveness. Walkability is dynamic and changes under the influence of some factors, including technology. ICT affects different elements of urban life and in this way, it changes the quality of urban design. ICT applications in urban life affect walkability, but how it changes is not clear since we face many uncertainties about the future of walkability in neighborhoods. Considering the difficulty of predicting the future and the existence of numerous uncertainties, applying the futures study, that tries to discover different futures instead of predicting a single future, increases the probability of achieving the preferred future.
[1] Connectivity, Convenience, Comfort, Conviviality, Conspicuousness, Coexistence and Commitment.
[2] The Neighbourhood Environment Walkability Scale (NEWS), a 68-item questionnaire, is the most commonly used method to evaluate walkability. Later, Cerin et al. (2006) developed a simplified version of NEWS (NEWS-A). The NEWS methods consider the following environmental characteristics: (a) residential density; (b) proximity to non-residential land uses; (c) ease of access to non-residential uses (land use mix-access); (d) street connectivity; (e) walking/cycling facilities; (f) aesthetics; (g) pedestrian traffic safety and (h) crime safety. Liao et al. (2020)
The case study is selected based on the availability of previous walkability studies in Iran. For this matter, we explored published papers in Farsi and English regarding walkability and found the paper “Spatial Multi-scale Evaluation of Walkability Potential at Street Segment Level: A Case Study of Tehran” by Taleai and Amiri (2017) a good fit for this research. In their paper, authors measured five walkability indices (land-use mix, accessibility, residential density, greenness index and, line-of-sight) in Tajrish and Velenjak Neighborhoods in District 1 of Tehran Municipality. These two neighborhoods are located in the northern areas of Tehran (Fig. 1) and have a rather hilly topography. They are among the oldest neighborhoods in Tehran. Therefore, they are organically developed and possess walkability characteristics. Taleai and Amiri combined the measuring results with people and experts’ views to define the weight and importance of their selected indices. Their results confirm the availability of walkability characteristics in these areas in addition to revealing two important factors: In citizens’ view, accessibility to public transport has been stated as the important index for walking while in experts’ view, the significant index is the greenness level. Due to the availability of data, possible access to related experts, scientific methodology used for measuring walkability, and the scale of study (neighborhood), the forenamed case study has been chosen for further evaluating the impacts of ICT on walkability of neighborhood in this paper.
This study incorporates qualitative and quantitative approaches at different stages. In the earliest stage, walkability indices are identified through literature. Then based on the experts’ views, indices are refined in accordance to the case study. During this process, some initial indices got merged and some split into two. In some occasions, irrelevant indices are replaced by new ones. The data related to the walkability indices in TVNDOTM (dependent variables) is collected via four questionnaires. The experts have shared their opinions about the importance of the index in the first questionnaire, the future state of the index regarding ICT’s impact in the second, how the mentioned indices interact in the third and the effect of the occurrence/non-occurrence of one key driver on the status of other ones in the fourth questionnaire. Each evaluation is then assigned a numerical value in the questionnaire to enable us to analyze them with SPSS, MicMac and ScenarioWizard softwares. In the end, the results are interpreted in an urban context. Subsequently the methodology of this research benefit from both quantitative and qualitative approaches.
The scope of the paper includes the changes in walkability status within a 10-year period into the future for this article's case study. After reviewing the literature and identifying walkability indices and ICT’s possible impact on them, this study has adapted the indices to its case study (Table 1) by collecting experts’ view, and classified them based on their type. Therefore, our selected indices to measure the impacts of ICT on walkability derive from three major sources:
1. Indices widely used to measure the presence of walkability in neighborhood,
2. Indices which are most affected by ICT and can have an indirect impact on walkability in consequence and,
3. Specific indices relevant to the case study based on experts’ views in the field.
The sample in this study includes 32 respondents (12 people from the subsection of the municipality focused on the selected neighborhoods, 10 people among the managers and staff of District One of Tehran Municipality (DOTM) and, 10 people from the academic sector with prior knowledge and experience about urban walkability) selected with snowball technique.
Table 1. Walkability indices.
|
Category |
Index |
Reference |
Label |
|
Demography |
Households with Low Income |
Liao, et al. 2020 |
Var_01 |
|
Employed Persons |
Liao, et al. 2020 |
Var_02 |
|
|
Migration Background |
Liao, et al. 2020 |
Var_03 |
|
|
Population Density |
Tsiompras and Photis, 2017; Habibian and Hosseinzadeh, 2018; Koohsari et al. 2018; Liao, et al. 2020 |
Var_04 |
|
|
Urban Mobility |
Street Connectivity |
Lawrence D. Frank et al. 2005; Zuniga-Teran et al. 2017; Tsiompras and Photis, 2017; Habibian and Hosseinzadeh, 2018 |
Var_05 |
|
Proximity to Destinations |
Williams et al. 2018 |
Var_06 |
|
|
Access to Public Transportation |
Koohsari et al. 2018; Knapskog et al. 2019 |
Var_07 |
|
|
Mobility Infrastructure |
Taleai and Taheri Amiri, 2017; Williams et al. 2018; Knapskog et al. 2019; Ramakreshnan et al. 2020 |
Var_08 |
Table 1. Walkability indices- continue.
|
Category |
Index |
Reference |
Label |
|
Urban Mobility |
Traffic Safety |
Zuniga-Teran et al. 2017; Ruiz-Padillo et al. 2018; Knapskog et al. 2019; Ramakreshnan et al. 2020 |
Var_09 |
|
Proximity to Bus Stop |
Barros et al. 2017 |
Var_10 |
|
|
Distance to CBD (Network) |
Habibian and Hosseinzadeh, 2018 |
Var_11 |
|
|
Traffic Volumes |
Knapskog et al. 2019 |
Var_12 |
|
|
Speed Levels |
Knapskog et al. 2019 |
Var_13 |
|
|
Parking Lots |
Knapskog et al. 2019 |
Var_14 |
|
|
On-Street Parking |
Fancello et al. 2020 |
Var_15 |
|
|
Built Environment |
Legibility |
Al-ghamdi and Al-Harigi, 2015; Yousefi and Dadashpoor, 2020 |
Var_16 |
|
Width of the Sidewalk |
Barros et al. 2017; Fancello et al. 2020 |
Var_17 |
|
|
Quality of the Pavement |
Ruiz-Padillo et al. 2018 |
Var_18 |
|
|
Street Enclosure |
Experts’ view |
Var_19 |
|
|
Lighting |
D'Orso and Migliore, 2020 |
Var_20 |
|
|
Transparency and Permeability of the Public-Private Space |
D'Orso and Migliore, 2020 |
Var_21 |
|
|
Shelter and Shade |
D'Orso and Migliore, 2020 |
Var_22 |
|
|
Facades |
Knapskog et al. 2019 |
Var_23 |
|
|
Visual Complexity |
Singh, 2016 |
Var_24 |
|
|
Residential Block Size |
Lefebvre-Ropars et al. 2017; Knapskog et al. 2019 |
Var_25 |
Table 1. Walkability indices- continue.
|
Category |
Index |
Reference |
Label |
|
Built Environment |
Non-Residential Block Size |
Experts’ view |
Var_26 |
|
Residential Building Density |
Experts’ view |
Var_27 |
|
|
Natural Environment |
Greenness Levels |
Taleai and Taheri Amiri, 2017 |
Var_28 |
|
Pollution and Noise |
Knapskog et al. 2019 |
Var_29 |
|
|
Social Wellbeing |
Experienced Safety |
Experts’ view |
Var_30 |
|
Public Security |
Ruiz-Padillo et al. 2018 |
Var_31 |
|
|
Locality |
Experts’ view |
Var_32 |
|
|
People Walking or Staying |
Knapskog et al. 2019 |
Var_33 |
|
|
Surroundings and Activities |
Attractiveness |
Ruiz-Padillo et al. 2018 |
Var_34 |
|
Retail Activities |
L. D. Frank et al. 2010; Koohsari et al. 2018 |
Var_35 |
|
|
Entertainment Activities |
Ahani and Pourmohammadi, 2019; Yousefi and Dadashpoor, 2020 |
Var_36 |
|
|
Mobile Retails |
Experts’ view |
Var_37 |
|
|
Urban Furniture |
Knapskog et al. 2019 |
Var_38 |
|
|
Mix of Functions |
Knapskog et al. 2019 |
Var_39 |
|
|
Vibrancy |
Knapskog et al. 2019 |
Var_40 |
|
|
Maintenance |
Knapskog et al. 2019; Fancello et al. 2020 |
Var_41 |
|
|
Way-Finding |
Knapskog et al. 2019 |
Var_42 |
|
|
Places for Walking and Cycling |
Bailey-Catalán et al. 2019 |
Var_43 |
|
|
Pleasantness |
D'Orso and Migliore, 2020 |
Var_44 |
|
|
Frequency of Services and Activities |
Fancello et al. 2020 |
Var_45 |
Table 1. Walkability indices- continue.
|
Category |
Index |
Reference |
Label |
|
Surroundings and Activities |
Aesthetics of Neighborhood Surroundings |
Bailey-Catalán et al. 2019 |
Var_46 |
|
Interest from an Architectural and Urban Viewpoint |
Fancello et al. 2020 |
Var_47 |
|
|
Function and Space |
Urban Structure |
Lefebvre-Ropars et al. 2017; Knapskog et al. 2019 |
Var_48 |
|
Access to Services |
Bailey-Catalán et al. 2019 |
Var_49 |
|
|
Land-Use: Urban Spaces and Parks |
Koohsari et al. 2018; Knapskog et al. 2019 |
Var_50 |
|
|
Land-Use: Commerce |
Barros et al. 2017 |
Var_51 |
|
|
Land-Use: Education |
Barros et al. 2017 |
Var_52 |
|
|
Land-Use: Sport Facilities |
Experts’ view |
Var_53 |
|
|
Land-Use: Cultural Spaces |
Experts’ view |
Var_54 |
Of all these variables, key drivers have three characteristics: they are important, uncertain, and have two-way effects, both affecting each other and affected by one another (Mousavi and Kahaki 2017).
Importance: to rank walkability indices by their importance, a questionnaire was used based on a Likert scale (from 1: very low importance to 5: very high importance), and the average of 32 experts’ answers were calculated. Indices with greater average than the average of the Likert scale (3), are considered as important.
Cronbach’s alphas were calculated in order to prove the reliability of questioners. Reliability is acceptable when this coefficient is greater than 0.5, and it being higher than 0.7 indicates high reliability. In this study, Cronbach’s alpha for Urban Mobility and Built Environment is greater than 0.5, and for Demography, Natural Environment, Social Wellbeing, Surroundings and Activities, and Function and Space is greater than 0.7. The overall Cronbach’s alpha is 0.664[1].
Uncertainty: another questionnaire was used to determine the uncertainties. At this stage, respondents were asked to guess the future status of walkability indices in the case study from several possible statuses, regarding the impact of ICT on them. Then, the selection percentages of every status related to variables were calculated. Different statuses are displayed by the letters A, B, and C. In this study, the indices of which a certain status has received 80% or more of comments, are considered certainties; meaning their future status and the impact of ICT on them, are considered definite and certain. The rest, are known as uncertainties.
Two-way effects: in the next step, to determine the amount of influence and dependence of indices, the Cross-Impact Matrix (CIM) technique has been used by MicMac[2] software. Experts identified the relationships between walkability indices in the TVNDOTM. In the Cross-Impact Matrix (CIM), the relationship between the two variables is shown quantitatively (0: No Influence, 1: Weak Influence, 2: Moderate Influence, 3: Strong Influence, P: Potential Influences).
Two structural analyses are performed by MicMac software: the first structure is the Matrix of Direct Influences (MDI), which considers only the current relationships between the variables, and the second one is the Matrix of Potential Direct Influences (MPDI), which includes both of current and potential relationships between the variables, and is filled by assigning corresponding values to the defined values in the MDI. The MPDI is more appropriate than the MDI in predicting the future. Both MDI and MPDI are the inputs of MicMac, and the two Matrices of Indirect Influences (MII) and Potential Indirect Influences (MPII) are the results of them. The MPII corresponds to the MPDI and is resulted by successive iterations of it. MicMac suggests the appropriate number of iterations (Mousavi and Kahaki 2017). In this study, the MPDI with four-time iterations of data, has 100% reliability (Table 2).
Table 2. Reliability of indices in the Matrix of Potential Direct Influences (MPDI) (Reference: MicMac).
|
4 |
3 |
2 |
1 |
Iteration |
|
100% |
100% |
100% |
98% |
Influence |
|
100% |
100% |
100% |
98% |
Dependence |
The potential direct and indirect influence/dependence maps (Fig2 and Fig3) show that among all the indices, traffic volumes (var_12), locality (var_32), attractiveness (var_34), places for walking and cycling (var_43), access to services (var_49), and land-use: urban spaces and parks (var_50) have two-way effects. It means they are both affecting and affected.
The key drivers are identified by controlling all three characteristics of importance, uncertainty, and two-way effects (Table 3). Considering the key drivers and possible status related to each of them (Table 4), the number of possible scenarios is 108, according to the product rule (2 × 3 × 3 × 3 × 2=108).
Table 3. Importance and uncertainty of walkability indices in TVNDOTM.
|
Label |
Index |
Important |
Uncertain |
Two-way effect |
Key driver |
|
Var_01 |
Households with Low Income |
* |
|
|
|
|
Var_02 |
Employed Persons |
* |
* |
|
|
|
Var_03 |
Migration Background |
|
|
|
|
|
Var_04 |
Population Density |
|
* |
|
|
|
Var_05 |
Street Connectivity |
* |
|
|
|
|
Var_06 |
Proximity to Destinations |
* |
* |
|
|
|
Var_07 |
Access to Public Transportation |
* |
* |
|
|
|
Var_08 |
Mobility Infrastructure |
* |
* |
|
|
|
Var_09 |
Traffic Safety |
* |
|
|
|
|
Var_10 |
Proximity to Bus Stop |
* |
|
|
|
|
Var_11 |
Distance to CBD (Network) |
* |
* |
|
|
|
Var_12 |
Traffic Volumes |
* |
* |
* |
* |
|
Var_13 |
Speed Levels |
* |
|
|
|
|
Var_14 |
Parking Lots |
* |
* |
|
|
|
Var_15 |
On-Street Parking |
|
|
|
|
|
Var_16 |
Legibility |
* |
* |
|
|
|
Var_17 |
Width of the Sidewalk |
* |
|
|
|
|
Var_18 |
Quality of the Pavement |
|
|
|
|
|
Var_19 |
Street Enclosure |
|
* |
|
|
|
Var_20 |
Lighting |
* |
* |
|
|
|
Var_21 |
Transparency and Permeability of the Public-Private Space |
* |
* |
|
|
Table 3. Importance and uncertainty of walkability indices in TVNDOTM- continue.
|
Label |
Index |
Important |
Uncertain |
Two-way effect |
Key driver |
|
Var_22 |
Shelter and Shade |
* |
* |
|
|
|
Var_23 |
Facades |
|
* |
|
|
|
Var_24 |
Visual Complexity |
|
* |
|
|
|
Var_25 |
Residential Block Size |
|
* |
|
|
|
Var_26 |
Non-Residential Block Size |
|
* |
|
|
|
Var_27 |
Residential Building Density |
|
* |
|
|
|
Var_28 |
Greenness Levels |
* |
* |
|
|
|
Var_29 |
Pollution and Noise |
|
* |
|
|
|
Var_30 |
Experienced Safety |
* |
* |
|
|
|
Var_31 |
Public Security |
* |
* |
|
|
|
Var_32 |
Locality |
* |
* |
* |
* |
|
Var_33 |
People Walking or Staying |
* |
* |
|
|
|
Var_34 |
Attractiveness |
* |
* |
* |
* |
|
Var_35 |
Retail Activities |
* |
* |
|
|
|
Var_36 |
Entertainment Activities |
* |
* |
|
|
|
Var_37 |
Mobile Retails |
* |
* |
|
|
|
Var_38 |
Urban Furniture |
* |
* |
|
|
|
Var_39 |
Mix of Functions |
* |
* |
|
|
|
Var_40 |
Vibrancy |
* |
* |
|
|
|
Var_41 |
Maintenance |
|
* |
|
|
|
Var_42 |
Way-Finding |
|
* |
|
|
|
Var_43 |
Places for Walking and Cycling |
* |
|
* |
|
|
Var_44 |
Pleasantness |
* |
* |
|
|
|
Var_45 |
Frequency of Services and Activities |
* |
* |
|
|
Table 3. Importance and uncertainty of walkability indices in TVNDOTM- continue.
|
Label |
Index |
Important |
Uncertain |
Two-way effect |
Key driver |
|
Var_46 |
Aesthetics of Neighborhood Surroundings |
* |
* |
|
|
|
Var_47 |
Interest from an Architectural and Urban Viewpoint |
* |
* |
|
|
|
Var_48 |
Urban Structure |
|
* |
|
|
|
Var_49 |
Access to Services |
* |
* |
* |
* |
|
Var_50 |
Land-Use: Urban Spaces and Parks |
* |
* |
* |
* |
|
Var_51 |
Land-Use: Commerce |
* |
* |
|
|
|
Var_52 |
Land-Use: Education |
|
|
|
|
|
Var_53 |
Land-Use: Sport Facilities |
|
* |
|
|
|
Var_54 |
Land-Use: Cultural Spaces |
|
* |
|
|
Table 4. Key drivers and their possible status in scenarios of ICT’s impacts on walkability in TVNDOTM.
The different status of the variables are coded by assigning various colors to them (Green: Favorable status, Yellow: Continuation of the current status, Red: Critical status). Then, to identify consistent scenarios, we used the Cross-Impact Balances (CIB) analysis technique ran by ScenarioWizard software. At this stage, the experts expressed the impact of the occurrence of a possible status of a key driver on occurrence or non-occurrence of possible situations of other key drivers, based on a numerical range from -3 to 3 (-3: Strong limiting effect, -2: Moderate limiting effect, -1: Weak limiting effect, 0: Affectless, 1: Weak empowering effect, 2: Moderate empowering effect, 3: Strong empowering effect).
ScenarioWizard shows inconsistencies by calculating the impacts balances of each scenario, and identifies scenarios with a strong degree of internal consistency (Table 5). The first scenario is the continuation of the current status, which has the highest internal consistency. The second and third ones are critical and preferred, respectively. That is, the impact of ICT on walkability in TVNDOTM is destructive according to scenario NO 2, and is optimal according to scenario NO 3.
Table 5. Scenarios tableau of ICT’s impacts on walkability in TVNDOTM (Reference: ScenarioWizard).
[1] See the exact Cronbach’s Alpha coefficients in the appendix A.
[2] Matrix impact cross- Multiplication applied to classification.
This study aims to discover probable futures for walkability in TVNDOTM regarding the effects of ICT on different aspects of urban life benefiting Futures Study methodology. Key drivers form the components of scenarios and meet the three simultaneous conditions of importance, uncertainty, and two-dimensional effects. Results of the analysis show three probable scenarios (Normal, Critical, and Favorable) with high consistency. In the normal scenario which has the highest amount of consistency among its components, current walkability status continues to exist in the neighborhoods. According to that, the cumulative effects of ICT on each walkability index are neutral and there is no significant correlation between the increase in using ICT and a change in walkability in urban neighborhoods. In the second scenario (critical), traffic volume stays constant while locality and attractiveness decrease. At the same time, due to the availability of virtual access to most of the residents’ needs, the importance of physical accessibility declines. Moreover, ICT-based facilities including social media and electronic entertainment cause residents to spend their leisure time inside their houses, and due to reduced usage of parks and public spaces, their land area decreases over time. Based on the third scenario (favorable) traffic volume will decrease because of ICT facilitating long-distance communication, reduced need for physical travel and personal cars. Lower traffic volumes with technology-based design in urban neighborhoods lead to more locality and attractiveness. In such conditions, a greater variety of services is offered at neighborhood scale and it is easier to access them due to the priority of walking over automobiles. In this scenario, residents continue to use parks and open spaces as much as before.
The results of this study are in line with former studies, which investigated ICT’s effects on different aspects of urban life. In the literature review, authors emphasized how vague the effects of ICT on urban affairs (changes in land-use, intra-city travel volume, and so on) are. Likewise, this study resulted in three scenarios, which differ significantly in terms of walkability indices; this emphasizes the uncertainty in the outcome of the effects of ICT on walkability in the proposed area. Therefore, based on former studies, these remarkable differences in scenarios are acceptable.
There is a delicate connection between the results and the existing literature. As mentioned earlier, walkability is one of the main principles of urban design and urban interventions should not jeopardize it. Since walkable neighborhoods comprise an important part of urban planning, there needs to be prior conscious planning and actions to ensure walkability in the future urban developments. The contribution is in suggesting that being indifferent towards the enhancements of ICT and inactive towards its consequences in urban life cannot be redeemed with a reaction in the future. Consequently, it is essential to do the proper actions at the proper time (before it is late) to create a favorable future and scenario.
This study is among the pioneer papers to clearly investigate the effects of ICT development and its application on walkability in urban neighborhoods featuring Futures Study methodology. In other words, this research is novel in both subject and methodology and authors have tried to unveil the knowledge gap in this regard.
Having the diversity of urban neighborhoods in mind, the results of the study are not generalizable, and, extension of results to the similar neighborhoods needs to be done cautiously. Anyhow, the authors emphasize the possible effects of ICT on walkability in urban neighborhoods as a novel subject and application of the methodology of this study which can be replicated in other case studies to reach a proper conclusion. The completion of questionnaires taking a long time has not been effect-less in the accuracy of the answers. The sample size in this study is 32 people. Authors suggest a larger sample size for better accuracy in future research. Little investigations and lack of novelty in the existing body of research about the effects of ICT on walkability in urban neighborhoods, resulted in the shortage of references for comparison. More research into this subject provides a better stage to compare the effects of ICT on walkability in different cities and this study is one of the pioneer studies in this regard.
The methodology used in this study can be employed for similar case studies where ICT development is not mature and may still cause dramatic changes in urban life. It is clear that the number of required indices differs depending on the context. Prioritizing indices based on their significance can be implemented through other methods (like Multiple Criteria Decision Making). Futures study can be implemented through various methods but considering the qualitative nature of most of the walkability indices, we suggest using qualitative methods for future research. Authors also suggest the Cross-Impact Matrix (CIM) technique (to consider relations among the variables) and Cross-Impact Balances Analysis (CIBA) technique (to identify scenarios with consistent components).
ICT can influence the walkability from two contrasting perspectives: First, ICT reduces walkability by changing the way citizens’ demands are met. In the second notion, ICT is utilized as a tool to enhance walkability. Considering the future advancements of ICT in Iran and its effects becoming more tangible in the future urban life, we tried to analyze the effects of ICT on the walkability in Tajrish and Velenjak Neighborhoods in District One of Tehran Municipality (TVNDOTM) using Futures Study. Research outcomes show the major influences of ICT on walkability in proposed neighborhoods are uncertain. Based on the analysis, key drivers to conform the different scenarios of possible effects of ICT on walkability in TVNDOTM that have all three conditions of uncertainty, importance and being both affecting and affected are: traffic volumes, locality, attractiveness, access to services and urban spaces and parks. Therefore, the answer to our first research question is now clear. Considering possible situations for each key driver based on the Product Rule, 108 initial scenarios were identified. After investigating the internal consistency of different scenarios, three scenarios with high consistency were revealed. The highest internal consistency belongs to the first scenario (which predicts the continuation of the current situation). The second scenario is critical and the third, favorable. These scenarios are the answer to our second research question.
Conducting more research on a broad range of urban cases will reveal if there is any relationship between the final scenarios and physical, economic, and socio-cultural characteristics of the neighborhood. Furthermore, this study focused on identifying different futures and probable scenarios of ICT affecting urban walkability at the neighborhood scale. Authors suggest clarifying the process of conscious intervention for changing the critical futures and encouraging the favorable one, and defining robust strategies through participation as recommendations for future research.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The authors declare that the contents of this article have not been published previously. All the authors have contributed to the work described, read and approved the contents for publication in this journal.
Conflicts of Interest
The authors declare no conflict of interest.
Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
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No competing interests reported.