Discussion Paper Nr

discussion paper Nr. 41/2018 Juni/2018

discussion paper

Houshmand E. Masoumi1, Amr Ah. Gouda1,7, Lucia Layritz1, Pia Stendera1, Cynthia Matta2, Haya Tabbakh3, Sima Razavi4, Houshiar Masoumi4, Betül Mannasoğlu5, Özlem Kılınç5, Ashraf

M. Sharara6, Mahmoud Elnably7, Ahmad Alhakeem1, Sherzad

Ismail1, Erik Fruth1

Urban Travel Behavior in Large Cities of MENA Region: Survey Results of Cairo, Istanbul, and Tehran

1 Technische Universität Berlin, Zentrum Technik und Gesellschaft, Germany 2 Notre Dame University, Louaize, Lebanon 3 American University of Beirut, Lebanon 4 Independent surveyor, Tehran, Iran 5 Istanbul Technical University, Turkey 6 Independent Surveyor, Cairo, Egypt 7 Ain Shams University, Cairo, Egypt

Impressum

Zentrum Technik und Gesellschaft Sekretariat HBS 1 Hardenbergstraße 16.18 10623 Berlin www.ztg.tu-berlin.de

Die Discussion Paper werden von Martina Schäfer, Leon Hempel und Dorothee Keppler herausgegeben. Sie sind als pdf-Datei abrufbar unter: http://www.tu-berlin.de/ztg/menue/publikationen/discussion_papers/

Abstract The present discussion paper summarizes an urban mobility survey as a part of the project “Urban Travel Behavior in Large Cities of MENA Region” (UTB-MENA) funded by the German Research Foundation (DFG) undertaken in summer and autumn 2017 in Tehran, Istanbul, and Cairo. This data collection was conducted in 18 neighborhoods located in different urban forms related to three different eras: traditional urban form, in-between (transitional) urban form, and new developments. The survey instrument included 31 questions organized in six different sections. As a result of face- to-face interviews with residents as well as quantification of several land use indicators, a database of 8284 validated subjects (Cairo: 2786 , Istanbul: 2781 , Tehran: 2717) was created by the research team based in Berlin, Tehran, Istanbul, and Cairo. The results of 46 variables (24 continuous and 22 categorical) are presented in this paper. The neighborhood-level precision of the findings is 4.5% to 4.7% for individual variables and 1.8% to 2.4% for household variables. These findings make the data representative for the case-study neighborhoods. These data are expected to provide a reliable sample for researchers of the region for the purpose of strengthening human-oriented urban transportation planning and research against the mainstream of transportation engineering by bringing in disaggregate individual urban mobility data into urban transport research of the Middle East and North Africa (MENA) region. Zusammenfassung Das vorliegende Discussion Paper fasst eine Mobilitätsbefragung zusammen, die Teil des durch die Deutsche Forschungsgemeinschaft geförderten Projekts „Städtisches Mobilitätsverhalten in Großstädten der MENA-Region“ (UTB-MENA) ist. Die Befragung wurde im Sommer und Herbst 2017 in 18 Nachbarschaften in Teheran, Istanbul und Kairo durchgeführt, die drei Zeitphasen urbaner Entwicklung repräsentieren: traditionelle urbane Form, Übergangsform, und neue Entwicklung. Die Befragung umfasst 31 Fragen in sechs unterschiedlichen Abschnitten. Ein Datensatz von 8284 validierten Zielpersonen (Kairo: 2786, Istanbul: 2781, Teheran: 2717) wurde als Ergebnis der persönlichen Interviews mit Bewohnern und der Quantifizierung von mehreren Landnutzungsindikatoren von den Forschungsteams in den drei Städten erzeugt. Die Resultate von 46 Variablen (24 kontinuierliche und 22 kategoriale) werden in diesem Paper vorgestellt. Die Genauigkeit der Ergebnisse auf Nachbarschaftsebene liegt zwischen 4,5 % und 4,7 % für einzelpersonenbezogene Variablen und zwischen 1,8 % und 2,4 % für haushaltsbezogenen Variablen. Diese spezifischen Forschungsergebnisse zeigen, dass die Daten für die in der vorliegenden Fallstudie berücksichtigten Nachbarschaften repräsentativ sind. Die Daten sollen Forschern für die Region eine zuverlässige Stichprobe liefern, um eine Menschen-orientierte Verkehrsplanung zu stärken, innovative Forschung gegen den etablierten technikorientierten Trend aufzubauen, und disaggregierte Daten zum Thema städtische Mobilität im Mittleren Osten und Nordafrika (MENA) zu etablieren.

Content

1. Introduction ...... 5

2. Methodology ...... 6 2.1. Neighborhood-Scale Urban Form Classification ...... 6 2.2. Case-Study Areas ...... 7 2.3. Pilot Survey ...... 14 2.4. Survey Instrument, Variables, & Representativeness ...... 14 2.5. Sample Characteristics and Validation ...... 19

3. Findings ...... 20 3.1. Overall Sample ...... 20 3.2. City-Level Samples ...... 23

4. Conclusion ...... 25

5. Appendixes ...... 27 5.1. Appendix 1: Survey instrument ...... 27 5.2. Appendix 2: Categorical data ...... 30 5.3. Appendix 3: City-level continuous data...... 32 5.4. Appendix 4: City-level categorical data ...... 34 5.5. Appendix 5: City-level comparative continuous data ...... 37 5.6. Appendix 6: City-level comparative categorical data ...... 38

Literature ...... 40

1. Introduction International literature concentrating on the interrelations between land use and urban travel behavior has grown rapidly during the past two decades, but the share of research on the Middle East and North Africa (MENA) region is very limited in number and depth. The region is far behind in in-depth studies on the topic. Iran is an example of one MENA country that is starting to focus on such subjects. During the past two or three years, the related studies in the context of Iran have also been launched by MENA researchers. The city-scale and regional characteristics and their linkage with travel are especially studied in Iran. Consistent with the Western literature (e.g. Handy et al. 2005), the Iranian studies show stronger effects of socio-economic factors than that of urban form (Arabani, Amani, 2007; Soltani, Esmaeili-Ivaki, 2011). Some Egyptian research results show the effectiveness of land use along with three other factors in defining travel speed reliability in a 35-Kilometer roadway connecting two regions of Cairo (Sabry, Talaat, 2015). The effects of the urban environment and the condition of sidewalks in providing safety for school students when walking to and from school has been studied in Amman, Jordan (Shbeeb, Awad, 2013). The topic of walkability provided by the street network for students of 12 to 14 years of age has also been studied in Istanbul. Özbil et al. (2014) conclude that characteristics like street length suitability for walking, number of pedestrian crossings and traffic signals, and sidewalk width significantly influence on route choice of students. Özbil (2013) examined the street connectivity and layout in three neighborhoods in Istanbul (Nişantaşı, Erenköy, and Moda), each with different land use and street layout characteristics, and found significant correlations with pedestrian flow. She suggests integrating more socio-demographic variables like population and employment densities to derive more extended results. There is also evidence that urban sprawl influences mode choice in Riyadh, Saudi Arabia (Alqhatani et al. 2013). The results of a recent work on the diversity of land uses show that this description is associated with the frequency of the intra- and extra-zonal trips (Soltani et al. 2012). Apart from the number of the studies, another weakness in the Iranian studies when compared with their Western counterparts is the absence of modeling and simulation. Limited efforts have been made for conducting multivariate mathematical or computer- based modeling of the effective factors of urban travels. Such a shortcoming can be more or less seen in other countries of the region. That is why this project has picked modeling as the main objective. To do that, the fundamental emphasis has been put on micro-scale, namely neighborhoods. Micro-scale effects on travel are interpreted inconsistently: according to Rodriguez and Joo (2004), the neighborhood attributes and design have little impacts on walking behavior, while Cervero (2002) notes that these qualities are important in changing the modal split and increasing the share

5 of walking. Nevertheless, Cervero (1993) had already concluded that macro-factors like travel costs and density are more important than micro- factors like design. In the context of MENA, the neighborhood has traditionally had a very important role in the social life of urban dwellers. Thus, to connect the debate to the regional context of MENA, the neighborhood can be given a focal position in land use/urban travel research. A very large research limitation is the lack of reliable data that accounts for inclusive types of factors like mobility behaviors, infrastructures and accessibilities, human perceptions, socio-economics and demographics, etc. as variables. This problem is seen in all the countries of the region – even in larger ones where more reliable and inclusive data is expected. Another weakness of the existing mobility surveys of the region is that they provide limited disaggregate data. A large part of the existing data is in aggregate level. This constraint is even more limiting when researchers intend to consider variables regarding active mobility, human perceptions of transportation and neighborhood, and the built environment. Except for studies in Iran, Turkey, and Egypt, very few related efforts in the region seem to provide a higher quality of data. To address the aforementioned problems, the current survey in Tehran, Istanbul, and Cairo was conducted under the framework of the project “Urban Travel Behavior in Large Cities of MENA Region” (UTB-MENA) funded by the German Research Foundation (DFG) and provides the necessary data in the large cities of the region. Comprehensiveness and inclusiveness of the variables was a goal in designing the survey instrument. Similar efforts have rarely been done in the context of the MENA region.

2. Methodology The fundamentals of transport surveys were used in this data collection. The interplay of land use with mobility decisions and passenger behaviors were highlighted in the methodology of case-study area selection based on three different chronologically defined urban form types. The data was collected using a standardized questionnaire and face-to-face interviews with people living in the selected areas. Neighborhood-level representative samples in the three cities of this survey provided a dataset that can be useful for studies on the large cities of the region or on each city separately. The above is discussed in more detail in following five sub-sections.

2.1. Neighborhood-Scale Urban Form Classification This survey was theoretically based on observations done on the micro- scale land use of large cities in the MENA region. These observations were first undertaken in the Iranian cities, the results of which were published online in a discussion paper in 2015 (Masoumi, 2015). According to this

6 paper, classification of the land use of the existing neighborhoods in the region can lead to three main urban form types. Type 1 is defined as traditional urban form, which is more or less reflected by historical cores of cities and old rural areas taken by urban growth. Many of these neighborhoods have a discernible neighborhood center (particularly in Cairo and Tehran) and are fairly compact. The buildings of such neighborhoods are attached to one another and no leapfrog developments are seen within the texture of this typical neighborhood. The second type of urban form (Type 2) consists of semi-grid street networks with lower population and/or construction compactness compared to Type 1. The third urban form (Type 3) includes centerless neighborhoods, the majority of which with (nearly) full-grid street networks. This type is usually found on the periphery of the cities of the region, and they were developed and inhabited usually after around 1980. Sprawled and car-oriented areas are part of this land use type. A more rigorous investigation of different neighborhood types in Cairo, Istanbul, and Tehran was undertaken together with Turkish and Egyptian colleagues from Istanbul Technical University and Ain Shams University, Cairo, in summer through winter of 2017 (Masoumi et al. unpublished paper). The findings showed that the eras could be defined slightly different for the three cities:  Cairo: Type 1: before 1869, Type 2: 1869-1975, Type 3: after 1975.  Istanbul: Type 1: before 1950, Type 2: 1950-1980, Type 3: after 1980;  Tehran: Type 1: before 1930, Type 2: 1930-1980, Type 3: after 1980;

These time classifications were mainly driven by socio-economic, political, or planning-related phenomena and transformations that led to different patterns of urban form. In the aforementioned study, a quantitative analysis of land use was undertaken using four variables: residential population density, neighborhood centrality, location and formation of neighborhood facilities, and form of street networks. The findings showed that, although there are some differences between the cities, the three cities follow general patterns and considerable similarities can be distinguished in the transformation of the three cities using the above eras.

2.2. Case-Study Areas Eighteen neighborhoods in three different urban forms in Tehran, Istanbul, and Cairo were selected. Table 1 illustrates the selected neighborhoods, their urban form type, population, and land use characteristics. Two types of population densities are presented in this table: gross population density estimated by the administrative neighborhood (or district) boundaries, and net population density calculated by net built up (mostly residential) areas estimated by Google Maps. Net population densities can be more relevant because there are large pieces of undeveloped land inside administrative neighborhood boundaries. Hence, these figures have been included in the

table. The location of the neighborhoods and some basic information about them are depicted in Fig. 1. In this table, the neighborhood types (1, 2, or 3) are assigned according to the three respective eras for each city, but each type refers to a (mostly) shared range of characteristics including urban form specifications explained in the methodology section.

Table 1: The general characteristics of the 18 case-study neighborhoods.

***

No.

City

(ha)

Type

Code

Gross Gross

Era

Name

Density Density Density

(Inh./ha)

Population Population

Gross Area Area Gross

(Inhb./ha)

Centrality

Facilities

Net Area (ha) Net Area

Network Type Network

Neighborhood Neighborhood Neighborhood

Net Population Net Population

13 C1 Darb El-Ahmar 1 Bfr. 1869 O r g a ni c 73210 182 134 402,25 546,3 C C/P

14 C2 Bab El-Shariah 1 Bfr. 1869 O r g a ni c 68371 103 103 663,8 663,8 C C/P

15 C3 El Zayton El Sharkia 2 1869-1975 Semi-grid 46194 88 88 524,9 524,9 C C/P

Cairo 16 C4 Masaken Al-Amiriah Al-Shamaliah 2 1869-1975 Semi-grid 191737 52 52 3687 3687 C C/P

17 C5 El Hadiqah El-Dowliah 3 Aft. 1975 Full-grid 21542 109 109 197,6 197,6 C CS

18 C6 Al-Matar 3 Aft. 1975 Full-grid 66828 645 390 103,6 171,3 C CS

7 I1 Karagümrük- Hırka-i Şerif 1 Bfr. 1950 O r g a ni c 35589 79,1 79,1 449,84 449,84 C CS

8 I2 Balat-Ayvansaray 1 Bfr. 1950 O r g a ni c 33296 133,3 133,3 324,21 324,21 C CS

9 I3 Yıldıztabya 2 1950-1980 Semi-grid 25953 67 67 387,36 387,36 C CS

10 I4 Koşuyolu-Acıbadem-Hasanpaşa 2 1950-1980 Semi-grid 54220 296,6 296,6 182,81 182,81 C C/P

Istanbul 11 I5 Adnan Kahveci 3 Aft. 1980 Semi-grid 73691 458,7 458,7 160,65 160,65 C C/P

12 I6 Başakşehir-Başak 3 Aft. 1980 Semi-grid 119129 2014,2 634 59,14 187,9 C C/P

1 T1 Sangalaj 1 Bfr. 1930 O r g a n i c 29359 107 107 274,4 274,4 C CS

2 T2 Arg-Pamenar 1 Bfr. 1930 O r g a n i c 5869 120 120 48,9 48,9 C C/P

3 T3 Towhid 2 1930-1980 Semi-grid 36106 104 104 347,2 347,2 C CS

4 T4 Sadeghieh 2 1930-1980 Full-grid 40872 210 210 194,6 194,6 C CS

Tehran 5 T5 Shahran-Jonoubi 3 Aft. 1970 Full-grid 21826 90 90 242,5 242,5 C CS

6 T6 Golestan-Sharghi 3 Aft. 1970 Full-grid 17005 160 160 106,3 106,3 Cl DF

*Eras are divided into three groups for each city: Tehran: before 1930, 1930-1980, and after 1980; Istanbul: before 1950, 1950-1980, and after 1980; and Cairo: before 1869, 1869-1975, and after 1975. **Centrality types are divided into Centered (C) and Centerless (Cl). ***Facility locations are divided into concentric or polycentric (centered) with the acronym C/P, commercial strip (centered) with the acronym CS, and dispersed facilities (centerless) with the acronym DF.

The six neighborhoods of Tehran are located on a line starting from the historical core towards the west and northwest of the city. The selected neighborhoods reflect different urban form patterns and socioeconomic characteristics. The residents of Type 1 neighborhoods are mostly from

8 lower-income families, while those living in Type 2 and 3 neighborhoods are considered to be among medium-income households. Selection of higher- income regions located in the north of the city, such as the municipal Region 1 of the city, was avoided in order to have a good representativeness regarding socioeconomic factors. The case study areas of Istanbul were selected on both the European and the Asian side, although they are mostly located on the European side for the purpose of easier logistics of the project surveyors. The selected neighborhoods represent very different urban forms emerged or developed in different eras under various circumstances. The metropolitan area of Cairo (Greater Cairo) consists of Cairo Governorate, New Cairo City, Giza, Shubra El-Kheima, 6th of October City and Obour City. The six case-study neighborhoods of Cairo were selected only from Cairo Governorate. However, in general, even selecting areas from other governorates could have been in line with the methodology of the project, which is related to the Middle Eastern large metropolitan areas and megacities.

Tehran

Fig.1a: Location and basic information of the 6 selected neighborhoods in Cairo

Istanbul

Fig.1b: Location and basic information of the 6 selected neighborhoods in Istanbul.

Cairo

Fig.1c: Location and basic information of the 6 selected neighborhoods in Cairo.

2.2.1. Cairo C1: El-Darb El-Ahmar is a complete district (Qesm), which is one of the oldest areas of Cairo. Built more than 700 years ago, it contains many of archaeological buildings and mosques. The location of the neighborhood is near to the CBD of Cairo. The main streets are Port Saeed St. and Al Azhar St., the main transport modes that inhabitants use are bus, minibus & microbus. Al Azhar Park, located on the east side of El-darb El-Ahmar neighborhood, is one of the most modern gardens of Cairo. C2: Bab El Shariah District is also located in the historical core of Cairo. It was built from more than 700 years ago. The buildings are very old and the streets are narrow with a historical view. There are also two main streets: Port Saeed Street and El Geish Street. The location of this neighborhood is near to the Central Business District of Cairo. Bab El Sharia covers 103 ha and hosts 68.371 inhabitants. The main transport modes of the neighborhood are bus, minibus, and microbus. Because the neighborhoods (shiakhat) in the historical core of Cairo are very small, the two areas selected as the Type-1 areas of Cairo cover complete districts comprising several neighborhoods. C3: El-Zaytoon El-Sharkia is a neighborhood of El-Zayton district. It is located in the north of Cairo and was built starting in 1960. It contains also Al Tahrah Palace, one of the presidential palaces in Cairo. The main street is Gesr Elsewes. A metro line passes in the west at Al Zayton metro station. The main public transport modes are bus, minibus, and metro. C4: Masaken Al-Amiriah Al-Shamaliah is one of the neighborhoods of northern Cairo that belonged to Zeitoun District until they were separated in 2014. With an area of 52 ha and a population of 191737, the neighborhood provides a dense urban form for the residents. The streets of this area form a semi-complete network covering the whole area inside the neighborhood boundary. The western boundary of the neighborhood is approximately 1.6 kilometers away from the Hadeyeq metro station. C5: El-Hadiqah El-Dowliah is a neighborhood of Nasr City. It was built in the early 1980s. The famous El-Hadiqah El-Dowliah garden is located in this area and the streets are wide and full of trees. The main street, Abbas El- Akkad, passes through the district. Around this street, the most used public transport modes are bus, minibus, and microbus. Located on the east side of Cairo, it is near the commercial area of Nasr City. C6: Al-Matar neighborhood is located near the main airport of the city, which is the second busiest airport in Africa. The neighborhood is located near Heliopolis. The airport is about 15 km away from the central business district and 22 km from the city center. With a gross area of 645 ha, the neighborhood accommodates 66928 inhabitants. The street grid is predominantly a complete network. There are vast undeveloped lands on the east of this neighborhood that makes the gross and net densities largely different.

2.2.2. Istanbul I1: Hırka-i Şerif and Karagümrük are also two neighborhoods combined into one study area. They are located in Fatih District on the European Side, which is one of the very central places in Istanbul. The Street pattern in this area is quite organic. This area is limited by Adnan Menderes Boulevard from the south and Fevzipaşa Street from the north, which are also used for accessing public transport. Around 28 bus lines can be accessed in Adnan Menderes Boulevard and about 20 in Fevzipaşa Street. Accessibility by metro is provided by Yenikapı-Atatürk Airport line with two stops (Emniyet & Topkapı) in the southern part, which is also covered by metrobus line and other metro lines. The total area of these neighborhoods is roughly 75 ha. According to the Turkish Statistical Institute, the total population of these areas is 35582 (2015). I2: Balat and Ayvansaray are two different neighborhoods that are combined in this survey. Balat is a focal point between Ayvansaray and Fener on the shore of the Golden Horn in the Fatih district of Istanbul. There are direct buses to the Balat neighborhood but there is no metro or metrobus line. Ayvansaray is one of the neighborhoods of Fatih District. On the historical peninsula, the neighborhood is located on the southern shores of the Golden Horn. It lies to the east of Balat, west of Eyüp, Defterdar neighborhood, north of the Golden Horn and Edirnekapi. Ayvansaray neighborhood has a public bus line and a metrobus stop providing direct access, but there is no metro line. I3: Yıldıztabya is a neighborhood connected to the Gaziosmanpaşa district of Istanbul. The only transportation systems of the neighborhood are bus and minibus, so other modes such as metro or metrobus are lacking. I4: Acıbadem neighborhood is located in Kadıköy District on the Anatolian side. This neighborhood is divided by the D100 highway and Acıbadem Street into two parts. There is a metrobus line that connects to Beylikdüzü on the Anatolian side via D100. According to İETT, Acıbadem Street has around six bus lines and while D100 has 12. Also, there is a Marmaray stop in this area, which is connected with various metro lines. According to 1946 and 1966 satellite images, there was almost nothing in this area in 1946 and only a roughly 5.5-ha area was built up in 1966 to the south of D100. Today, there are housing complexes in some regions of this neighborhood. The area is roughly 47 ha. According to the Turkish Statistical Institute, the total population of Acıbadem is 30.406 in 2015. I6: Adnan Kahveci is a Type 3 neighborhood located on the periphery of the city to the west of the European side. The E5 highway that passes from the north of the neighborhood connects it to the city center. The neighborhood has a semi-grid network of streets and centered facilities at the edge of streets. There is a notable availability of bus stations on the north, center, and south of the neighborhood while fewer stations are found on the eastern side.

I6: Başakşehir is a district of Istanbul. In 2008, districts were separated from Küçükçekmece, Esenler, and Büyükçekmece districts. Basaksehir does not have a coast to the sea with Arnavutköy and Eyüp to the north, Sultangazi and Esenler to the east, Bağcılar to the south, Küçükçekmece and Avcılar to the south and Esenyurt to the southwest. The transportation infrastructure of the neighborhood includes the TEM motorway and subway. There are also bus services as public transport. The M3 Başakşehir-Kirazlı- Bakırköy metro line is the main metro line passing by and has stops that transfer to the metrobus.

2.2.3. Tehran T1: Sangalaj is among Tehran’s oldest neighborhoods and was the scene of several historical political events in the nineteenth century. During and after World War II, the neighborhood could not continue its successful presence in the history of Tehran and lost its importance as a hub of social affairs and political movements. The City Park (Park-e Shahr), located on the northern side of the city, was built after the world war to combat deterioration. Today, more than one-third of the area of this neighborhood is considered deteriorated texture. The neighborhood has a compact and organic urban form with many cul-de-sacs and curvy and broken narrow streets. T2: Arg-Pamenar is attached to Sangalaj from the northeastern side and to the Grand Bazaar and neighborhood of Bazaar from the northern side. The urban land use is quite like Sangalaj. This area has had a unique social, cultural, and political importance in the nineteenth-century Tehran during the reign of Qajar Dynasty. Today the neighborhood has a population of 5869 and an area of 120ha. A high percentage of the population consists of youth. T3: Towhid was first formed in the 1960s as a response to the rural-urban migration. Large parts of what is called Towhid neighborhood today were formerly gardens. The street network is a semi-orthogonal grid with many dead-end allies. T4: Sadeghieh was formed in the 1950s and 1960s after Tehran’s population boom caused by rural-urban migration and fast urbanization rates. Sadeghieh Square located in the west of the neighborhood acts as a city-level node connecting many destinations in western Tehran. T5: Shahran-Jonoubi was a good leisure place just outside the city before 1980 that attracted the residents of Tehran for entertainment because of its good weather at the hilly areas of Alborz Mountains. After the 1979 revolution, new housing projects started in the form of villa construction, which in the past 2-3 decades took shape as 4-5-story building constructions for middle-class inhabitants. The neighborhood has a nearly complete street network, with centered neighborhood facilities on the edge of main streets. The public transport systems are mainly taxis and buses; metro stations are not accessible by walking.

T6: Golestan-Sharghi was recently developed during the past two decades based on a completely orthogonal street network. New residential buildings are four or five-story buildings near to two large urban highways. The majority of residents living in their personal apartments are from the middle class looking for affordable housing units as an investment for a future rise in prices.

2.3. Pilot Survey Before starting the main data collection, a pilot survey was undertaken during the first half of 2017 in five neighborhoods of different urban form types in the three cities to find pitfalls in the questionnaire. Table 2 summarizes the number of filled questionnaires in the selected neighborhoods. As a result of this activity, eight questions were slightly changed according to the interviewees’ feedback – i.e. choices were added to them or some of the choices were modified. For instance, in mode choice questions, “By bus/minibus/van/BRT” was changed to “By bus/minibus/metrobus/microbus/van/BRT” because there are metrobuses in Istanbul and microbuses in Cairo but these bus variances do not exist in Tehran. As seen in Table 2, in general, 202 questionnaires were filled out in the five neighborhoods. The output data of the questions that were modified according to the interviewers’ suggestions were dropped from the final outputs and treated as missing data. The results of the remaining 23 questions were kept in the final raw data.

Table 2: Pilot survey in five neighborhoods.

Number of Neighborhood City Neighborhood Name Type Filled Code Questionniares

Cairo C5 El-Hadiqah El-Dowliah 3 20

I1 Karagümrük & Hırka-i Şerif 1 65 Istanbul I3 Yıldıztabya (Gaziosmanpaşa District) 2 46

T3 Sadeghieh 2 41 Tehran T5 Golestan-Sharghi 3 30

2.4. Survey Instrument, Variables, & Representativeness The questionnaire included 31 questions, the last two of which collected information about the home place and working place (if any) of the respondents on two accompanying maps of the neighborhood (for home place) and the city (for work place). Appendix 1 presents the questionnaire in English. The surveyors translated the questionnaires to Persian, Turkish, and Arabic and prepared for interviews. The data collection and quantification of land use was undertaken in a way that a wide array of qualities and decisions regarding socio-demographics,

14 mobility decisions, infrastructures, and the built environment were measured. The data collection consisted of face-to-face interviews as well as geographic analysis with maps of the neighborhoods to quantify urban form. These two activities are explained separately in the sections below. In general, 45 variables were developed; some of the variables had collinearity with others so not all of them would be applied in empirical studies at the same time.

2.4.1. Variables Embedded in Questionnaire The questionnaire included six sections with the topics of individual and household information, commuting, local activities, public transport use, pedestrian and bicycle facilities, and neighborhood. The questionnaire facilitated generation of 29 variables, namely gender, age, activity, individual driving license ownership, household car ownership, number of driving license in household, household income, monthly living cost, frequency of commute trips, commute mode choice, reason for car use, number of non-work activities, shopping-entertainment place, shopping- entertainment mode choice in neighborhood, shopping-entertainment mode choice outside neighborhood, attractive shopping centers in neighborhood, reason for no social activity in neighborhood, frequency of public transit trips, reason for public transit use, reason for no public transit use, subjective security of public transport, cycling, reason for not cycling, reason for not walking, sense of belonging to neighborhood, entertainment place, neighborhood attractiveness perception, residential location choice, and finally last relocation time. It is possible to combine or transform a couple of these factors to develop more interesting variables, e.g. the proportion of average household income and costs can make a more viable variable. Seven of these variables are continuous (age, household car ownership, number of driving license in the household, household income, monthly living cost, frequency of commute trips, and last relocation-time) and 22 are categorical.

2.4.2. Land Use Variables Land use variables included 16 variables measured within a 600-meter walking catchment areas based on street network and centered by the respondents’ homes were taken as the effective area. Table 3 summarizes these variables and the related quantification method. Estimation of land use variables was fully undertaken by ArcGIS version 10.4. For measuring the number of neighborhood facilities and accessibility to neighborhood facilities, five types of facilities including bakeries (that are important retails in the Middle Eastern context), mosques (as the dominant religious buildings in the observation countries), clinics and other medical centers, schools, and urban parks and other green spaces were pinpointed on Google Earth and imported into the ArcGIS environment.

Fig. 2: Presentation of neighborhood boundaries and 600-meter catchment areas centered by homes in three neighborhoods in Tehran (A1, B1, and C1) and distribution of different facility types in the same areas (A2, B2, and C2).

Fig. 3: Details of quantification of two land use variables: the number of accessed facilities and the average distance to them. The sketch on the left hand shows the walkable distances from an exemplary house to facilities located 600 meters out of the neighborhood boundaries. On the right side, the catchment 600-meter catchment area of an imaginary house and its accessed local facilities are illustrated.

The above five retail types were inspired by the main local functions of the Neighborhood Development guide from Leadership in Energy and Environmental Design (LEED). The source for the location of the local functions in Tehran was the online map of the city municipality (map.tehran.ir). For Istanbul and Cairo, the functions inputted to Google Maps were trusted and applied. Of course, none of the above – particularly the Google inputs – can reliably represent the reality about the number of the selected infrastructures, but it was assumed that this deficiency exists for all the cities and neighborhoods so it can provide a level of consistency. A lack of consistent data in the three cities was one of the limitations of this research. This barrier was confronted when trying to obtain the existing online data of local facilities. Here, the sources of data had to be adopted from different sources. The Tehran Municipality website provided more reliable data in this case. To estimate the number of local facilities and their accessibility, amenities located outside the neighborhood boundaries within a 600-meter buffer zone were also brought into the calculations. These extra areas were added to the calculations to cover the facilities located outside the neighborhoods but still accessed by the homes located near the edge of the neighborhoods.

Table 3: Land use variables and their estimation methods.

Variable Unit Quantification Method

The street network-based distance between home and workplace of Commuting respondents who have work/study activity was calculated by the information of meter Distance Q30 (place of home in the neighborhood) and Q31 (workplace signed on the city map). The number of intersections per hectare in a 600m-catchment area (based on the network) of each of the respondents’ homes. Calculations were done for Intersections Nodes/ha areas inside the neighborhood boundary or outside. This indicator quantifies Density the number of intersections per unit area. Higher densities indicate better connectivity. The number of links (street segments) divided by nodes (street intersections) of the street network within 600m-catchment area (based on the network) of Link Node each of the respondents’ homes. Calculations were done for areas inside the - Ratio neighborhood boundary or outside. This indicator evaluates the typology of intersections (i.e., four- and five-ways intersections get higher values than three-way intersections). Values of 1.4 and higher indicate good connectivity. The length of streets divided by the area of the 600-meter catchment area Street (based on the network) of the respondents’ homes. Calculations were done for Length m/ha areas inside the neighborhood boundary or outside. Higher densities indicate Density better connectivity. The number of neighborhood public facilities within a 600m-catchment area No. of (based on the network) of the respondents’ homes. The facilities included five Accessed - types: bakeries, clinics and other medical centers, mosques, parks, and Facilities schools. No. of The number of bakeries within a 600m-catchment area (based on the Accessed - network) of each of the respondents’ homes. Bakeries No. of The number of clinics within a 600m-catchment area (based on the network) Accessed - of each of the respondents’ homes. Clinics No. of The number of mosques within a 600m-catchment area (based on the Accessed - network) of each of the respondents’ homes. Mosques No. of The number of parks within a 600m-catchment area (based on the network) of Accessed - each of the respondents’ homes. Parks No. of The number of schools within a 600m-catchment area (based on the network) Accessed - of each of the respondents’ homes. Schools The average distance (based on the network) from each respondent’s home to Accessibility neighborhood public facilities within the neighborhood or located within a to meter linear 600-meter buffer (like the crow flies) outside the neighborhood Neighborhoo boundary. The facilities included five types: bakeries, clinics and other medical d Facilities centers, mosques, parks, and schools. The average distance (based on the network) from each respondent’s home to Accessibility meter bakeries within the neighborhood or located within a linear 600-meter buffer to Bakeries (like the crow flies) outside the neighborhood boundary. The average distance (based on the network) from each respondent’s home to Accessibility meter mosques within the neighborhood or located within a linear 600-meter buffer to Mosques (like the crow flies) outside the neighborhood boundary. The average distance (based on the network) from each respondent’s home to Accessibility meter clinics within the neighborhood or located within a linear 600-meter buffer (like to Clinics the crow flies) outside the neighborhood boundary. The average distance (based on the network) from each respondent’s home to Accessibility meter schools within the neighborhood or located within a linear 600-meter buffer to Schools (like the crow flies) outside the neighborhood boundary. The Average distance (based on the network) from each respondent’s home to Accessibility meter parks within the neighborhood or located within a linear 600-meter buffer (like to Parks the crow flies) outside the neighborhood boundary.

In order to describe the method of land-use quantification, the neighborhood boundaries, the 600-meter catchment areas centered by homes, and the five different facility types are illustrated in the three neighborhoods of Sangalaj (T1), Towhid (T3), and Shahran-Jonoubi (T5) in Fig. 2. Fig. 3 presents conceptual maps summarizing all the ideas of the

18 three examples illustrated in Fig. 2, i.e. neighborhood walkable catchment areas as well as the outer ribbons and estimation method of accessibilities.

2.5. Sample Characteristics and Validation The interviews were organized in a way that guaranteed a high level of precision. For that, more than 640 inhabitants were interviewed in all neighborhoods, except two neighborhoods in Tehran with 420 and 441 respondents. The sample sizes and precisions were calculated by methods suggested by Cochran (1963, 75). There are two formulations for this aim:

푍2푝푞 푛 = (1) 0 푒²

Where Z² is the abscissa of the normal curve, p is the estimated proportion of an attribute, e is the level of precision, and finally, q is equal to 1-p. The result is adjusted by the following relation:

푛0 푛 = (푛 −1) (2) 1+ 0 푁

Where n is the sample size (number of valid respondents shown in several tables in this paper). The amount of e was calculated as a plus or minus value of possible error.

These error estimations based on sample sizes of each neighborhood were also performed for questions that went to the individual respondents and those that went to whole households. The questions about the number of household cars, driving license, average monthly income, average monthly costs, and the number of years since the last relocation targeted whole households; except for these five questions, all others were related to the specific individual respondents. The sample sizes of individual questions were multiplied by the average household size of the city to estimate the city-level precision. The city-level average household sizes applied in this study were 4.3 for Tehran, 3.5 for Istanbul, and 3.75 for Cairo. Table 4 shows the sample sizes in the city and neighborhood levels and the precisions in the neighborhood levels. These figures show that the survey provides a good representativeness in the neighborhood level.

For validation of the raw output data, no interviewee responses were treated as survey loss. In other words, the data processing staff did not remove the data of any respondent because of partially problematic or missing data. Instead, the data was checked based on the overall logic of the questionnaire considering all of the questions, and accordingly, problematic answers were discarded and treated as missing. In a few cases, clearly mistaken cell values were replaced by a logical response.

Numerical methods for finding outliers were not followed, but the normal distribution plots of all variables were checked, and obvious outliers were detected and deleted from the data.

Table 4: Sample sizes and precisions.

Level Level Neighborhood Name

(n)

(N)

City

Code

Variables Variables

City

Individual Individual

Household Household

Population Population

Sample Size Sample

Level Sample Sample Level

Precision for for Precision for Precision

% of Sample of %

for Individual Individual for

for Household Household for

Neighborhood Neighborhood Neighborhood -

Variables (±%) Variables (±%) Variables

% of Population Population of % Population of % C-1 Darb El-Ahmar 470 5,7 0,64 4,5 2,4 2,3

C-2 Bab El-Shariah 470 5,7 0,69 4,5 2,59 2,3

C-3 El-Zaytoon El-Sharkia 470 5,7 1,02 4,5 3,83 2,3

2786 Cairo C-4 Al-Amiriyah Al-Shamaliah 470 5,7 0,41 4,5 1,54 2,3 391799 C-5 El-Hadiqah El-Dowliah 436 5,3 2,02 4,6 7,58 2,3 C-6 Al-Matar 470 5,7 0,7 4,5 2,63 2,3 I-1 Karagümrük & Hirka-i Serif 470 5,7 1,32 4,5 4,62 2,4

I-2 Balat & Ayvansaray 470 5,7 1,41 4,5 4,94 2,4

I-3 Yildiztabaya 460 5,6 1,77 4,5 6,2 2,4

2781 I-4 Kosuyolu & Acibadem & Hasanpasa 461 5,6 0,85 4,5 2,98 2,4

342144 Istanbul I-5 Adnan Kahveci 460 5,6 0,62 4,6 2,17 2,4 I-6 Basaksehir & Basak 460 5,6 0,39 4,6 1,37 2,4 T-1 Sangalaj 460 5,6 1,57 4,5 6,75 2,1

T-2 Arg-Pamenar 460 5,6 7,84 4,4 33,71 1,8

T-3 Towhid 460 5,6 1,27 4,5 5,46 2,1

2717 T-4 Sadeghieh 476 5,7 1,16 4,5 4,99 2,1

Tehran 151037 T-5 Shahran-Jonoubi 420 5,1 1,92 4,7 8,26 2,2 T-6 Golestan-Sharghi 441 5,3 2,59 4,6 11,14 2,1

3. Findings The findings include a corrected and calibrated database consisting of three smaller city-level samples. This section contains descriptive statistical findings of the overall sample of the three cities as well as city-level samples. The data structure allows for separation of the database in neighborhood-level samples, but this is not presented in this report. The following presents the descriptive statistics of the overall and city-level samples divided into continuous and categorical data.

3.1. Overall Sample The overall sample provides the possibility of developing universal models for the studied neighborhoods and also models with relative or acceptable representativeness for the three observed cities.

3.1.1. Continuous Data Using the questionnaire and land-use data, 24 continuous variables were developed, the descriptive statistics of which are presented in Table 5. Since the data was collected from different socio-economic contexts, a wide range resulted for most of the variables. However, the means and standard deviations present a more reliable perspective, e.g. household car

20 ownership ranged between 0 and 11, but the mean is a little less than one car per household. Other good examples are household monthly income and living costs that yield 4094€ and 3669€ as averages. Likewise, data on the amount of time since the respondent’s last residential relocation showed an average of 15 years with a large deviation of 11 years and a wide range of 0 to 87 years. The average commuting distance stands at 8.8 km, while a considerable percentage is less than 1 km. A couple of commuting distances in Cairo were around 77 km because the relevant respondents commuted from Cairo to one of the new suburbs around the city, for instance from El Hadiqah El-Dowliah in Nasr City to Sixth of October New City. The distribution of intersections density is largely exogenous, maybe because of the presence of different urban forms a nd planning approaches in the three cities. The accessibility estimations reveal that the observed sample population has walking accessibility to 12.77 local facilities including bakeries, mosques, schools, clinics, and parks located 1353 m away from their homes on average. Most accessible distances fall between 900 and 1400 m. More details are presented in Table 5. To explore the nature of the distribution of the numerical data, two tests of normality were conducted: Kolmogorov-Smirnov and Shapiro-Wilk tests. In this paper, the details of these two tests are not introduced. The null hypothesis of both is that there is no statistically significant relationship between the distribution and a normal distribution. P-values of less than 0.05 represent non-normal distributions. Both of the two tests were applied because Shapiro-Wilk tests usually show higher significance. Table 6 illustrates the normality test results. As seen there, all of the variables show a P-value of less than 0.001, indicating that their subjects are non-normally distributed.

Table 5: Descriptive statistics of continuous variables.

Standard Variable N Minimum Maximum Mean Deviation

Age 8225 7 90 36.37 13.73 Household Car Ownership 7667 0 11 0.97 0.79 No. Driving License in 7832 0 8 1.79 1.12 Household

Houshold Income (€)* 8045 0 70000 4094.11 4525.18 Monthly Living Cost (€)** 8143 40 75000 3668.58 3532.38 Frequency of Commute Trips 7154 0 70 10.61 6.41 No. of Non-Work Activities 7735 0 30 3.02 2.58 Last Relocation-Time 8210 0 87 15.15 11.80 Commuting Distance (m) 5123 0 77106 8824.79 8661.35 Intersections_Density 8097 0.119 11.563 3.62 2.61 (Nodes/ha) Link Node Ratio 8097 1.110 2.833 1.57 0.21 Street Length Density (m/ha) 8098 92.3 646.1 303.96 75.46

No. of Accessed Facilities 8097 0 55 12.77 9.40 No. of Accessed Bakeries 8097 0 16 3.66 3.38 No. of Accessed Clinics 8097 0 11 1.24 1.43 No. of Accessed Mosques 8097 0 25 3.40 4.22 No. of Accessed Parks 8097 0 10 1.58 1.70 No. of Accessed Schools 8097 0 21 2.89 3.16 Accessibility to Bakeries (m) 8132 514 4516 1263.99 479.85 Accessibility to Mosques (m) 8132 745 4367 1460.89 549.05 Accessibility to Clinics (m) 8132 123 4282 1257.73 557.69 Accessibility to Schools (m) 8132 351 3651 1316.23 509.71 Accessibility to Parks (m) 8132 411 4242 1464.98 461.98 Accessibility to Facilities (m) 8132 633 3946 1353.08 453.28 *and **Based on conversion rates of Toman, Turkish Lira, and Egyptian Pound to Euro in summer and autumn of 2017.

Table 6: Test of Normality for continuous data.

Kolmogorov-Smirnov Shapiro-Wilk Variable Statistic df P-value Statistic df P-value

Age 0.092 4326 <0.001 0.962 4326 <0.001 Household Car Ownership 0.294 4326 <0.001 0.800 4326 <0.001 No. Driving License in Household 0.196 4326 <0.001 0.907 4326 <0.001 Houshold Income 0.169 4326 <0.001 0.670 4326 <0.001 Monthly Living Cost 0.137 4326 <0.001 0.815 4326 <0.001 Frequency of Commute Trips 0.184 4326 <0.001 0.878 4326 <0.001 Last Relocation-Time 0.188 4326 <0.001 0.853 4326 <0.001 Commuting Distance (m) 0.139 4326 <0.001 0.882 4326 <0.001 Intersections_Density (Nodes/ha) 0.151 4326 <0.001 0.797 4326 <0.001 Link Node Ratio 0.145 4326 <0.001 0.902 4326 <0.001 Street Length Density (m/ha) 0.093 4326 <0.001 0.979 4326 <0.001 No. of Accessed Facilities 0.043 4326 <0.001 0.992 4326 <0.001 No. of Accessed Bakeries 0.135 4326 <0.001 0.902 4326 <0.001 No. of Accessed Clinics 0.172 4326 <0.001 0.867 4326 <0.001 No. of Accessed Mosques 0.213 4326 <0.001 0.818 4326 <0.001 No. of Accessed Parks 0.247 4326 <0.001 0.742 4326 <0.001 No. of Accessed Schools 0.184 4326 <0.001 0.843 4326 <0.001 Accessibility to Bakeries (m) 0.198 4326 <0.001 0.829 4326 <0.001 Accessibility to Mosques (m) 0.124 4326 <0.001 0.881 4326 <0.001 Accessibility to Clinics (m) 0.171 4326 <0.001 0.781 4326 <0.001 Accessibility to Schools (m) 0.117 4326 <0.001 0.863 4326 <0.001 Accessibility to Parks (m) 0.113 4326 <0.001 0.898 4326 <0.001 Accessibility to Facilities (m) 0.083 4326 <0.001 0.976 4326 <0.001

3.1.2. Categorical Data Twenty-two categorical (including binary) variables were developed, all of which resulted from the questionnaires. Several of them were results of perceived judgment of respondents based on multiple choice or five-point Likert scale. These are most useful for discrete choice modeling. Appendix 2 shows the frequencies and percentages of responses for each category. The percentages are partial shares of the categories in the related question within the whole sample (three cities). These findings reveal the importance of walking non-work travels inside the residential neighborhoods in the sample (71.3%). Bus (33.8%) and personal car (37%) have a dominant role in non-work travels to outside of the neighborhood. Bus (23.5%), personal car (14.8%), and rail systems (13%) are the most used commute modes. More than one-third use public transport every day, while 89.3% of them do not bike at all. The most important reason behind public transport ridership is its low price, while personal interest in car use is the strongest reason for refraining from public transport. Lack of biking facilities and social and cultural reasons are the most referred reasons for not cycling. The neighborhood itself has a decisive role in attracting non-work and leisure trips (86.8%). One of the reasons supporting this can be that 63.5% of respondents believe their neighborhood has attractive shops and shopping centers. On the other hand, those who do not have any social activity inside their neighborhood cite lack of good shops and facilities as the reason for their social absence from their neighborhood. Like the first reason, those who do not have so much walking behavior say that destinations being far away is what leads to their little walking activity. About three-fourths of the sample population has a sense of belonging to their neighborhood. However, 60.4% of respondents prefer to entertain themselves in places outside their neighborhood rather than within it. The most prominent causes of residential location choices ranked by frequency of selection are affordability (29.1%), living in the place for a long time, i.e. since childhood or birth (21.2%), and proximity to relatives (21%).

3.2. City-Level Samples The overall database can also be separated into city-level databases, each comprising of 2700-2800 valid subjects. The validated results can provide a good ground for comparative studies using the identical questionnaire and survey methods. The outputs are again separated into continuous and categorical data, the summary of which is presented below.

3.2.1. Continuous Data A summary of the inter-city comparative findings is presented in Appendixes 3 and 5. The percentages in Appendix 3, referred to the total sample population of the three cities (N). According to these two figures, the average age of the respondents was 35.2 years in Cairo , 35.9 in

Istanbul, and 38.1 in Tehran,. The number of driving licenses per household in Tehran (≈2.5) is considerably higher than in the other two cities. Because of the current political circumstances and their impacts on international currencies and their conversion to Euro, particularly in the MENA region, the results of the two financial variables do not seem so useful. This is seen especially clearly with the Iranian Toman. The average household income and costs in Tehran are much lower than in Istanbul and Cairo. The car ownership rate in the Istanbul sample is lower than in the Tehran and Cairo samples (0.7, 1, and 1.2, respectively). The frequency of commute trips is higher in Cairo compared to Tehran and Istanbul, while the average number of non-work activities made by each individual is higher in Tehran than in the other two. In Cairo, inhabitants seem more attached to their or their parent’s old houses: the last relocation average time in Cairo dates back to more than 20 years ago, while in Istanbul and Tehran it was only 11 to 12 years. Istanbul has the longest commuting distances with about 10839 m (one way), followed by Tehran with 9096 m and Cairo with 6670 m (Appendix 3). With about two and a half intersections per hectare, the street network of Istanbul is less woven compared to Tehran and Cairo with roughly four intersections each. No major difference is detected in the link node ratio of the three cities. Access to all types of neighborhood facilities in Istanbul and Tehran is about 15 to 16 amenities but is as low as 7.5 in Cairo. From the view of the number of accessed facilities in the walkable catchment areas, Istanbul is better than the other cities in terms of accessing bakeries, mosques, and urban parks and green spaces. Tehran is better with medical centers and schools. Nevertheless, the average walkable accessible distances are shorter in Cairo (≈1100 m) than in Tehran (≈1400 m) and Istanbul (≈1500 m). In Tehran, the walkable distances are very much equal for the five neighborhood amenities. In Istanbul, people must walk as far as about 1750 m to reach a mosque and more than 1500 m to reach a school. In Cairo, a school is available by walking about one kilometer on average. To sum up the accessibility findings, the case neighborhoods in Cairo have less amenities but provide better walking access to the neighborhood residents than Tehran and Istanbul.

3.2.2. Categorical Data Appendixes 4 and 6 reflect the outcomes of the qualitative data collection by means of categorical indicators. The percentages in Appendix 4 represent the city-wide shares. As shown in these figures, there is a good gender balance in the Istanbul and Cairo samples but slightly more male respondents in the Cairo sample. In all cities, there were twice as many respondents with an ongoing activity outside the home like working or studying than those who stay at home. This difference is larger in Cairo. More respondents possess a driving license in Tehran and Istanbul

24 compared to those without one, but a larger share of people in Cairo are not allowed to drive. In all cities, residents are in favor of walking as a mobility mode for shopping or entertainment activities inside their neighborhoods when they choose to conduct local activities (71.3% in general). This is more visible in Istanbul (0.9% of Istanbul’s sample). For conducting activities outside the neighborhood in Istanbul, the three main modes are bus, urban rail systems, and personal car, while the succession is bus, car, and rail in Cairo and car, bus, and rail systems (i.e. metro) in Tehran. At 64.4%, the dominance of personal car for non-work activities in farther places in Tehran is very obvious. For commuting, bus is still the dominant mode in Istanbul and Cairo, but metro and personal car are the dominant modes in Tehran (18.8% and 19.2%). This reflects the superiority of Tehran’s metro network to its bus system, although the city is more automobile-dependent for commuting. In all three cities, the main reason for car use is better comfort (if the respondents often use car). Daily public transit riders in Istanbul (45%) are relatively more than in Tehran and Cairo (33-34%). On the other hand, fewer people in Istanbul use public transport rarely or never compared to people in Tehran and Cairo. The most important reason for public transport use is that it is cheaper, but this reasoning varies in Cairo (33%), Istanbul (35%), and Tehran (19%). The main barrier to public transport ridership is the tendency to use the car (Cairo: 11.5%, Istanbul: 3%, Tehran: 14%), but comfort of public transport systems also seems important. The rates of not biking are identically dominant in the three cities (Cairo: 91%, Istanbul: 87%, and Tehran: 90%). In Istanbul, lack of biking facilities and infrastructures is the main reason of not bicycling (56%), while in Cairo and Tehran, the main cause is social and cultural reasons (43% and 38%, respectively). The main reason for not walking locally is the lack of nearby destinations, which relates to the low accessibility of neighborhood facilities. The local neighborhood is by far the main place for shopping and leisure activities in all three cities. However, a considerable number of people believe that there are not enough shops and shopping centers in their neighborhoods (Cairo: 65.2%, Istanbul: 73.5%, and Tehran: 51.7%). People in the three cities have a strong sense of belonging to their neighborhoods (Cairo: 78%, Istanbul: 70%, Tehran: 77%) and find their neighborhood acceptably attractive or moderately attractive but prefer to travel to destinations outside their neighborhood for entertainment purposes (Cairo: 61%, Istanbul: 56%, Tehran: 65%).

4. Conclusion This study presents the results of a data collection about urban mobility, built environment, human perceptions, and infrastructures in three megacities of the MENA region with a considerable number of respondents distributed in different neighborhoods and urban form types. The output

25 data addresses the situation of urban form and mobility decisions in mid- 2017. The disaggregate data provided by this survey allows for a wide range of empirical and comparative studies on a less-studied geographical context. The need for good-quality mobility and land use data within the region highlights the importance of this survey and similar activities in MENA. It is expected that collecting data at individual level enables researchers to study the human behaviors and habits regarding urban mobility more comprehensively. Such studies can hopefully break the dominance of technology- and engineering-oriented urban transportation research of the region and lead it toward human-oriented mobility studies. Future data collections on the MENA region can bring in more interdisciplinary variables and topics, including but not limited to public health of various age groups like the elderly and children, energy use, air pollution, the effects of life course events on short, medium, and long-term mobility decisions (mobility biographies), the interactions with urban sprawl, and finally, more in-depth perceptions of people – particularly about walking, car use, and security in public transport systems.

Acknowledgments This research was funded by the German Research Foundation (DFG) under the project number MA 6412/3-1 (Urban Travel Behavior in Large Cities of MENA Region- UTB-MENA).

5. Appendixes

5.1. Appendix 1: Survey instrument

Urban Travel Behavior in Large Cities of MENA Region (UTB-MENA) Travel Behavior Questionnaire

Date: City: Germanyany Survey Phase: XX Pilot XX Main GermanyGemay

Questionnaire Code: Ger manyGany Interviewer: Charlckes-Grundschule

Section 1: Individual & Household Information

1. Gender: XX Female XX Male

2. Age: 9999 s

3. What is your daily activity? XX Work and/or Study XX No Work/Study

4. Do you have driving license? XX Yes XX No

5. How many cars do you own in your household? _

6. How many people have driving license in your household? _

7. How much money do your household earn monthly? (Gross) __

8. How much money do your household spend monthly? _ _ _

Section 2: Commuting

9. How many commute trips did you have during the past seven days? ____

10. If you have chosen “Work and/or Study” in question 3, how do you most frequently go to your work/study place? (one most important option) XX On foot XX By bicycle XX By motorbike XX By taxi XX By taxi apps XX By informal public transport XX By personal/household car XX Others XX By bus/minibus/metrobus/microbus/BRT/van XX By metro/light rail train/tram XX By organizational service/shuttle

11. If you use personal car as daily transport mode, what is the main reason? (one option) XX It is cheaper XX It is more comfortable XX It is more secure

XX It takes less time XX There is no public transportation XX I like driving

Section 3: Local Activities

12. How many times did you go out for entertainment, shopping, etc. (non-work activities) during the past seven days? ___ _

13. Do you often buy the daily living stuff inside your neighborhood or go farther? □□ Neighborhood □□ Farther

14. How do you most frequently go for shopping/entertainment inside your neighborhood? (one option) XX On foot XX By bicycle XX By motorbike XX By taxi XX By taxi apps XX By informal public transport XX By personal/household car XX Others XX By bus/minibus/metrobus/microbus/BRT/van XX By metro/light rail train/tram XX By organizational service/shuttle

15. How do you most frequently go for shopping/entertainment outside your neighborhood? (one option) XX On foot XX By bicycle XX By motorbike XX By taxi

XX By taxi apps XX By informal public transport XX By personal/household car XX Others XX By bus/minibus/metrobus/microbus/BRT/van

XX By metro/light rail train/tram XX By organizational service/shuttle

16. Are there attractive shops or shopping centers in your neighborhood? □□ Yes □□ No

17. If you do not shop or do not join social activities (like shopping/entertainment) in your neighborhood, what is the reason? Do not answer if you shop in your neighborhood. (one option) XX There are no suitable shops or facilities XX There are no suitable streets, allies, routes XX There is no good social atmosphere XX There shops or facilities are expensive XX There is little security in the neighborhood XX To avoid boredom by visiting new places/people

Section 4: Public Transport

18. How often do you use public transit? XX Every day XX a few times per week XX a few times per month XX rarely XX almost never

19. If you use the public transit, what is the main reason? (one option) XX It is cheaper than other ways XX It is faster XX For safety reasons

XX It is more secure XX It is accessible XX I do not have car

20. If you do not use public transit, what is the reason? (one option) XX It is not comfortable XX It is expensive XX Far stations

XX No accessibility/no public transportation XX Social problems XX It is slow XX I prefer my own car

21. How do you find the security of public transport of your city or area? XX Very secure XX Secure XX Medium XX Insecure XX Very Insecure

Section 5: Pedestrian & Bicycle Facilities

22. Do you cycle to your near destinations inside your neighborhood? XX Yes XX No

23. If you do not cycle to your near destinations, what is the reason? (one option) XX Social and cultural reasons XX lack of biking facilities XX too old/disabled XX It is slow/takes too much time

24. In situation that you do not walk to destinations in your neighborhood and prefer to use a vehicle, what is the main reason? (one option) XX The destinations are not near my living place XX There are no attractive and beautiful routes XX The streets are not safe

XX There are social and cultural problems in the spaces near my living place XX I do not like walking XX It is slow/takes too much time

Section 6: Neighborhood

25. Do you feel belonging to your neighborhood? XX Yes XX No

26. Where do you usually prefer to have entertainment? XX In my own neighborhood XX Far away

27. How do you find the social/recreational facilities of your neighborhood? XX Very attractive XX Acceptably attractive XX Medium XX Little attractive XX Not attractive or not available

28. Why did you choose this neighborhood to live? (one option) XX The house was affordable to buy or rent XX The house was near to my working place/school XX The surrounding environment is attractive

XX The house will have higher price in the future

XX To be near to our relatives and/or friends XX I live here since I was born/my childhood X The house was easy for me to commute to my working place/school XX Public transportation is available around the neighborhood

29. How many years ago did your household move to the current home? (number of years) __ __

30. Please sign the place of the nearest intersection of streets to your house on Map 1 (attached).

31. If you work or study, please sign the place of the nearest intersection of streets to your working place on Map 2 (attached).

Thank you for your participation in this survey

5.2. Appendix 2: Categorical data

Percent

Percent Percent Percent

requency

Variable Variable Variable Variable

Category Category Category Category

Frequency Frequency Frequency F

No response No response No response No response

55 52 58 93

0.6

0.7 0.7 1.1

No work / Female No Farther

study

997

28.9

45.1 42.0 12.0

3732 2391 3483

Gender Male Activity Work / Study Yes Neighborhood

Entertainment Place Entertainment

Ownership

70.5

54.3 57.3 86.8

4497 5841 4743 7194

Total Total Total Total

Individual Driving License License Driving Individual

100

100 100 100

8284 8284 8284 8284

Shopping

No response No response No response No response

65 95 69

0.8

1.1 0.8

29.9

2479

Bicycle Bicycle Bicycle No

33 20

1.3

0.4 0.2

111

35.6

2952

orhood

Bus Bus Bus Yes

eighb

7.7

637

23.5 33.8 63.5

1948 2800 5263

Attractive Shops in in Shops Attractive

Informal TP Informal TP Informal TP Total

2.7

1.8 2.6

149 223 214 100

8284

Metro / Light- Metro / Light- Metro / Light- No response

rail / Tram rail / Tram 0.3 rail / Tram

13.0 16.4 72.8

1074 1356 6032

Motorbike Motorbike Motorbike Expensive

2.3

2.3 2.4 0.3

191 191 200

On foot On foot On foot Far stations

58 68

7.9 0.7 0.8

658

71.3

5909

Commute Mode Choice Mode Commute

Prefer own Others Others Others

16 32

0.4 0.2 0.0 car

929

11.2

Entertainment Mode Choice in Neighborhood in Choice Mode Entertainment

Entertainment Mode Choice outside Neighborhood outside Choice Mode Entertainment

Personal / Personal / - Personal / No

household car household car household car accessibility 0.8

959

11.6

14.8 37.0

1225 3063

Shopping

Service / Service / Service / Not

Shopping

17 17

0.2

Shuttle 4.6 Shuttle Shuttle 0.2 comfortable 9.2

382 763

Reason for no Public Transit Use Transit Public for no Reason

Taxi Taxi Taxi Slow

68 84

1.0

0.8 3.0 2.8

250 233

Social Taxi apps Taxi apps Taxi apps

61 29

0.4

0.7 2.5 problems 2.1

207 170

No response No response No response No response

90.0

84.4 27.0 79.4

6990 7452 2237 6576

Public Public

Use

Destinations Cheaper Boring Accessible

1.4

0.3 not near 9.3

116 771

19.6

1626

Walking

Activity in in Activity

Transit Transit

Neighborhood I don't like I like driving Expensive Cheaper for not Reason

Reason for Reason

1.5

1.1 walking 4.0

Reason for Car Use Car for Reason

122 331

29.2

Reason for no Social Social for no Reason

2417

No attractive Less time Little security Faster

0.2

2.9 routes 1.8

241 147

15.9

1318 Social /

More No good More secure cultural

83 58

1.0 comfortable 9.3 atmosphere 3.4 0.7 774 282 problems

No suitable

Streets not More secure shops or No car

5.8 1.6 4.1 safe 1.7 130 facilities 477 340 141

No public No suitable Too old / Safety

29 21 64

0.3

transportation 0.4 streets 0.8 Disabled 3.1

260

No response Very attractive No response Very secure

89 79

4.6

1.1 1.0 2.7

383 225

Acceptably A few times Far away Secure attractive per month

25.3

60.4 16.9 29.3

5002 2092 1404 2427

A few times Neighborhood Medium Medium per week

33.5

38.5 26.2 45.7

3193 2778 2169 3782

Entertainment Place Entertainment Little Total Almost never Insecure

attractive 5.8

100 483

23.6

16.7

8284 1957 1386

No response Not attractive Every day Very insecure

0.8 2.3

998 190

12.0

37.6

3111

No No response Rarely No response

0.9

3.3

274

89.3 12.5

7397 1038

Frequency of Public Transit Trips Transit of Public Frequency

Subjective Security of Public Transport of Public Security Subjective

Cycling Yes Perception Attractiveness Neighborhood

9.9

821 Total Total Total

100

100 100 Total 8284 8284 8284

100

8284

No response Near working place No response No response

85 59

7.4

1.0 0.7

613 948

11.4

Public transportation Lack of biking Affordable No

available 3.9 facilities

324

29.1 33.9 24.4

2412 2807 2025

Attractive Since childhood Slow

8.4 7.7

698 640

21.2

1754

Yes

Social /

74.8 Commute to 6200 Total cultural

working place 4.9

100

410

33.5 8284 reasons 2779

Higher price in cycling for not Reason Too old /

Residential Location Choice Location Residential

the future 3.0 Disabled

247

13.4

1110

Sense of Belonging to Neighborhood to Belonging of Sense

Total

100

8284

Near relatives Total

100

21.0

1741 8284

5.3. Appendix 3: City-level continuous data

Cairo Istanbul Tehran Total

n n n n

City

n n n n

Mean Mean Mean Mean

St. Dev. St. Dev. St. Dev. St. Dev. St.

Minimum Minimum Minimum Minimum

Maximum Maximum Maximum Maximum

% of Total Total of % Total of % Total of % Total of %

Age 7 7

12 76 12 84 33 84 90

100

33.8 35.2 13.5 33.2 35.9 13.9 38.1 13.6 36.4 13.7

2778 2733 2714 8225

Household Car

1 0 8 0 0 1 0

10 10 11

0.9 0.7 0.7 1.2 0.7 0.8

100

Ownership 30.1 35.7 34.3

2306 2734 2627 7667

No. of Driving License in

0 8 1 0 7 1 0 7 0 8

1.6 1.2 1.5 2.3 1.8 1.1

100

Household 30.7 34.9 34.4

2403 2734 2695 7832

Household Income 0 0

400 100

32.3 33.9 33.8

2597 2731 2717 8045

70000 60000 60000 70000

6498.2 5812.9 4521.4 3576.3 1366.7 1530.7 4094.1 4525.2

9 Monthly Living Cost

40 40

400 400 100

33.2 33.5 33.3

2700 2730 2713 8143 366

983.8

75000 40000 40000 75000

5790.9 4248.4 4155.8 2643.6 1066.1 3532.4

Frequency of Commute

0 0 0 0

13 70 40 40 70

6.8 9.1 3.6 9.1 7.1 6.4

100

Trips 38.7 30.8 30.5 10.6

2768 2205 2181 7154

No. of Non-Work

0 0 0 3 0

30 10 10 30

2.5 2.1 2.5 2.0 4.1 3.1 2.6

100

Activities 31.5 35.3 33.2

2435 2734 2566 7735

Last Relocation Time 0 0 1 0

72 87 87 87

100

33.8 20.2 12.5 33.2 12.7 10.9 32.9 12.4 10.1 15.2 11.8

2777 2728 2705 8210

Commuting Distance

0 0

132 210 100

(m) 34.5 32.5 33.0

1768 1664 1691 5123

77106 49587 31682 77106

6669.9 8569.6 9095.9 6095.7 8824.8 8661.3

10838.9 10305.1

2 Intersections_Density

4.4 2.8 2.4 1.3 0.1 4.0 2.9 3.6 2.6

100

(Nodes/ha) 34.3 32.6 0.23 0.1

2781 2640 2676 8097

0.346 11.41 11.56

11.563

6 Link Node Ratio

1.6 0.2 1.7 0.1 1.5 0.2 1.6 0.2

100

34.3 32.

2781 2640 2676 8097

1.161 2.154 1.343 2.500 1.110 2.833 1.110 2.833

Street Length Density

100 304

(m/ha) 34.3 86.6 32.6 75.3 33.0 51.6 75.5 92.3

2781 2641 2676 8098

328.8 145.6 490.3 280.5 92.32 301.3 126.7 434.9 646.1

646.06

No. of Accessed

0 0 0 0

16 55 33 44 55

7.5 3.3 7.4 9.4

100

Facilities 34.3 32.6 16.2 12.6 14.8 12.8

2781 2640 2676 8097

3 No. of Accessed

0 5 4 0 3 0 0

15 33 16 16

1.5 1.3 5.4 4.2 3.7 3.4

100

Bakeries 34. 32.6

2781 2640 2676 8097

No. of Accessed Clinics 0 5 0 6 0 0

33 11 11

0.9 1.3 1.2 1.2 1.6 1.6 1.2 1.4

100

34.3 32.6

2781 2640 2676 8097

Cairo Istanbul Tehran Total

City

n n n n

Mean Mean Mean Mean

St. Dev. St. Dev. St. Dev. St. Dev. St.

Minimum Minimum Minimum Minimum

Maximum Maximum Maximum Maximum

% of Total n Total of % n Total of % n Total of % n Total of %

No. of Accessed

0 9 0 0 0

22 33 25 25

2.7 1.9 4.5 5.3 3.1 4.5 3.4 4.2

100

Mosques 34.3 32.6

2781 2640 2676 8097

No. of Accessed Parks 0 7 0 0 7 0

10 33 10

1.2 1.5 2.0 2.0 1.5 1.4 1.6 1.7

100

34.3 32.6

2781 2640 2676 8097

No. of Accessed Schools 0 8 0 0 0

12 33 21 21

1.3 1.8 3.0 2.9 4.5 3.6 2.9 3.2

100

34.3 32.6

2781 2640 2676 8097

Accessibility to Bakeries

514 553 801 100 514

(m) 34.2 32.9 32.9

2781 2331 2677 4516 2674 2622 8132 1264 4516

259.1 683.2 270.8 479.8

1026.6 1365.5 1409.2

Accessibility to Mosques

745 870 824 100 549 745

(m) 34.2 32.9 32.9

2781 1971 2677 4367 2674 2523 8132 4367

187.4 824.5 238.6

1217.5 1744.2 1430.4 1460.9

Accessibility to Clinics

251 123 769 100 123

(m) 34.2 32.9 32.9

2781 1852 2677 4282 2674 2491 8132 4282

305.2 818.4 289.8 557.7

1018.9 1335.0 1428.8 1257.7

Accessibility to Schools

999 351 791 891 100 351

(m) 34.2 32.9 32.9

2781 2143 2677 3651 2674 2898 8132 3651

298.2 666.6 291.9 509.7

1547.0 1415.2 1316.2

Accessibility to Parks

411 923 916 100 462 411

(m) 34.2 32.9 32.9

2781 2232 2677 4242 2674 2592 8132 1465 4242

405.2 504.8 315.5

1192.8 1664.5 1548.3

Accessibility to Facilities

212 633 780 892 100 633

(m) 34.2 32.9 32.9

2781 2031 2678 3946 2674 2593 8133 3946

637.9 241.9 453.3

1090.9 1532.1 1446.4 1353.1

5.4. Appendix 4: City-level categorical data

City City City

Cairo Cairo Cairo

Tehran Tehran Tehran

Istanbul Istanbul Istanbul

Variable Variable Variable

Category Category Category

n n n n n n n n n

% % % % % % % % %

No response 8 0 No response 8

47 47 24 38 47 10 0.3 1.7 0.0 0.3 1.7 0.9 response 1.4 1.7 0.4

A few times

Female Bicycle 0 9

0.4 0.0 0.3

454 303 647

36.6 49.6 49.1 per month 16.3 10.9 23.8

1019 1378 1335

Gende

A few times

Male Trips Bus

817 893 459 294

63.1 48.8 50.9 per week 29.3 32.1 16.9 38.4 51.6 10.8

1759 1356 1382 1070 1436

Of Of Informal

Almost

No response 2 3 public 0

47 25

0.1 1.7 0.1 6.6 4.9 6.0 0.9 0.0 7.0 never 184 137 162 189 transport

Freq

Metro / No work /

Every day 8 Light-rail /

9.2

628 893 870 955 123 918 401 704 251

study 22.5 32.1 32.0 34.3 44.5 33.8 14.4 25.3 Tram

Activity

Work / Rarely Motorbike

93 10 97

5.9 3.3 0.4 3.6

368 163 507

Study 77.4 66.2 67.9 13.2 18.7

2156 1841 1844

No response 4 7 No response On foot

47 11 26 21

0.1 1.7 0.3 0.4 0.9 0.8

847 347

30.4 12.5 38.4

1043

No Accessible Others 2 1 1

0.1 0.0 0.0

666 529 618 479

59.0 42.2 24.5 19.0 22.2 17.6

Mode Choice outside Neighborhood outside Choice Mode

1643 1174

Personal /

Individual driving driving Individual license ownership license Yes Cheaper household

Entert

919 971 527 816 498

40.9 56.1 75.2 33.0 34.9 19.4 29.3 17.9 64.4 1139 1560 2044 car 1749

Service/shu

No response Faster 0 2

0.0 0.1 0.6

649 942 888 301 661 356

23.3 33.9 32.7 10.8 23.8 13.1 ttle

Shopping

Bicycle 1 8 More secure Taxi 6

24 51 17 57

0.9 0.3 1.8 0.6 7.9 6.7 2.0 0.2

214 187

0.04

Reason for public transit use public transit for Reason

Bus No car Taxi apps 0

55 75

4.5 5.8 2.0 4.7 0.0 2.8

760 808 380 124 161 132

27.3 29.1 14.0

Informal No

public 7 Safety 6 5

16 15 43 47 17

0.3 0.6 4.6 0.5 0.2 1.6 0.2 1.7 0.6 126 response transport

Metro/light No No response

22 47 20

9.8 0.8 1.7 0.7

272 290 512

rail/tram 10.4 18.8 71.3 86.3 60.6 response

1987 2399 1646

Commute mode choice mode Commute

Motorbike Expensive 2 2 Far away

12 79 18

3.6 0.4 2.9 0.1 0.1 0.7

100

61.1 55.6 64.6

1702 1545 1755

Transit Use Transit

Neighborho

On foot Far stations Place Entertainment

25 12 31

Reason for no Public Public for no Reason

9.2 4.2 0.9 0.4 1.1

256 288 114 942

10.4 od 38.1 42.8 34.7

1062 1189

City City City

Cairo Cairo Cairo

Tehran Tehran Tehran

Variable Variable Variable

Istanbul Istanbul Istanbul

Category Category Category

n n n n n n n n n

% % % % % % % % %

Prefer own No Others 9 6 1

19 47 10

0.3 0.2 8.6 0.7 1.7 0.4

310 240 379

0.04 car 11.1 13.9 response

Personal / No Acceptably household

11 15 41

7.9 0.4 0.5 1.5

485 219 521 757 948 387

17.4 19.2 accessibility attractive 27.2 34.1 14.2 car

Service / Not Little

perception

43 73

5.4 6.8 1.6 2.6

150 189 321 369 572 573 812

Shuttle comfortable 11.5 13.6 attractive 20.5 20.6 29.9

9 Taxi Slow attractiveness Neighborhood Medium

37 22 48 19

1.3 0.3 0.8 1.7 0.7 6.1

166 633 881

45.4 22.8 32.4

Commute mode choice mode Commute

1264

Reason for no public transit use public transit for no Reason

Social Not

Taxi apps 1

37 23 82 21 67

1.3 0.8 2.9 0.8 2.5 3.9

108 322 568

0.04 problems attractive 11.6 20.9

Very No response No response

47 57 66 59

6.1 1.7 2.1 2.4 9.3 2.2

170 258

81.7 92.3 79.0 attractive

2276 2567 2147

No Cheaper 1 7 Insecure 8

17 29 48

0.6 0.3 6.1 1.0 1.7 0.3

405 814 167

0.04 14.5 29.3 response

I like driving Medium Affordable

62 10 23

2.2 0.4 0.8

646 684

41.5 40.1 55.6 23.2 24.6 39.8

1156 1116 1510 1082

Less time Secure Attractive

50 42

1.8 1.5 5.5 5.0 6.5

149 911 587 929 138 383 177

32.7 21.1 34.2 13.8

Reason for Car Use Car for Reason

Commute More Very

3 to working

31 95 48

4.7 1.1 5.6 0.1 3.4 1.7 9.8

303 132 339 156 267

comfortable 10.9 12.5 insecure place

Subjective Security of Public Transport of Public Security Subjective

Higher More secure Very secure price in the

75 14 41 61 51 17

2.7 0.5 1.5 4.1 2.2 1.9 4.0 0.6 4.3 113 future 112 118

No public Near

transportatio 3 No response 8

15 11 47 11

0.1 0.5 0.4 0.3 1.7 0.4

316 745 680

relatives 11.3 26.8 25.0 n

Residential Location Choice Location Residential

Near No response No working

30 47 16

1.1 1.7 0.6 6.4 3.7

177 335 101

90.6 87.2 90.1 12.0

2524 2425 2448

Cycling place

Public Farther Yes transportati

71 69

9.8 9.1 9.5 2.5 6.6 2.5

357 272 368 254 309 258 184

entertainment entertainment

12.8 13.5 11.1

place - on availabl

Neighborhoo Since No response

7.9

290 357 301 337 215

d 86.1 88.5 85.9 10.4 12.8 11.1 childhood 43.1 12.1

2399 2462 2333 1202

Shopping

Cycling

Lack of No No response 4 biking

14 47

0.5 1.7 0.1

411 841 965 691

Reason for not for not Reason 14.8 55.9 31.0 response 34.6 24.8 47.7

1555 1296

e mode e mode

neigh.

choice

- facilities

shops in in shops

S Attractive

City City City

Cairo Cairo Cairo

Tehran Tehran Tehran

Istanbul Istanbul Istanbul

Variable Variable Variable

Category Category Category

n n n n n n n n n

% % % % % % % % %

Bicycle 3 Slow No

55 53 45

2.0 0.1 2.0 1.6 8.6

362 233

13.0 65.2 73.5 51.7

1816 2043 1404

Social and

Neigh.

Shops in in Shops Bus cultural Attractive Yes

6.9 4.7

317 191 129 549

11.4 43.3 19.7 37.7 90.8 90.9 88.1 reasons 1205 1025 2529 2528 2395

Informal Too old /

public 4 Boring 8

34 65 43

1.2 0.1 6.8 9.9 2.3 0.3 1.6

185 518 275 317

Reason for not Cycling for not Reason Disabled 18.6 11.7 transport

Metro /

Light-rail / 0 No response Expensive

17 10 34 11 77

0.6 0.0 0.4 1.2 0.4 2.8

79.9 91.0 66.9 Tram 2227 2532 1817

Destinations Little

Motorbike 6 4 0 9

88 97 69

3.2 0.2 3.6 8.9 2.5 0.1 0.0 0.3

247 455

not near 16.7 security

I don't like No good On foot

54 13 30 40

6.0 1.9 4.0 0.5 1.1 1.5

167 110

65.8 87.8 60.1 walking atmosphere

1833 2442 1634

No suitable No attractive

Others 0 0 shops or

32 53 36 58

1.1 0.0 0.0 1.9 1.3 2.1 4.7 7.3 5.2 routes 131 204 142 facilities

Personal /

Entertainment Mode Choice in Neighborhood in Choice Mode Entertainment No suitable

- household 0

78 10 11

2.8 0.4 0.0 0.4

334 547

Reason for no Social Activity in Neighborhood in Activity Social for no Reason

12.0 20.1 Social and streets car cultural 0

10 48

0.4 0.0 1.8

problems

Reason for not Walking for not Reason Service / No

4 5 8 4 8

Shopping Shuttle 0.1 0.2 0.3 response 0.1 1.7 0.3

Streets not

Taxi 4 No

41 39 49 54 38

1.5 0.1 1.4 1.8 1.9 1.4

609 785 631

safe 21.9 28.2 23.2

Too old /

1 Taxi apps to Neighborhood Yes

17 11 33 36

0.6 0.4 1.2 1.3 7.0

191

Sense of Belonging Belonging of Sense

0.04 Disabled 78.0 70.1 76.5

2173 1949 2078

5.5. Appendix 5: City-level comparative continuous data

5.6. Appendix 6: City-level comparative categorical data

Literature Alghatani, M. and Bajwa, S. and Setunge, S. (2013), “Modeling the Influence of Socioeconomic and Land-Use Factors on Mode Choice: A Comparison of Riyadh, Saudi Arabia, and Melbourne, Australia”, International Journal of Environmental, Ecological, Geological and Geophysical Engineering Vol:7, No:2, pp: 69-81. Arabani, M., Amani, B. (2007), “Evaluating the Parameters Affecting Urban Trip-Generation”, Iranian Journal of Science & Technology, Transaction B, Engineering, Vol. 31, No. B5, pp. 547-560. Cervero, R. (1993), Transit-Supportive Development in the United States: Experiences and Prospects, Washington, DC: U.S. Department of Transportation, Federal Transit Administration. Cervero, R. (2002), “Built Environments and Mode Choice: Toward a Normative Framework”, Transportation Research Part D 7 (2002) 265–284. Handy, S., Cao, X., Mokhtarian, P. (2005), “Correlation or Causality between the Built Environment and Travel Behavior? Evidence from Northern California”, Transportation Research Part D 10: 427-444. Masoumi, H.E. (2015), Transformation of Urban Form and the Effects on Travel Behavior in Iran, Discussion Paper- Center for Technology and Society discussion paper series, paper Nr. 36/2015. doi:10.13140/RG.2.2.30717.97769 Masoumi, H.E., Terzi, F, & Serag, Y. (paper in review), Neighborhood-Scale Urban Form Typologies of Large Metropolitan Areas: Observations on Istanbul, Cairo, and Tehran. Cities. Özbil, A. (2013), “Modeling Walking Behavior in Cities Based on Street Network and Land-Use Characteristics: The Case of Istanbul”, Middle East Technical University, Turkey, Journal of Faculty of Architecture, Vol. 30, No. 2, pp. 17-33. Özbil, A. and Argin, G. and Yesiltepe, D. (2014), Pedestrian Route Choice by Elementary School Students: the Role of Street Network Configuration and Pedestrian Quality Attributes in Walking to School, SFB/TR 8 Report No. 038-09/2014, Report Series of the Transregional Collaborative Research Center SFB/TR 8 Spatial Cognition, Universität Bremen / Universität Freiburg, pp. 10-13. Rodriguez, D., Joo, J. (2004), “The Relationship between Non-motorized Mode Choice and the Local Physical Environment”, Transportation Research Part D 9:151–173. Sabry, A., and Talaat, H. (2015), “Factors Impacting Link Travel Speed Reliability: A Case Study at Cairo, Egypt”, Journal of Traffic and Logistics Engineering, Vol, 3, No. 1, pp. 67-71. Shbeeb, L. and Awad, W. (2013), “Walkability of School Surroundings and its Impacts on Pedestrian Behavior”, TeMA. Journal of Land Use, Mobility and Evnvironment, Vol. 6, No. 2, pp. 171-188. Soltani, A., Esmaeili-Ivaki, Y. (2011), “The Influence of Urban Physical Form on Trip Generation, Evidence from Metropolitan Shiraz, Iran”, Indian Journal of Science and Technology, Vol. 4, No. 9, pp. 1168-1174.

Soltani, A., Saghapoor, T., Izadi, H., Pakshir, A. (2012), “Trip generation and its relationship with land use diversity: Case studies of four urban districts in Shiraz metropolitan area”, Urban - Regional Studies and Research Journal, 3rd Year – No. 12 - spring 2012.

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Nr. 01/02 Susanne Schön, Dorothee Keppler, Brigitte Geißel: Gender und Nachhaltigkeit. Sondierung eines unübersichtlichen Forschungsfeldes. Nr. 02/02 Alexander Peine, Rainer Haase, Hans-Liudger Dienel: Prozessevaluation – Evaluation der interdisziplinären Zusammenarbeit im Rahmen der Forschergruppe Sentha. Nr. 03/02 Martina Schäfer: Kundenvielfalt erfordert Marktvielfalt – Eine Untersuchung der Potenziale von vier verschiedenen Bioeinkaufsformen. Nr. 04/02 Heike Walk: Global Governance – Hoffnung und Ernüchterung in der internationalen Klimapolitik. Nr. 05/03 Susanne Schön: Co-operation Management as a Part of Engineering Education. Nr. 06/03 Leon Hempel, Eric Töpfer: On the Threshold to Urban Panopticon? Objectives and Results of the "Urbaneye" Project on the employment of CCTV in Europe. Nr. 07/03 Dörte Ohlhorst: Der Weg ist das Ziel... Radfernwanderwege als nachhaltige Verknüpfung kontrastreicher Regionen. Nr. 08/03 M. Schophaus, H. L. Dienel, C. F. von Braun: Von Brücken und Einbahnstraßen. Aufgaben für das Kooperationsmanagement interdisziplinärer Forschung. Nr. 09/03 Leon Hempel, Hans-Liudger Dienel: Tele City Vision –Perceptions of ICT and its Impacts on City Competition. Nr. 10/03 Martina Schäfer, Benjamin Nölting, Lydia Illge: Zukunftsfähiger Wohlstand. Analyserahmen zur Evaluation nachhaltiger Wohlstandseffekte einer regionalen Branche. Nr. 11/04 Gabriele Wendorf, Doris Felbinger, Bettina Graf, Sabine Gruner, Helga Jonuschat, Olaf Saphöster: Von den Qualitäten des Wohnumfeldes zur Lebensqualität? Das Konzept des „Atmosphärischen“ als Ausgangspunkt einer integrierten Theorie. Nr. 12/04 Susanne Schön, Benjamin Nölting, Martin Meister: Konstellationsanalyse. Ein interdisziplinäres Brückenkonzept für die Technik-, Nachhaltigkeits- und Innovationsforschung. Nr. 13/04 Jörg Potthast, Hans-Liudger Dienel: „Die Zeiten des natürlichen Fortschritts sind vorbei.“ Verkehrssicherheit als Gegenstand von Forschung und Politik. Vertiefung der SMARTBENCH-Teilstudie Frankreich. Nr. 14/04 Achim Brunnengräber, Kristina Dietz, Bernd Hirschl, Heike Walk: Interdisziplinarität in der Governance-Forschung. Nr. 15/05 Elke Baranek, Corinna Fischer, Heike Walk: Partizipation und Nachhaltigkeit. Reflektionen über Zusammenhänge und Vereinbarkeiten. Nr. 16/05 Dorothee Keppler: Nachhaltigkeitskompetenzen. Zur Bedeutung geschlechtsspezifischer Kompetenzunterschiede für eine nachhaltige Technikentwicklung.

Nr. 17/05 Tina Boeckmann, Pamela Dorsch, Frauke Hoffmann, Dörte Ohlhorst, Ulrike Schumacher, Julia Wulff: Zwischen Theorie und Praxis. Anregungen zur Gestaltung von Wissenschafts-Praxis-Kooperationen in der Nachhaltigkeitsforschung. Nr. 18/05 Benjamin Nölting, Tina Boeckmann: Struktur der Land- und Ernährungswirtschaft in Brandenburg und Berlin – Anknüpfungspunkte für eine nachhaltige Regionalentwicklung. Nr. 19/05 Hans-Liudger Dienel: Grupy nieprofesjonalnych planistów i opinie mieszkańców. Nowa metoda uczestnictwa mieszkańców na przykładzie opracowania projektu dla jednej dzielnicy Berlina (Übersetzung Bürgergutachen „Zukunft Sparrplatz” der Senatsverwaltung für Stadtentwicklung Berlin 2001). Nr. 20/05 Adina Herde: Kriterien für eine nachhaltige Ernährung auf Konsumentenebene. Nr. 21/05 Christin Wemheurer, Jens Eitmann: Coaching in der ökologischen Landwirtschaft. Nr. 22/05 Dorothee Keppler: Zur Evaluierung integrativer Arbeitsmarktkonzepte für Menschen mit Benachteiligungen. Nr. 23/06 Benjamin Nölting: Die Politik der Europäischen Union für den ländlichen Raum. Die ELER-Verordnung, nachhaltige ländliche Entwicklung und die ökologische Land- und Ernährungswirtschaft. Nr. 24/06 Dorothee Keppler, Eric Töpfer: Die Akzeptanz und Nutzung erneuerbarer Energien in der "Energieregion" Lausitz. Nr. 25/07 Benjamin Nölting, Dorothee Keppler, Birgit Böhm: Ostdeutschlandforschung trifft Nachhaltigkeitsforschung - fruchtbare Spannungsfelder für die Entwicklung neuer Perspektiven in Ostdeutschland. Nr. 26/08 Dorothee Keppler: "Das persönliche Engagement derer, die hier sind, das ist doch das eigentlich Wertvolle". Die Bürgerausstellung als Forum für die Stimmen von BürgerInnen zur Zukunft der Energieregion Lausitz. Nr. 27/08 Benjamin Nölting: Social-ecological research for sustainable agriculture and nutrition. Nr. 28/08 Christine Dissmann, Nina Gribat, Benjamin Nölting: Bilder des Wandels – Wandel der Bilder. Analysen zu Ostdeutschland. Nr. 29/09 Leon Hempel, Michael Carius & Carla Ilten: Exchange of information and data between law enforcement agencies within the European Union. Nr. 30/09 Benjamin Nölting, Silke Reimann, Carola Strassner: Bio-Schulver- pflegung in Deutschland. Ein erster Überblick. Nr. 31/11 Jochen Gläser, Grit Laudel: Life with and without coding. Two methods of early-stage data analysis in theory-guided qualitative research. Nr. 32/12 Safaa Mohajeri, Daphne Reim, Martin Schönberg: Umgang mit den Herausforderungen der Existenzgründung.

Nr. 33/12 Benjamin Nölting, Martina Schäfer, Carsten Mann, Eva Koch: Positionsbestimmungen zur Nachhaltigkeitsforschung am Zentrum Technik und Gesellschaft. Nr. 33/13 Martina Schäfer, Dorothee Keppler: Modelle der technikorientierten Akzeptanzforschung. Überblick und Reflexion am Beispiel eines Forschungsprojekts zur Implementierung innovativer technischer Energieeffizienz-Maßnahmen. Nr. 35/15 Jochen Gläser, Grit Laudel: The Three Careers of an Academic. Nr. 36/15 Houshmand E. Masoumi: Transformation of Urban Form and the Effects on Travel Behavior in Iran. Nr. 37/17 Arbeitsgruppe Smart City (Hrsg.): Smart City: Zur Bedeutung des aktuellen Diskurses für die Arbeit am Zentrum Technik und Gesellschaft. Nr. 38/17 Jonas van der Straeten, Sebastian Groh, Setu Pelz, Alexander Batteiger, Hannes Kirchhoff, Natalia Realpe Carrillo, Martina Schäfer: From dualism to convergence– A research agenda for energy access. Nr. 39/18 Martina Schäfer, Hadeer Hammad, Marcia Frezza, Noha El-Bassiouny, Viola Muster: Transitions of the energy sector in Egypt, Brazil and Germany – a comparison of the interplay between government, the private sector and civil society Nr. 40/18 Judith Vey: Leben im Tempohome. Qualitative Studie zur Unterbringungssituation von Flüchtenden in temporären Gemeinschaftsunterkünften in Berlin. (Unter Mitarbeit von Salome Gunsch und Aryan Sehatkar)