Determination of Pedestrian-Friendly Environments in Ordu City Center Using the Pedestrian Environment Index (PEI)

Abstract

Most of the cities are designed in such a way that long distances require the use of motor vehicles between the places where people live and the areas where they work, shop or spend their free time. However, the global epidemic, which has recently affected the whole world, has led to the questioning of transportation preferences and urban space fiction. The restriction of transportation and public mobility within the scope of the fight against the global epidemic has once again emphasized the importance of pedestrian-friendly cities. A pedestrian-friendly city; it is a living environment where its residents can meet their basic needs such as shopping, education, health and recreation in a short distance by walking or cycling. In various cities of the world; there is an increasing amount of research being done on the level of walkability and identifying pedestrian-friendly zones. In this context, the aim of the study is; this study determines the areas with high pedestrian-friendly potential on the scale of the neighborhoods that make up the city center of Ordu, which is in a rapid urbanization process. The basic method adopted for this purpose is to calculate the Pedestrian Environment Index (PEI) values, developed as a quantitative and spatial scale, and to reveal them spatially. PEI; it consists of five sub-indices: land use diversity index (LDI), intersection density index (IDI), commercial density index (CDI), population density index (PDI) and park density index (PaDI). Neighborhoods within the scope of the study; the Pedestrian Environment Index values obtained because of the analysis were compared in terms of having a pedestrian-friendly environment. As a result; Yeni, Düz, and Şarkiye neighborhoods, which offer many alternatives in terms of trade and shopping areas and have a high variety of use, are more pedestrian friendly than neighborhoods with low YCI values. The neighborhoods with the lowest pedestrian-friendliness are Kirazlimanı, Aziziye, and Güzelyalı.

Keywords

• Pedestrian Zones
• Walkability
• Active Transportation
• Pedestrian Environment Index
• Ordu

Ordu Kent Merkezindeki Yaya Dostu Bölgelerin Yaya Çevre İndisi Kullanılarak Belirlenmesi

Abstract

Kentlerin birçoğu, insanların yaşadıkları yerler ile çalıştıkları, alışveriş yaptıkları ya da boş zamanlarını geçirdikleri alanlar arasında motorlu taşıt kullanımı gerektiren uzun mesafeler olacak şekilde kurgulanmıştır. Ancak son zamanlarda tüm dünyayı etkisi altına alan küresel salgın, ulaşım tercihlerinin ve kentsel mekân kurgularının sorgulanmasına neden olmuştur. Küresel salgınla mücadele kapsamında ulaşımın ve kamusal hareketliliğin kısıtlanması, yaya dostu kentlerin önemini bir kez daha vurgulamıştır. Yaya dostu bir kent; sakinlerinin yürüyerek ya da bisiklete binerek, çok kısa mesafelerde, alışveriş, eğitim, sağlık ve rekreasyon gibi temel ihtiyaçlarını karşılayabildiği bir yaşam ortamıdır. Dünyanın çeşitli kentlerinde; yürümeye elverişlilik düzeyi ve yaya dostu bölgelerin belirlenmesi üzerine giderek artan sayıda araştırma yapılmaktadır. Bu kapsamda çalışmanın amacı; hızlı bir kentleşme sürecinde olan Ordu’nun kent merkezini oluşturan mahalleler ölçeğinde yaya dostu olma potansiyeli yüksek bölgelerin belirlenmesidir. Bu amaç doğrultusunda benimsenen temel metot, kantitatif ve mekânsal bir ölçek olarak geliştirilen Yaya Çevre İndisi (YCI) değerlerinin hesaplanarak mekânsal olarak ortaya konmasıdır. YCI; alan kullanım çeşitliliği indisi (AKCI), kavşak yoğunluğu indisi (KYI), ticari yoğunluk indisi (TYI), nüfus yoğunluğu indisi (NYI) ve park yoğunluğu indisi (PYI) olmak üzere beş alt indisten oluşmaktadır. Çalışma kapsamındaki mahalleler; analiz sonucunda elde edilen Yaya Çevre İndisi değerleri kullanılarak yaya dostu bir çevreye sahip olma yönünden karşılaştırılmıştır. Sonuç olarak; ticaret ve alışveriş alanları yönünden çok sayıda alternatif sunan, alan kullanım çeşitliliğinin yüksek olduğu Yeni, Düz ve Şarkiye mahalleleri YCI değerleri düşük olan mahallelere göre daha yaya dostu çıkmıştır. Yaya dostu olma bakımından en yetersiz mahalleler ise Kirazlimanı, Aziziye ve Güzelyalı mahalleleridir.

Keywords

• Yaya Bölgeleri
• Yürünebilirlik
• Aktif Ulaşım
• Yaya Çevre İndisi
• Ordu

References

1. AZMI, D. I. VE KARIM, H. A. (2012). IMPLICATIONS OF WALKABILITY TOWARDS PROMOTING SUSTAINABLE URBAN NEIGHBOURHOOD. PROCEDIA- SOCIAL AND BEHAVIORAL SCIENCES 50, 204-213.
2. BİRLEŞMİŞ MİLLETLER-B.M. (2019). WORLD URBANIZATION PROSPECTS 2018 HIGHLIGHTS, DEPARTMENT OF ECONOMIC AND SOCIAL AFFAIRS, TRENDS IN URBANIZATION. ERİŞİM ADRESİ: https://population.un.org/wup/Publications/Files/WUP2018-Highlights.pdf
3. CARRINGTON, D. (2020). STUDY REVEALS WORLD’S MOST WALKABLE CITIES. ERİŞİM ADRESİ: https://www.theguardian.com/cities/2020/oct/15/study-reveals-worlds-most-walkable-cities.
4. CREATORE, M. I., GLAZIER, R. H., MOINEDDIN, R., FAZLI, G. S., JOHNS, A., GOZDYRA, P., BOOTH, G. L. (2016). ASSOCIATION OF NEIGHBORHOOD WALKABILITY WITH CHANGE IN OVERWEIGHT, OBESITY, AND DIABETES. JAMA, 315 (20), 2211-2220.
5. DOBESOVA, Z. VE KRIVKA, T. (2012). WALKABILITY INDEX IN THE URBAN PLANNING: A CASE STUDY IN OLOMOUC CITY. IN: BURIAN, J. (ED.), ADVANCES IN SPATIAL PLANNING. INTECH PUBLICATIONS, RIJEKA, CROATIA, PP. 179–196.
6. DUANY, A., PLATER-ZYBERK, E., SPECK, J. (2001). SUBURBAN NATION: THE RISE OF SPRAWL AND THE DECLINE OF THE AMERICAN DREAM, NORTH POINT PRESS: NEW YORK.
7. FOX, J. VE WEISBERG, S. (2019). AN R COMPANION TO APPLIED REGRESSION. SAGE, THOUSAND OAKS: CA.
8. FRANK, L. D., ANDRESEN, M. A., SCHMID, T. L. (2004). OBESITY RELATIONSHIPS WITH COMMUNITY DESIGN, PHYSICAL ACTIVITY, AND TIME SPENT IN CARS. AM J PREVENT MED, 27, 87-96.

9. FRANK, L. D., SALLIS, J. F., CONWAY, T. L., CHAPMAN, J. E., SAELENS, B. E., BACHMAN, W. (2006). MANY PATHWAYS FROM LAND USE TO HEALTH: ASSOCIATIONS BETWEEN NEIGHBORHOOD WALKABILITY AND ACTIVE TRANSPORTATION, BODY MASS INDEX, AND AIR QUALITY. JOURNAL OF THE AMERICAN PLANNING ASSOCIATION, 72 (1), 75-87.
10. FRANK, L. D., SALLIS, J. F., SAELENS, B. E., LEARY, L., CAIN, K., CONWAY, T. L., HESS, P. M. (2010). THE DEVELOPMENT OF A WALKABILITY INDEX: APPLICATION TO THE NEIGHBORHOOD QUALITY OF LIFE STUDY. BRITISH JOURNAL OF SPORTS MEDICINE, 44 (13), 924-933.
11. GEHRKE, S. R. (2017). LAND USE MIX AND PEDESTRIAN TRAVEL BEHAVIOR: ADVANCEMENTS IN CONCEPTUALIZATION AND MEASUREMENT (DOKTORA TEZİ, PORTLAND STATE UNIVERSITY). ERİŞİM ADRESİ: https://pdxscholar.library.pdx.edu/open_access_etds/3477.
12. GORI, S., NIGRO, M., PETRELLI, M. (2014). WALKABILITY INDICATORS FOR PEDESTRIAN-FRIENDLY DESIGN. TRANSPORTATION RESEARCH RECORD, 2464 (1), 38-45.
13. GORRINI, A. VE BERTINI, V. (2018). WALKABILITY ASSESSMENT AND TOURISM CITIES: THE CASE OF VENICE. INTERNATIONAL JOURNAL OF TOURISM CITIES, 4 (3), 355-368.
14. HARRIS, C. R., MILLMAN, K. J., VAN DER WALT, S. J., GOMMERS, R., VIRTANEN, P., COURNAPEAU, D., OLIPHANT, T. E. (2020). ARRAY PROGRAMMING WITH NUMPY. NATURE, 585 (7825), 357-362.
15. HUNTER, J. D. (2007). MATPLOTLIB: A 2D GRAPHICS ENVIRONMENT. IEEE ANNALS OF THE HISTORY OF COMPUTING, 9 (3), 90-95.
16. IACONO, M., KRIZEK, K. J., EL-GENEIDY, A. (2010). MEASURING NON-MOTORIZED ACCESSIBILITY: ISSUES, ALTERNATIVES, AND EXECUTION. JOURNAL OF TRANSPORT GEOGRAPHY, 18, 133-140.
17. KOH, P. P. VE WONG, Y. D. (2013). COMPARING PEDESTRIANS’ NEEDS AND BEHAVIOURS IN DIFFERENT LAND USE ENVIRONMENTS. JOURNAL OF TRANSPORT GEOGRAPHY, 26, 43-50.
18. KOMANOFF, C., ROELOFS, C., ORCUTT, J., KETCHAM, B. (1993). ENVIRONMENTAL BENEFITS OF BICYCLING AND WALKING IN THE UNITED STATES. TRANSPORTATION RESEARCH RECORD, 1405, 7-7.
19. LARCO, N., STEINER, B., STOCKARD, J., WEST, A. (2012). PEDESTRIAN-FRIENDLY ENVIRONMENTS AND ACTIVE TRAVEL FOR RESIDENTS OF MULTIFAMILY HOUSING: THE ROLE OF PREFERENCES AND PERCEPTIONS. ENVIRONMENT AND BEHAVIOR, 44 (3), 303-333.
20. LESLIE, E., COFFEE, N., FRANK, L., OWEN, N., BAUMAN, A., HUGO, G. (2007). WALKABILITY OF LOCAL COMMUNITIES: USING GEOGRAPHIC INFORMATION SYSTEMS TO OBJECTIVELY ASSESS RELEVANT ENVIRONMENTAL ATTRIBUTES. HEALTH PLACE, 13, 111-122.
21. LOO, B. P. VE CHOW, S. Y. (2006). SUSTAINABLE URBAN TRANSPORTATION: CONCEPTS, POLICIES, AND METHODOLOGIES. JOURNAL OF URBAN PLANNING AND DEVELOPMENT, 132 (2), 76-79.
22. MCKINNEY, W. (2010). DATA STRUCTURES FOR STATISTICAL COMPUTING IN PYTHON. IN PROCEEDINGS OF THE 9TH PYTHON IN SCIENCE CONFERENCE, 445, 51-56.
23. MEGAHED, N. A. VE GHONEIM, E. M. (2020). ANTIVIRUS-BUILT ENVIRONMENT: LESSONS LEARNED FROM COVID-19 PANDEMIC. SUSTAINABLE CITIES AND SOCIETY, 61, 102350.
24. MENDIBURU, F. D. (2017). PACKAGE ‘AGRICOLAE’. ERİŞİM ADRESİ: https://cran.r-project.org/web/packages/agricolae/agricolae.pdf.
25. MILLWARD, H., SPINNEY, J., SCOTT, D. (2013). ACTIVE-TRANSPORT WALKING BEHAVIOR: DESTINATIONS, DURATIONS, DISTANCES. JOURNAL OF TRANSPORT GEOGRAPHY, 28, 101-110.
26. MUSSELWHITE, C., AVINERI, E., SUSILO, Y. (2020). THE CORONAVIRUS DISEASE COVID-19 AND IMPLICATIONS FOR TRANSPORT AND HEALTH J. TRANSP. HEALTH, 16, 100853.
27. OGLE, D. H., WHEELER, P., DINNO, A. (2021). FSA: FISHERIES STOCK ANALYSIS. R PACKAGE VERSION 0.8. ERİŞİM ADRESİ: https://cran.r-project.org/web/packages/FSA/FSA.pdf.
28. PEDESTRIAN FIRST (2021). PEDESTRIAN FIRST-TOOLS FOR A WALKABLE CITY. ERİŞİM ADRESİ: https://pedestriansfirst.itdp.org.
29. PEIRAVIAN, F., DERRIBLE, S., IJAZ, F. (2014). DEVELOPMENT AND APPLICATION OF THE PEDESTRIAN ENVIRONMENT INDEX (PEI). JOURNAL OF TRANSPORT GEOGRAPHY, 39, 73-84.
30. QGIS.ORG (2021). QGIS GEOGRAPHIC INFORMATION SYSTEM. QGIS ASSOCIATION. ERİŞİM ADRESİ: http://www.qgis.org.
31. R CORE TEAM (2020). R: A LANGUAGE AND ENVIRONMENT FOR STATISTICAL COMPUTING. R FOUNDATION FOR STATISTICAL COMPUTING, VIENNA, AUSTRIA. ERİŞİM ADRESİ: https://www.r-project.org.
32. RABL, A. VE DE NAZELLE, A. (2012). BENEFITS OF SHIFT FROM CAR TO ACTIVE TRANSPORT. TRANSPORT POLICY, 19 (1), 121-131.
33. SAELENS, B. E. VE HANDY, S. L. (2008). BUILT ENVIRONMENT CORRELATES OF WALKING: A REVIEW. MEDICINE AND SCIENCE IN SPORTS AND EXERCISE, 40, 550-566.
34. SALIGAROS, N. (2010). P2P URBANISM. CREATIVE COMMONS ONLINE REPORT. ERİŞİM ADRESİ: http:// zeta.math.utsa.edu/~yxk833/p2purbanism.pdf.
35. SHANNON, C. (1948). A MATHEMATICAL THEORY OF COMMUNICATION. THE BELL SYSTEM TECHNICAL JOURNAL, 27, 379-423.
36. SPECK, J. (2013). WALKABLE CITY: HOW DOWNTOWN CAN SAVE AMERICA, ONE STEP AT A TIME; NORTH POINT PRESS: NEW YORK, NY: USA.
37. TÜİK (2021). TÜRKIYE İSTATİSTİK KURUMU. ERİŞİM ADRESİ: https://www.tuik.gov.tr.
38. WICKHAM, H., FRANÇOIS, R., HENRY, L., MÜLLER, K. (2018). DPLYR: A GRAMMAR OF DATA MANIPULATION. R PACKAGE VERSION 0.7.6. ERİŞİM ADRESİ: https://cran.r-project.org/package=dplyr.
39. YAMEQANI, A. S. VE ALESHEIKH, A. A. (2019). PREDICTING SUBJECTIVE MEASURES OF WALKABILITY INDEX FROM OBJECTIVE MEASURES USING ARTIFICIAL NEURAL NETWORKS. SUSTAINABLE CITIES AND SOCIETY, 48, 101560.
40. ZECCA, C., GAGLIONE, F., LAING, R., GARGIULO, C. (2020). PEDESTRIAN ROUTES AND ACCESSIBILITY TO URBAN SERVICES: AN URBAN RHYTHMIC ANALYSIS ON PEOPLE'S BEHAVIOUR BEFORE AND DURING THE COVID-19. TEMA: JOURNAL OF LAND USE, MOBILITY AND ENVIRONMENT, 13 (2), 241-256.