This repository/application was created for a Uni Project, called "Remote Sensing Applications" of the Department of Rural and Surveying Engineering and Geoinformatics Enginnering of the National Technical University of Athens.
The aim of this project is, how green spaces are affected by socio-economic factors and land use, with a focus on the case of New York City. For better and more accurate results, two epochs have been investigated, 2011 and 2019.
The application includes a study implementation section with subsections on Luxury Effect, Land Use Effect, and Legacy Effect, and a bibliography.
The study implementation section includes graphs, charts, and maps to support the analysis. The application concludes that there is a strong correlation between green spaces and median income,
and suggests further analysis with additional indicators and dynamic maps for more representative results.
The project is made of four Pages. The 1st is about the Datasets that are being used in this project. The 2nd examines how Luxury Effect can affect Urban Green Spaces (UGS) and
the 3rd and 4th Pages examine how Land Use Effect and Legacy Effect can affect UGS, respectively.
The Socioeconomic Factors which will be examined are :
- Mean Income (Luxury Effect)
- Historic Districts (Legacy Effect)
- and also - Land Use (Land Use Effect)
Green spaces are an integral part of urban fabric, and their relationship should be characterized as inseparable. Green spaces refer to areas where vegetation is present, including parks and squares, generally providing spaces for residents to freely walk and connect with nature. According to Angelo Siolas (2015), green spaces, also known as "Urban Green Spaces", are the points where the city meets nature. Green spaces should be an integral part of every neighborhood, and their distribution should be uniform, with sizes sufficient to meet the needs of all residents.
Numerous studies have shown that green spaces are influenced by various socio-economic factors. Hai-Li Zhang et al. (2021) studied how green spaces depend on property values, the history of the area, land uses, and other factors in two different periods. Mendel Giezen (2018) used satellite images to detect a reduction in green spaces over time due to increased construction. Therefore,the purpose of this study is to identify the correlation between certain socio-economic factors and green spaces in two time periods at the level of USA Tract.
It is important to note that in this study, green spaces refer specifically to areas with vegetation. Green spaces in cities offer various benefits that make the relationship between nature and the urban environment inseparable. They can act as natural carbon sinks, absorbing carbon dioxide emissions from vehicles. They also reduce the likelihood of flooding by absorbing atmospheric precipitation and help control soil erosion. Additionally, green spaces function as noise reduction tools in cities, creating a more pleasant and sustainable living environment for residents. Furthermore, green spaces can be considered as temperature regulators, mitigating the heat island effect predominantly found in urban areas. Lastly, they undoubtedly enhance the aesthetics of the city, providing a more visually appealing environment.
This repository/application has been made for uploading it to Streamlit. You can visit the application following this link https://remote20sensing20app20-20introductionpy-myvoxzalycr9w5r9vsivov.streamlit.app/ Streamtit is a cloud-based platform fro making apps, using Python and share them with the world.
A big thanks to my supervisor Konstantinos Karantzalos and Alekos Falagas for their valuable advices and guidance.
Hai-Li Zhang a, J. P.-L.-F. (2021). Wealth and land use drive the distribution of urban green space in the tropical coastal city of Haikou, China. ELSEVIER.
Mendel Giezen, S. B. (2018). Using Remote Sensing to Analyse Net Land-Use Change from Conflicting Sustainability Policies: ISPRS
Άγγελος Σιόλας, Α. Β. (2015). Μέθοδοι, Εφαρμογές και Εργαλεία Πολεοδομικό Μετασχηματισμού: Εργαλεία και Τεχνικές. ΑΘΗΝΑ: Kallipos
Richards, J. A. (2012). Remote Sensing Digital Image Analysis - An introduction. Canbera, Australia: Spring
Sofia F. Francoa, J. L. (2018). Measurement and valuation of urban greenness: Remote sensing and hedonic applications to Lisbon, Portugal. ELSEVIER
Wei Lia, J.-D. M. (2014). A comparison of the economic benefits of urban green spaces estimated with NDVI and with high-resolution land cover data. ELSEVIER
Wu, Q. (2020). geemap: A Python package for interactive mapping with. Geograpgy. The Journal of Open Source Software.
Yingdan Mei, X. Z. (2018). apitalization of Urban Green Vegetation in a Housing Market with Poor Environmental Quality:. Journal of Urban Planning and Development.
Zhonglin Ji, Y. P. (2022). Prediction of Corn Yield in the USA Corn Belt Using Satellite Data and Machine Learning: From an Evapotranspiration Perspective . Basel, Switzerland.: MDPI
Γεωργούλη, Κ. (2015). Τεχνητή Νοημοσύνη - Μια Εισαγωγική Προσέγγιση. Αθήνα: Kallipos
Δημήτρης, Α. (1998). Ψηφιακή Τηλεπισκόπηση. Αθήνα
Ιωάννης, Ψ. (2021). Χαρτογράφηση καμένων εκτάσεων απο διαχρονικά δορυφορικά δεδομένα Sentinel-2 στο περιβάλλον του Google Earth Engine. Αθήνα: ΕΜΠ
Κωνσταντίνος Γ. Περάκης, Ι. Ν. (2015). Η Τηλεπισκόπηση σε 13 ενότητες. Kallipos
Σπύρος Φουντάς, Θ. Γ. (2015). Γεωργία Ακριβείας. Kallipos
Data Folder refers to the Folder which has the data that being used for the project. Maps Folder refers to the Folder with all the maps in the best resolution. pages Folder refers to all pages that assemble the application.