Context

This dataset is the result of my study on web-scraping of English Wikipedia in R and my tests on regression and classification modelization in R.

Content

The content is create by reading the appropriate articles in English Wikipedia about Italian cities: I did'nt run NPL analisys but only the table with the data and I ranked every city from 0 to N in every aspect. About the values, 0 means "*the city is not ranked in this aspect*" and N means "*the city is at first place, in descending order of importance, in this aspect* ". If there's no ranking in a particular aspect (for example, the only existence of the airports/harbours with no additional data about the traffic or the size), then 0 means "*no existence*" and N means "*there are N airports/harbours*". The only not-numeric column is the column with the name of the cities in English form, except some exceptions (for example, "*Bra (CN)* " because of simplicity.

Acknowledgements

I acknowledge the Wikimedia Foundation for his work, his mission and to make available the cover image of this dataset, (please read the article "The Ideal city (painting)") . I acknowledge too StackOverflow and Cross-Validated to be the most important focus of technical knowledge in the world, all the people in Kaggle for the suggestions.

Inspiration

As a beginner in data analisys and modelization (Ok, I passed the exam of statistics in Politecnico di Milano (Italy), but there are more than 10 years that I don't work in this topic and my memory is getting old ^_^) I worked more on data clean, dataset building and building the simplest modelization.

You can use this datase to realize which city is good to live or to expand this to add some other data from Wikipedia (not only reading the tables but too to read the text adn extrapolate the data from the meaningless text.)

This dataset provides a snapshot of Airbnb listings across major Italian cities and regions, offering valuable insights into the short-term rental market in Italy. Whether you're interested in pricing trends, regional variations, or the impact of seasonality, this dataset has something for you.

Data refer to a period between September 2023 and September 2024

Key Features:

• City-level data: Explore listings in popular cities like Florence, Milan, Naples, Rome, and Venice.
• Regional insights: Analyze trends across broader regions including Puglia, Sicily, and Trentino.
• Comprehensive metrics: Data includes pricing, review scores, host details, and more.
• Seasonal analysis: Data spans different periods, allowing for comparisons across seasons.

Data Dictionary:

• id: Unique identifier for each listing.
• number_of_reviews_ltm: Number of reviews received in the last twelve months.
• date_of_scraping: Date the data was scraped.
• host_since: Date the host joined Airbnb.
• host_is_superhost: Whether the host is a superhost (t/f).
• host_total_listings_count: Total number of listings the host has.
• neighbourhood: Neighborhood where the listing is located.
• latitude: Latitude coordinate of the listing.
• longitude: Longitude coordinate of the listing.
• room_type: Type of room (e.g., entire home/apt, private room).
• accommodates: Number of guests the listing can accommodate.
• price: Price per night (in local currency).
• number_of_reviews: Total number of reviews.
• review_scores_rating: Overall rating of the listing.
• review_scores_accuracy: Accuracy rating.
• review_scores_cleanliness: Cleanliness rating.
• review_scores_checkin: Check-in rating.
• review_scores_communication: Communication rating.
• review_scores_location: Location rating.
• review_scores_value: Value rating.
• reviews_per_month: Number of reviews per month.
• place: City or region where the listing is located.
• period: Time period when the data was scraped (e.g., Early Winter).

For visualization reason it is also provide a csv with all city neighbourhoods and the relative geojson.

I also added datasets that group listings according to period and neighbourhood/cities, quantitative features were been aggregate according to median and MAD, qualitative according to mode and Shannon's entropy.

Disclaimer:

This dataset is intended for informational and research purposes only. It is not affiliated with Airbnb or any other organization.

IT: Population in Largest City data was reported at 3,755,830.000 Person in 2017. This records an increase from the previous number of 3,737,750.000 Person for 2016. IT: Population in Largest City data is updated yearly, averaging 3,416,411.000 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 3,755,830.000 Person in 2017 and a record low of 2,455,581.000 Person in 1960. IT: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Italy – Table IT.World Bank: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;

Our analysis focuses on seven metropolitan cities across Italy. Here, we report the number of spatial cells of the mobile phone network and the population (in thousands) of each of these cities split across 6 age groups. Population data is retrieved from the 2011 Italian census and comprises all the census sections within the phone cells considered for each city. It is important to highlight that in each cell of the network there can be several mobile phone users, thus we cannot estimate the fraction of the census population included in our data set. Note that the age groups provided by the Italian census do not perfectly match those of the Telecom Italia dataset.

Data come from the follow-up of the main study “Inclusive ageing in place” (IN-AGE), regarding frail older people aged 65 years and over (males and females). The main study was a cross-sectional qualitative survey carried out in 2019 by face-to-face interviews to frail older people without cognitive impairment, and living at home, alone or with a private personal care assistant (PCA), in three Italian Regions: Lombardy (North), Marche (Centre) and Calabria (South). Both peripheral/degraded areas of urban sites and fragile rural locations were included, with regard to social and material vulnerability aspects (e.g. high presence of frail older people living alone, poor provision of services). The follow up was carried out in July-September 2020, and it was aimed to explore and compare effects of lockdown, due to the first wave of the COVID-19 pandemic (February-May 2020), on frail older people living alone at home in Brescia and Ancona, two urban cities located respectively in the Northern and Central Italy. This country was the Western epicenter of the first wave of the pandemic, that differently affected the two cities as for infections, with a more severe impact on the former one. The dataset (41 respondents, vs 48 in the main survey) regards available care arrangements, both informal (family members) and formal (public services), to support the performing of daily living activities (ADLs and IADLs), especially in the presence of functional limitations. The use of/access to health services (General Practitioner, Medical Specialist and other health services) was also explored. A semi-structured interview was administered by telephone due to social distancing imposed by the pandemic. Participants were asked to report possible worsening/improving (or no change/not affected) due to the pandemic. A simple quantitative analysis (frequency distribution/bivariate analysis) of closed responses was carried out by using Microsoft Excel software 2019. Analyses suggested how the lockdown and social distancing overall negatively impacted on frail older people living alone, to a different extent in Ancona and Brescia, with a better resilience of home care services in Brescia, and a greater support from the family in Ancona, where however major problems in accessing health services also emerged. Even though the study was exploratory only, also due to the small sample, that cannot be considered as representative of the target population, findings suggested that enhancing home care services, and supporting older people in accessing health services, could allow ageing in place, especially in emergency time. The dataset is provided in open format (xlsx) and includes the following: a “numeric” dataset regarding the unlabelled dimensions used for statistics elaboration; a codebook with both the complete variables list and variables labels we used. The dataset was produced within the framework of the IN-AGE project, funded by Fondazione Cariplo, Grant N. 2017-0941.

Italian Negation Constructions - Tweets

Exploring Language Variation Across 10 Cities

By [source]

About this dataset

This dataset, the Twitter Italian Negation (TIN) Corpus, provides an interesting glimpse into language change in Romance languages with the emergence of non-standard uses of negations. This collection contains 10,000 tweets from ten different cities -Milan, Rome, Naples, Palermo, Bologna, Turin, Florence Cagliari Genoa and New York City -each collected in August 2019. The data includes tokenized text and frequency measures for each tweet as well as a city column so users can explore regional differences. With this resource users can uncover how the language of these cities is changing over time or even how language usage between neighboring countries or states may differ. Get ready to dive deep into the fascinating shifts that occur between spoken and written languages!

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How to use the dataset

This dataset contains 10,000 tweets in Italian gathered from ten different cities between August and December 2019. This collection of tweets provides an interesting insight into the language change phenomena in Romance languages, specifically with regard to non-standard uses of negations.

The dataset is composed of nine columns: token, absolute frequency, relative frequency, variation, and city from which the tweet originated. Each row represents a single token in a particular tweet: each tweet can contain more than one token.

By using this dataset you can analyze and compare patterns of usage across different cities or even within a specific city. You can also compare variations within tokens between different cities to understand how certain constructions are used differently across regions or dialects. Additionally you could use this data to examine trends in literary works such as poetry by looking at the most commonly used words and phrases over time.

To use the data effectively, it is important first to understand what each column represents:

• Tok (Tokenized text): This is text that has been broken down into individual words or tokens representing all of the words found in a particular tweet including punctuation marks like commas or exclamation points;

• Abs (Absolute Frequency): This is the total number of times that a particular token appears within all tweets;

• Rel (Relative Frequency): This is calculated by calculating how many times a particular token appears compared to other tokens;

• Var (Variation): This indicates whether there have been any alterations made compared to standard usage such as “has” being replaced with “haz”;

• City: The originator's city corresponds with each tweet guiding analysis on usage differences among locales for example “Milan” or “Genua” but also generalized larger geographic areas such as “Italy” versus other countries like “United States.

  Using these numeric values alongside thematic exploration allows for understanding not only usages but trends across different geographic populations relative representations both locally and globally provided by Twitter users regarding issues related language use especially non-standard dialectical contructs throughout Italy

Research Ideas

• Studying the regional variation of Italian negation constructions by comparing the frequency and variation between cities.
• Investigating language change over time by tracking changes in relative and absolute frequencies of negation constructions across tweets.
• Exploring how different socio-economic contexts or trends such as news, fashion, sports impacted the evolution of language use in tweets in each city

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: interessa+word1.csv | Column name | Description | |:--------------|:------------------------------------------------------| | tok | Tokenized text of the tweet. (String) | | abs | Absolute frequency of a token in the...

The seismic data acquisition shown in the repository was done in the frame of the HISOPE project, which is intended to provide critical marine geological and archaeological constraints, in support of two currently funded coastal research projects: the IDEX Lyon project Thalassocracies without a port, myth or reality? (2020-2022) and the LabEx-IMU (Institut des Mondes Urbains) URBO project (2020-2023). These projects are carried by the Archéorient laboratory (UMR 5133 CNRS - Université Lyon 2) as part of a scientific consortium associating several other French and Italian laboratories and institutions: UMR 5189 HiSoMA, UMR 5600 EVS (ENS Lyon - Univ. Lyon 1 ), the Italian archaeological superintendencies of Lazio and Tuscany, the Museo civico del mare e della navigazione antica de Santa Severa, the University of Florence, the University of Roma-La Sapienza, the CNR, the WWF, the Municipality of Orbetello. The main objective is to discover ancient port cities along the seafront of the Tyrrhenian Sea. If the Roman ports, dating from the 4th century BC were discovered, the Etruscan ports (dating from the 10th to the 5th century BC) remain untraceable. The Etruscans are one of the only Mediterranean thalassocracies (societies based on maritime commercial and military power) whose archaeologists have never found the ports. The second objective is to determine the environmental characteristics of the sites chosen for the foundation of the Etruscan ports. The study of the evolution of these environments will also make it possible to understand the factors (environmental, political, economic) which led to their disappearance, and to the ex nihilo creation of new port sites in Hellenistic and Roman times. These projects are based on a resolutely interdisciplinary approach and bring together historians, archaeologists, geophysicists, geologists and geoarchaeologists. The main goals of the field campaigns carried out in this context are 1 ° to identify the location of the port basins (underground or at sea), and 2 ° to sample the study sites, in order to characterize their geological environment. For these reasons, the field missions combine terrestrial geophysical prospecting as well as terrestrial coring campaigns and lagoon coring. The main archaeological targets of the 2019 and 2020 campaigns were two Etruscan coastal cities: Pyrgi (Lazio) and Orbetello (Tuscany). In both cases, land surveys (geophysical and coring) have shown that, contrary to the dominant hypotheses, the port basins are not currently located on land. The present HISOPE - IFREMER project therefore aims to use sediment imagery to determine if they are present at sea, and to characterize the evolution of the coastline that has led to this present state. The reasons are very different in each case. In Pyrgi, the coastline has receded sharply since Etruscan times, due to rapid beach erosion. This decline sharply accelerated in the 20th century, threatening this important archaeological site. In Orbetello, the lagoon, in which the ancient city was settled, seems to have become a more restrictive environment over time; even becoming a source of countless environmental problems, against which the inhabitants still struggle today. The present HISOPE - IFREMER project aims to acquire high frequency seismic lines on these two sites. At Pyrgi, in an open sandy marine environment, sediment imagery will help determine the position of ancient shorelines, and determine the ancient positioning of archaeological remains discovered 350 m offshore, in October 2020. In Orbetello, set of measurements, in front of a tombolo that closes the lagoon, aims to document the source of a high-energy event that affected the lagoon after Roman times. In addition to providing answers to archaeologists, these measurements will provide fundamental information on the dynamics of these environments, making it possible to improve the fight against coastal erosion in Pyrgi, and the fight against the progressive confinement of the lagoon in Orbetello. Seismic data were acquired with high resolution 3,5 kHz Ixblue Subbottom Profiler, customized by Ifremer institute to optimize the data acquisition in shallow water; the sonar was hull mounted on board of the Haliotis research vessel; data post processing was performed by QC_Subop software (developed by Ifremer /REM/GEOOCEAN/ANTIPOD laboratories), and Geosuite software (license to CNR of Oristano). The flow processing was based on signal normalization, basic band pass filtering, and gain adjustment by Liner Gain (LG) or Time Varying Gain (TVG), depending on acquisition depth and sea bottom reflectivity . In the repository are provided navigation data (shape and Kmz file) showing navigation points; let's find in the attributes the file and line name, the progressive fix number of each profile (related to the acquisition shot) and the acquisition date (Year, month, day, hour. Minute, second); processed profiles are shown in jpg files with reference to line name, fix/shot (for horizontal scaling) and vertical scale in milliseconds; a conversion table from ms to meters is provided in the repository, assuming 1500 as sound velocity speed in the water.

Dataset supporting publication: “Data collected by coupling fix and wearable sensors for addressing urban microclimate variability in an historical Italian city” (publication available for download: GEOFIT Zenodo)

Datasets resulting from monitoring activities of Sant'Apollinare systems and climatic parameters inside and outside the building (post-intervention monitoring).

The article presents the data collected through an extensive research work conducted in a historic hilly town in central Italy during the period 2016-2017. Data concern two different datasets: long-term hygrothermal histories collected in two specific positions of the town object of the research, and three environmental transects collected following on foot the same designed path at three different time of the same day, i.e. during a heat wave event in summer. The short-term monitoring campaign is carried out by means of an innovative wearable weather station specifically developed by the authors and settled upon a bike helmet. Data provided within the short-term monitoring campaign are analysed by computing the apparent temperature, a direct indicator of human thermal comfort in the outdoors. All provided environmental data are geo-referenced. These data are used in order to examine the intra-urban microclimate variability. Outcomes from both long- and short-term monitoring campaigns allow to confirm the existing correlation between the urban forms and functionalities and the corresponding local microclimate conditions, also generated by anthropogenic actions. In detail, higher fractions of built surfaces are associated to generally higher temperatures as emerges by comparing the two long-term air temperature data series, i.e. temperature collected at point 1 is higher than temperature collated at point 2 for the 75% of the monitored period with an average of þ2.8 [1]C. Furthermore, gathered environmental transects demonstrate the high variability of the main environmental parameters below the Urban Canopy. Diversification of the urban thermal behaviour leads to a computed apparent temperature range in between 33.2 [1]C and 46.7 [1]C at 2 p.m. along the monitoring path. Reuse of these data may be helpful for further investigating interesting correlations among urban configuration, anthropogenic actions and microclimate variables affecting outdoor comfort. Additionally, the proposed dataset may be compared to other similar datasets collected in other urban contexts around the world. Finally, it can be compared to other monitoring methodologies such as weather stations and satellite measurements available in the location at the same time.

The Pavia University dataset is a hyperspectral image dataset which gathered by a sensor known as the reflective optics system imaging spectrometer (ROSIS-3) over the city of Pavia, Italy. The image consists of 610×340 pixels with 115 spectral bands. The image is divided into 9 classes with a total of 42,776 labelled samples, including the asphalt, meadows, gravel, trees, metal sheet, bare soil, bitumen, brick, and shadow.

This record contains raw data related to article “Incidence rates of hospitalization and death from COVID-19 in patients with psoriasis receiving biological treatment: a Northern Italy experience"

Introduction: Whether biologic therapies enhance the risk of coronavirus 2019 (COVID-19) or affect the disease outcome in patients with chronic plaque psoriasis remains to be ascertained.

Objective: We sought to investigate the incidence of hospitalization and death for COVID-19 in a large sample of patients with plaque psoriasis receiving biologic therapies compared with the general population.

Methods: This is a retrospective multicenter cohort study including patients with chronic plaque psoriasis (n = 6501) being treated with biologic therapy and regularly followed up at the divisions of dermatology of several main hospitals in the Northern Italian cities of Verona, Padua, Vicenza, Modena, Bologna, Piacenza, Turin, and Milan. Incidence rates of hospitalization and death per 10,000 person-months with exact mid-p 95% CIs and standardized incidence ratios were estimated in the patients with psoriasis and compared with those in the general population in the same geographic areas.

Results: The incidence rate of hospitalization for COVID-19 was 11.7 (95% CI, 7.2-18.1) per 10,000 person-months in patients with psoriasis and 14.4 (95% CI, 14.3-14.5) in the general population; the incidence rate of death from COVID-19 was 1.3 (95% CI, 0.2-4.3) and 4.7 (95% CI, 4.6-4.7) in patients with psoriasis and the general population, respectively. The standardized incidence ratio of hospitalization and death in patients with psoriasis compared with those in the general population was 0.94 (95% CI, 0.57-1.45; P = .82) and 0.42 (95% CI, 0.07-1.38; P = .19), respectively.

Conclusions: Our data did not show any adverse impact of biologics on COVID-19 outcome in patients with psoriasis. We would not advise biologic discontinuation in patients on treatment since more than 6 months and not infected with severe acute respiratory syndrome coronavirus 2 to prevent hospitalization and death from COVID-19.