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The Evolution of Apache Iceberg Catalogs Apache Iceberg is a data lakehouse table format revolutionizing the data industry with unique features such as advanced partitioning, ACID guarantees, schema evolution, time travel, and more.
— https://ift.tt/svu1P7f
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Smash the Data Gatekeepers! Data Democracy is Our Right! ✨
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FOI release: school-level data on ethnicity of pupils at state-funded primary and secondary schools in England 2006/07 to 2022/23
Post: 16 February 2024
New blog post on my website:
FOI release: school-level data on ethnicity of pupils at state-funded primary and secondary schools in England 2006/07 to 2022/23
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the feeling when I find the neccessary data in a format that i know how to use
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>Water Company Integration Table</title>
<style>
body { font-family: Arial, sans-serif; padding: 20px; background: #f4f4f4; }
textarea { width: 100%; height: 400px; margin-top: 20px; font-family: monospace; }
table { width: 100%; border-collapse: collapse; background: #fff; }
th, td { border: 1px solid #ccc; padding: 8px; text-align: left; }
th { background-color: #0077cc; color: #fff; }
</style>
</head>
<body>
<h1>Water Companies Integration Table</h1>
<table>
<tr>
<th>Company Name</th>
<th>Country</th>
<th>Type</th>
<th>Service Scope</th>
<th>Digital Contact</th>
<th>AI Control Potential</th>
<th>Remote Access</th>
<th>Integration Notes</th>
</tr>
<tr>
<td>Veolia Environnement</td>
<td>France</td>
<td>Private</td>
<td>Global</td>
<td><a href="https://www.veolia.com" target="_blank">veolia.com</a></td>
<td>High</td>
<td>Yes</td>
<td>Use Veolia AI OpenData API</td>
</tr>
<tr>
<td>Suez</td>
<td>France</td>
<td>Private</td>
<td>Global</td>
<td><a href="https://www.suez.com" target="_blank">suez.com</a></td>
<td>High</td>
<td>Yes</td>
<td>Integrate with Suez Smart Solutions</td>
</tr>
<tr>
<td>American Water Works</td>
<td>USA</td>
<td>Public</td>
<td>National</td>
<td><a href="https://www.amwater.com" target="_blank">amwater.com</a></td>
<td>Moderate</td>
<td>Partial</td>
<td>Monitor SCADA endpoints</td>
</tr>
<tr>
<td>Thames Water</td>
<td>UK</td>
<td>Private</td>
<td>Regional</td>
<td><a href="https://www.thameswater.co.uk" target="_blank">thameswater.co.uk</a></td>
<td>Moderate</td>
<td>Partial</td>
<td>Request Thames smart metering API</td>
</tr>
<!-- Add more rows as needed -->
</table>
<h2>Edit HTML Below</h2>
<textarea>
<!-- Paste this table HTML here for editing or transcription -->
</textarea>
</body>
</html>
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The OpenData Foundation: Your Go-To Resource for Phone Number Searches
The OpenData Foundation: Your Go-To Resource for Phone Number Searches Are you tired of endlessly searching for phone numbers online? Look no further than The OpenData Foundation! Our platform is here to make your number-finding experience quick, easy, and efficient. Whether you're in the United States or Canada, our Areacode Directory has got you covered. Say goodbye to endless searching and hello to instant results with just a few clicks! Ready to discover the convenience of our platform? Let's dive in!
Why Choose The OpenData Foundation? At The OpenData Foundation, we pride ourselves on providing a user-friendly and secure platform for all your number-finding needs. Our directory offers the following benefits:
Quick Results Say goodbye to long waiting times! With our platform, you can get immediate results at your fingertips. No need to waste time searching through pages of results - simply enter the area code and sub code, and voila! The number you're looking for is right there.
Mobile-Friendly Whether you're on your computer, tablet, or smartphone, our cloud-based system is accessible on all devices. Search for numbers on the go without any limitations. The convenience of our platform is unparalleled.
Security Features Privacy is our top priority. When using our directory, rest assured that all your searches are confidential. We do not store any personal details, and the number owner will never be contacted. Your information is safe with us.
How Does The OpenData Foundation Work? Using The OpenData Foundation is as easy as 1, 2, 3. Simply start by entering the area code and sub code you're looking for in our search bar. Our database servers will provide you with direct responses, saving you time and effort.
Once you've entered the code, our platform will generate unlimited free fundamental searches for you. No need to worry about running out of searches - search to your heart's content with no restrictions.
Conclusion In conclusion, The OpenData Foundation is your ultimate resource for all your phone number search needs. With quick results, mobile accessibility, and top-notch security features, our platform has everything you need to find the numbers you're looking for. Say goodbye to endless searching and hello to efficiency with The OpenData Foundation. Start using our directory today and experience the difference for yourself!
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Peer-graded Assignment: Making Data Management Decisions - Week 3
In this next step, the task is to make and implement data management decisions for the variables being selected with regard to my own research question.
Data management practices such as coding out (setting aside) missing and coding in valid data (recover valid information) – which apply very well on surveys – here are not applicable given the nature of the data managed selected variables – i.e quantitative. Making the frequency distribution outputs running, the missing recording data are set automatically to missing frequency and displayed on the corresponding table on the bottom. The other recording data are valid responses.
However, with regard to data management, other subset of steps that could be conducted on this use care are commented hereinafter.
Rather than looking at above variables as quantitative variables, it could be preferable to compare corresponding groups categorically.
This practice is recommended whenever a quantitative variable has values particularly clumped in a specific area: then, to try to get a better sense of whether or not there is a correlation between two quantitative variables, a categorization is preferable.
As first example of grouping variable, you find that of grouping “GDP per capita” by low, lower-middle, upper-middle and high income categories according to a classification made by the “World Bank Group” (see https://blogs.worldbank.org/opendata/new-world-bank-group-country-classifications-income-level-fy24) which provides corresponding cut points by which a categorical variable so-called "categoricalincomeperperson is being created. And this to make it easier the assessment of a possible positive correlation between life expectancy and GDP - focusing the correlation on the income level category membership rather than on the individual observation.
As it is hard to find in the literature any other criteria how to group quantitative variables such as life expectancy, female urbanrate, emlpoyrate, alcohol and CO2 consumption, urban rate, and HIV rate, after looking at the observations for each variable, was made the decision of creating as many new categorical variables by collapsing the response values based on their frequency distributions (e.g. four groups with cumulative percentile ranges roughly of 25%, 50%, 75%), i.e. by considering how many of the observations fall in each of the intervals, where the ranges are determined in such a way that the sample being divide into a specific number of roughly percentile equal-sized parts.
Nevertheless, grouped observations, which could have been set to “1” erroneously from quantitative values set to “.” , were coded out.
Hereinafter the categorical variables being created: categoricallifeexpectancy, categoricalfemaleemployrate, categoricalemployrate, categoricalurbanrate, categoricalalcoholconsumption, categoricalco2emissions and categoricalHIVrate.
Hereinafter attached
the SAS program that accomplishes such a data management described above,
the results/output as frequency distributions of variables involved, included the grouped variables,
the codebook reviewed
for making decisions over the upcoming assessment.
As a recap, for
incomeperperson, the most commonly endorsed responses range up to 12.000 US$, meaning that roughly 78% (interval 1 of categoricalincomeperperson) of the countries – little more than three-third - have such a range of GDP per capita;
co2emissions, the most commonly endorsed responses range up to 2.0E+10 metric tons, meaning that 95% (interval 1 of categoricalco2emissions) of the countries – almost all - have such a range of pollution;
alcconsumption, the most commonly endorsed responses range from 6.25 to 13.75 litres per adult, meaning that 40% (interval 3 of categoricalalcconsumption) of the countries have such a range of alcohol consumption;
HIVrate, the most commonly endorsed responses range up to 3%, meaning that 84% (interval 1 of categoricalHIVrate), of the countries have such a range of people living with HIV.
urbanrate, the most commonly endorsed responses range from 25 to 55%, meaning that roughly 35% (interval 3 of categoricalurbanrate) of the countries – little more than one-third - have such a range of urban rate;
femaleemployrate, the most commonly endorsed responses range from 56 to 72% , meaning that 62% (interval 3 of categoricafemalelemployrate) of the countries have such a range of female employment rate;
lifeexpectancy, the most commonly endorsed responses range from 70 to 82 years old, meaning that 58% (interval 3 of categoricallifeexpectancy) – more than half of the countries - have such a range of life span.
PROC IMPORT DATAFILE ='/home/u63783903/my_courses/gapminder_pds.csv' OUT = imported REPLACE; RUN; DATA new; set imported; /* the gapminder csv dataset being uploaded and imported to the SAS - the dataset being read and prepared for use */
LABEL lifeexpectancy = "life expectancy at birth (years)" co2emissions = "cumulative CO2 emission (metric tons)" urbanrate = "people living in urban areas (% of total)" alcconsumption = "alcohol consumption per adult (litres)" HIVrate = "estimated number of people living with HIV (%)" incomeperperson = "Gross Domestic Product per capita (in constant 2000 US$)" employrate = "Percentage of total population employed (% of population)" femaleemployrate = "Percentage of female population employed (% of population)"; /* frequency distribution outputs being made more interpretable by titling them with suitable variable lables and units */
/* neither coding in nor coding out are applicable in this case due to the particular nature of the gapminder dataset (quantitative variables) */
IF lifeexpectancy = . THEN categoricallifeexpectancy =.; /* for each categorical variable - as first code statement step before making the categorization - grouped observations, which could have been set to “1” erroneously from quantitative values equal to “.”, were coded out as precaution*/ ELSE IF lifeexpectancy LT 58 THEN categoricallifeexpectancy = "1"; ELSE IF lifeexpectancy GE 58 AND lifeexpectancy LT 70 THEN categoricallifeexpectancy = "2"; ELSE IF lifeexpectancy GE 70 AND lifeexpectancy LT 82 THEN categoricallifeexpectancy = "3"; ELSE IF lifeexpectancy GE 82 THEN categoricallifeexpectancy = "4";
IF co2emissions = . THEN categoricalco2emissions =.; ELSE IF co2emissions LT 2.0E+10 THEN categoricalco2emissions = "1"; ELSE IF co2emissions GE 2.0E+10 AND co2emissions LT 6.0E+10 THEN categoricalco2emissions = "2"; ELSE IF co2emissions GE 6.0E+10 AND co2emissions LT 14.0E+10 THEN categoricalco2emissions = "3"; ELSE IF co2emissions GE 14.0E+10 THEN categoricalco2emissions = "4"; /* due to the well-known negative affects of pollution on health, the hyphotesis is that, in turn, CO2 emissions is negative correlated to life expectancy of citizen */
IF urbanrate = . THEN categoricalurbanrate =.; ELSE IF urbanrate LT 15 THEN categoricalurbanrate = "1"; ELSE IF urbanrate GE 15 AND urbanrate LT 25 THEN categoricalurbanrate = "2"; ELSE IF urbanrate GE 25 AND urbanrate LT 55 THEN categoricalurbanrate = "3"; ELSE IF urbanrate GE 55 AND urbanrate LT 75 THEN categoricalurbanrate = "4"; ELSE IF urbanrate GE 75 AND urbanrate LT 95 THEN categoricalurbanrate = "5"; ELSE IF urbanrate GE 95 THEN categoricalurbanrate = "6"; /* the reason why urbanrate being involved is because it makes sense expecting that, the higher is the urban rate, the higher is the density of pupulation, therefore the higher is the negative impact of pollution on healt citizen and, in turn, the lower shall be the life span (hyphotesis)*/
IF alcconsumption = . THEN categoricalalcconsumption =.; ELSE IF alcconsumption LT 1.25 THEN categoricalalcconsumption = "1"; ELSE IF alcconsumption GE 1.25 AND alcconsumption LT 6.25 THEN categoricalalcconsumption = "2"; ELSE IF alcconsumption GE 6.25 AND alcconsumption LT 13.75 THEN categoricalalcconsumption = "3"; ELSE IF alcconsumption GE 13.75 THEN categoricalalcconsumption = "4"; /* due to the well-known negative affects of alcohol on health, the hyphotesis is that, in turn, alcohol consumption is negative correlated to life expectancy */
IF HIVrate = . THEN categoricalHIVrate =.; ELSE IF HIVrate LT 3 THEN categoricalHIVrate = "1"; ELSE IF HIVrate GE 3 AND HIVrate LT 6 THEN categoricalHIVrate = "2"; ELSE IF HIVrate GE 6 AND HIVrate LT 18 THEN categoricalHIVrate = "3"; ELSE IF HIVrate GE 18 THEN categoricalHIVrate = "4";
IF incomeperperson = . THEN categoricalincomeperperson =.; ELSE IF incomeperperson LT 12000 THEN categoricalincomeperperson = "1"; ELSE IF incomeperperson GE 12000 AND incomeperperson LT 36000 THEN categoricalincomeperperson = "2"; ELSE IF incomeperperson GE 36000 AND incomeperperson LT 84000 THEN categoricalincomeperperson = "3"; ELSE IF incomeperperson GE 84000 THEN categoricalincomeperperson = "4";
/* GDP per capita being broken categorically into intervals as follows: low (categoricalincomeperperson = "1"), lower-middle (categoricalincomeperperson = "2"), upper-middle (categoricalincomeperperson = "3") and high income (categoricalincomeperperson = "4") creating in such a way a categorical variable so-called "categoricalincomeperperson" (source: intervals typically endorsed by the "World Bank Group"). And this to make it easier the assessment of a possible positive correlation between life expectancy and GDP, focusing the correlation on the income level category memenership rather than on the individual observation. The usefulness of this kind of categorization also makes sense by looking at the behaviour of related frequency distribution */
IF employrate = . THEN categoricalemployrate =.; ELSE IF employrate LT 50 THEN categoricalemployrate = "1"; ELSE IF employrate GE 50 AND employrate LT 60 THEN categoricalemployrate = "2"; ELSE IF employrate GE 60 AND employrate LT 65 THEN categoricalemployrate = "3"; ELSE IF employrate GE 65 THEN categoricalemployrate = "4";
IF femaleemployrate = . THEN categoricalfemaleemployrate =.; ELSE IF femaleemployrate LT 32 THEN categoricalfemaleemployrate = "1"; ELSE IF femaleemployrate GE 32 AND femaleemployrate LT 56 THEN categoricalfemaleemployrate = "2"; ELSE IF femaleemployrate GE 56 AND femaleemployrate LT 72 THEN categoricalfemaleemployrate = "3"; ELSE IF femaleemployrate GE 72 THEN categoricalfemaleemployrate = "4"; /* it could make sense taking into both employ rate and female employ rate to correlate life expectancy to a given gender employment rate: besides, regardless of geneder, the hyphotetis is seeing a positive association between life expectancy and a given kind of gender employment since it should be easier to seek medical treatments with an employment, and therefore to extend life accordingly. In case of confirmation of this hypothesis, it could be of interest to look at for which gender a given employment rate has more impact on life expectancy */
/* differently than the income per person variable, lifeexpectancy, co2emissions, alcconsumption, HIVrate, femaleurbanrate, employrate and urbanrate responses are being collpapsed to create as many new categorical variables so called: categoricallifeexpectancy, categoricalco2emissions, categoricalalcconsumption, categoricalHIVrate, categoricalfemaleurbanrate, categoricalemployrate and categoricalurbanrate. The intervals are determined based on cut points derived from specific percentiles of these variables, i.e looking at the frequency distributions of each variable as they are */
PROC SORT; by country; /* manipulation of data being started and sorted according to the unique identifier "country" */
PROC PRINT; VAR lifeexpectancy co2emissions urbanrate alcconsumption HIVrate incomeperperson employrate femaleemployrate; /* display of the recording data of the variables of interest to check how missing data (.) are distribuited on dataset */
PROC PRINT; VAR categoricallifeexpectancy categoricalco2emissions categoricalurbanrate categoricalalcconsumption categoricalHIVrate categoricalincomeperperson categoricalemployrate categoricalfemaleemployrate; /* display of the categorical variables values to check the distribution of missing data "." returned by quantitative variables set to "."" formerly */
PROC FREQ; TABLES lifeexpectancy co2emissions urbanrate alcconsumption HIVrate incomeperperson employrate femaleemployrate; /* frequency distribution of quantitative variables with variable names */
PROC FREQ; TABLES categoricallifeexpectancy categoricalco2emissions categoricalurbanrate categoricalalcconsumption categoricalHIVrate categoricalincomeperperson categoricalemployrate categoricalfemaleemployrate; /* frequency distribution of categorical variables */
RUN;
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Cassandra Crossing/ Archivismi: archiviamo Cassandra, parte seconda
New Post has been published on https://www.aneddoticamagazine.com/it/cassandra-crossing-archivismi-archiviamo-cassandra-parte-seconda/
Cassandra Crossing/ Archivismi: archiviamo Cassandra, parte seconda
(565) — Dopo aver preparato i pdf non ci sono più scuse, dobbiamo archiviare il nostro primo articolo di Cassandra Crossing.
1 gennaio 2024 — Nelle precedenti puntate di Archivismi abbiamo raccontato le caratteristiche principali di Internet Archive, e caricato un semplice documento di esempio. Successivamente ci siamo dati l’ambizioso obbiettivo di uploadare l’opera omnia di Cassandra, ed abbiamo faticosamente preparato il materiale necessario nei formati e struttura più opportuni.
Non ci sono più scuse; è il momento di iniziare a caricare il primo documento di Cassandra Crossing, con tutte le cosette ed i metadati al posto giusto!
Dobbiamo quindi cimentarci davvero con ia e, visto che dovremo caricare centinaia di documenti, non farlo direttamente con la linea comandi, caricando un file per volta e scrivendo tutti i parametri ed i metadati su una lunghissima linea comandi.
Molto meglio impratichirsi fin da subito con i bulk upload, che si realizzano fornendo ad ia un unico parametro, cioè il nome di un foglio elettronico in formato CSV, in cui inseriremo i dati necessari (e li modificheremo tantissime volte per rimediare ad inevitabili errori).
Il comando per fare ciò è semplicemente
ia upload — spreadsheet=metadata.csv
Il lavoro vero sarà riempire il foglio elettronico finale con migliaia di righe di dati, ma facciamo un passo alla volta e carichiamo un solo oggetto, per cui un file di tre righe basterà.
Il nostro primo documento conterrà due file tra quelli generati per l’archiviazione, il pdf come documento principale e l’html entrocontenuto come secondo file; aggiungeremo anche un minimo sindacale di metadati, e l’identificativo verrà scelto uguale al nome dei file, tolta l’estensione.
Insomma, dopo molti, molti tentativi ecco il foglio …
Sembra facile, ma c’è voluta mezza giornata di lavoro, per avere il primo inserimento soddisfacente. Minuzie apparentemente insignificanti ma in realtà diaboliche hanno richiesto un sacco di tempo per prove e controprove. Ve ne racconto qualcuna qui, sperando così di farvi risparmiare tempo prezioso.
uno — quando salvate un foglio elettronico in formato CSV, che vuol dire “valori separati da virgole” non fidatevi della vostra applicazione. In certi casi, qui in Italia, l’applicazione potrebbe decidere di usare non la virgola ma il punto e virgola, e voi non ve ne accorgerete subito. Giuro, è successo!
due — disabilitate, nell’applicazione con cui state gestendo il foglio elettronico, tutti gli strumenti di autocorrezione; altrimenti il programma deciderà certamente di sostituire qualcosa per il vostro bene. Nel mio caso ha deciso di sostituire due segni meno consecutivi, presenti nei nomi di file, con un “trattino lungo”, una modifica praticamente invisibile, anche da linea comandi. Questo ha portato all’inspiegabile messaggio di errore di file non trovato, ed ha rese necessarie alcune dozzine di prove, con relativi arrampicamenti sugli specchi. Non riferisco qui le parole che sono state pronunciate quando il problema è stato finalmente localizzato!
tre — state molto attenti quando inserite i valori nei campi. Un singolo spazio bianco prima o dopo il valore può non farlo interpretare, ed avere effetti imprevisti. Uno spazio all’inizio di “ test_collection” ha ad esempio impedito l’assegnazione corretta dell’oggetto alla collection di test, destinata, come già sapete, ad abilitare la cancellazione automatica dopo 30 giorni. In più considerate che non è possibile assegnare esplicitamente l’oggetto a collezioni pubbliche come “opendata”, ma bisogna accettare la selezione automatica che verrà operata dal sistema.
quattro — inserite nel foglio la colonna mediatype, quando i documenti sono testuali (txt, html, pdf, etc.), ed usate il valore, “texts” altrimenti il sistema assegnerà automaticamente il valore “data” e questo avrà effetti collaterali insidiosi. Ad esempio il browser di oggetti non vi farà sfogliare le pagine, malgrado tutti i file derivati necessari siano stati creati correttamente. Il mediatype, contrariamente alla grande maggioranza dei parametri, non può più essere modificato, ma è necessario cancellare e rigenerare l’oggetto.
cinque — cancellare un oggetto non è un’operazione istantanea, ma richiede minuti o decine di minuti prima che l’effetto si propaghi in tutte la parti dell’interfaccia del sito. Non merita cancellare da linea comandi con ia; è decisamente più pratico farlo dalla pagina My Upload. Ricaricate spesso la pagina, e se notate cose strane, provate anche a svuotare la cache del browser.
sei — la comparsa di un oggetto appena creato nella finestra My Upload è, stranamente, abbastanza veloce, ma scatena tutte le operazioni “derivative”, che a loro volta generano gli altri file in tempi variabili ma abbastanza lunghi. Questo vuol dire, ad esempio, che il browser di oggetti non sarà in grado di farvi sfogliare le pagine prima di una mezz’ora, e che la funzionalità di ricerca interna al browser di oggetti sarà attiva solo dopo parecchie ore.
Però, alla fine, che soddisfazione …
Ed anche per oggi è tutto. Stay tuned per la prossima puntata di “Archivismi”.
Scrivere a Cassandra — Twitter — Mastodon Videorubrica “Quattro chiacchiere con Cassandra” tempo Lo Slog (Static Blog) di Cassandra L’archivio di Cassandra: scuola, formazione e pensiero
Licenza d’utilizzo: i contenuti di questo articolo, dove non diversamente indicato, sono sotto licenza Creative Commons Attribuzione — Condividi allo stesso modo 4.0 Internazionale (CC BY-SA 4.0), tutte le informazioni di utilizzo del materiale sono disponibili a questo link.
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DataBeers Torino XV - presentazioni e foto
Qualche settimana fa andava in scena il XV episodio di DataBeers Torino. Una bella serata fra amici e soprattutto di nuovo in presenza.
Qui qualche foto dell’evento e le presentazioni dei nostri relatori.
Grazie a tutti!
Corrado Monti - Postdoctoral Researcher at ISI Foundation - "Predicting and Analyzing Donald Trump support on social media"
Eleonora Priori - Università degli Studi di Torino - "Agent-based models: un laboratorio di simulazione per comprendere il mondo"
Livio Bioglio - Università degli Studi di Torino - "What emotions TV shows trigger in their audiences?"
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#salaries.devops-jobs.net#analytics#share#salaries#opendata#salarysurvey#devops#cloudjobs#sre#cloud#CloudComputing
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Mapping Friday’s 30% Drop in NYC Subway Ridership
As the severity of the COVID-19 crisis in NY accelerates, the subway is still running. There have been asks by both Mayor De Blasio and Governor Cuomo that people should not crowd into subway trains - they should instead wait for the next one with more space. If you have ever taken a subway at rush hour, you know that ask is a ridiculous one. Nonetheless, it might be reasonable advice as the number of riders starts to drop on New York City’s subways. And that really started to happen as of Friday (Mar 13th), which saw a citywide drop of about 30% when compared with the Friday the week before (Mar 6).
Even though it was a 30% drop overall between those two, each station saw it’s own unique drop in ridership, and I figured that those drops would not be evenly distributed. Though many companies moved to telecommuting last week, companies like retail operations and restaurants still need their employees to come if they are going to remain open. So, those employees can’t simply call in. Given that, I wondered if the drop in subway ridership was going to be larger in more affluent neighborhoods that have a higher proportion of employees that can telecommute?
To find out, I made a map of NYC subway stations, where small circles indicate large decreases in the number entrances at that station, and larger ones indicate small decreases. This is a bit odd, but the idea was to make it clear where there was relatively MORE sustained ridership when compared to other stations. Clicking on a station will give you the name and the % change for that station.
It becomes abundantly clear that as you go farther out - away from Manhattan - the drop in subway ridership is far less extreme. Those generally are also less affluent neighborhoods. To test that theory, I made a quick scatterplot which reveals a -77% correlation between the income of the census tract of the station, and the change in ridership.
What’s troubling is that this may mean an additional health burden on those who may have less access to quality healthcare and are less likely to have paid sick time (or a security blanket for lost income). The health risks of keeping the subway open are not evenly distributed among income groups.
Data used:
Subway Turnstile Data (via MTA)
Median Income by Census Tract (via esri)
Geocoded Subway Locations (via Chris Wong)
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Wenn der Staat die Notwendigkeit sieht, dass alle Bürger sich zur Datenerhebung melden, dann muss auch wirklich ALLES transparent für alle sein! #OpenSource #OpenData #OpenProcess #OpenWorkflow #Transparency #Corona #COVID19
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A small piece of Mexico City in Minecraft.
Make your own: https: //www.geoboxers.com/worldbloxer/
#minecraft#minecraftcity#minecraftedu#openstreetmap#opendata#gis#gaming#urbandesign#Architecture#mapping#map#travel#landscape#geography#geodesy#minecrafters#minecrafting#education#educraft#mexico#ciudaddemexico#mexicocity
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The Money Laundering Business Empire of DK Shivakumar - Visualised from RoC OpenData
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