Numera Loka

Curating ideas and research. Sharing insights on everything urban-related. Instagram: @numeraloka.

[Numera Loka @NumeraLoka]

Komparasi penurunan ridership antara stasiun yang berlokasi di area menengah ke atas, i.e., Orinda, dan yang berlokasi di area relatif miskin, i.e., Richmond.

[Numera Loka @NumeraLoka]

Komparasi alur matriks keluar-masuk penumpang untuk melihat pengaruh pandemi terhadap penggunaan transit untuk bulan Mei tahun 2019 dan 2020. Apresiatif terhadap @SFBART yang merilis data bulanan seperti ini.

[Numera Loka @NumeraLoka]

Adakah data serupa yang dapat diakses publik di Indonesia? Cc: @mrtjakarta @PT_Transjakarta

[Numera Loka @NumeraLoka]

@yadiwinarto @mrtjakarta @PT_Transjakarta Baru saja kami DM emailnya. Terima kasih Pak @yadiwinarto.

[Numera Loka @NumeraLoka]

@yadiwinarto @mrtjakarta @PT_Transjakarta Terima kasih Pak @yadiwinarto — tautannya sangat bermanfaat. Adakah data historis @PT_Transjakarta yang dapat diakses publik dlm format krg lebih seperti ini: https://t.co/u8lAhmkUAs? Kami sdg coba kompilasi data dari berbagai transit agencies. Terima kasih sekali lagi.

[Numera Loka @NumeraLoka]

#Jakarta dan #Bali merupakan contoh yang tampak mewakili observasi di atas.

[Numera Loka @NumeraLoka]

Penurunan mobilitas tampak dipengaruhi oleh proporsi sektor pekerjaan. Di provinsi-provinsi dengan proporsi sektor pekerjaan bidang jasa yang besar, penurunan mobilitas terkait tempat kerja tampak lebih signifikan.

[Numera Loka @NumeraLoka]

Meski ada kecenderungan penurunan mobilitas yang relatif lebih signifikan di provinsi dengan kasus #COVID19 yg cukup tinggi dibandingkan dengan provinsi yg relatif terdampak lebih rendah, keterkaitannya cenderung tidak linier. #RStats #gganimate

[Numera Loka @NumeraLoka]

Eksplorasi data harian #GoogleCommunityMobility dan kaitannya dengan jumlah kasus #COVID19 di tingkat provinsi di #Indonesia. Apakah penurunan mobilitas masyarakat di provinsi terdampak tinggi lebih besar dari masyarakat di provinsi terdampak rendah?

[Numera Loka @NumeraLoka]

Thanks! Much appreciated. We are working on developing a github account and have the data stored there for the public to access.

[Numera Loka @NumeraLoka]

A very valid point! We would welcome a more elaborative information on the variation, if any, of testing rate across districts and the likely implications on reported cases. Cc: @BNPB_Indonesia @KemenkesRI

[D! @dannygazali]

@NumeraLoka There's gap in testing. The capital could enjoy the privilege of having great fund and lab facilities compared to others

Public need to get informed on how many testing a day each district, we still otherwise have no nearly-clear picture of viral transmission yet.

[Numera Loka @NumeraLoka]

@FrestMango It looks like the images resolution as uploaded is somewhat sufficient. Let us know if you needed larger image sizes.

[Numera Loka @NumeraLoka]

Animated map showing the spatial distribution of #coronavirus cases in Java, Indonesia over time (March 25 - April 13, 2020). Each dot represents district (kabupaten/kota) that has at least one reported case during this period.

[Numera Loka @NumeraLoka]

Historical #coronavirus cases data at village-level as released by @DKIJakarta allow us to analyze and observe the reported cases at a considerable level of granularity.

[Numera Loka @NumeraLoka]

Along the same lines, the graph below shows reported cases at the district-level as well, including data derived from South Sulawesi, West Sumatera, and West Nusa Tenggara.

[Numera Loka @NumeraLoka]

We compiled the data by manually inputting (duh!) reported cases on daily basis from provinces that open up their data at the district-level. We believe data at this level of granularity would be of importance to shed light on the variation across space and time.

[Numera Loka @NumeraLoka]

In observance of #InternationalDayOfDisability, these maps and figures show the geographic distribution of disability-related cases in #Jakarta (https://t.co/3FUNdk7tQW). #RStats #DataViz

[Numera Loka @NumeraLoka]

A qualitative observation of educational attainment in #Jakarta seems to suggest a slightly uneven distribution of relatively highly-educated population in the region.
#RStats #DataViz #DotDensity #30DayMapChallenge

[Numera Loka @NumeraLoka]

As a follow up to previous post depicting the most and least affluent neighborhood in #Jakarta using property taxes data, we further probe how does this observation align with the built environment characteristics by a virtue of slum assessment. #30DayMapChallenge #RStats

[Numera Loka @NumeraLoka]

Property taxes data reveal an interesting pattern of neighborhood characteristics in Jakarta. One particular puzzle: the most and least affluent neighborhoods in the city (bounded by green and red line, respectively) are almost adjacent from one to the other #30DayMapChallenge