Introduction

Humanitarian logistics is defined as the process of planning, implementing and controlling the efficient, cost-effective flow and storage of goods and materials, as well as related information, from the point of origin to the end of consumption to alleviate the suffering of vulnerable people

Its Extent and Impacts

The humanitarian logistics of peace operations in sub-Saharan Africa is one of the most challenging operations in the contemporary world. Faced with this reality, several scenarios challenge conventional logistical practices translated into humanitarian efforts on the African continent. Several events have led Africa to the current situation, whose conflicts have worsened the case in some countries. In this scenario, three countries stand out on the African continent: South Sudan (SS), the Central African Republic (CAR) and the Democratic Republic of Congo (DRC).

1.Positive Impacts

Data Collection and Mapping - GIS technology is used to collect, analyze, and visualize geospatial data. Humanitarian organizations in South Sudan leverage GIS to map various elements such as population distributions, events, infrastructure, health facilities, water sources, and more. This comprehensive mapping enables organizations to identify vulnerable areas and better understand the needs of the population.

Situation and Needs Assessment - OSM aids in conducting rapid situation assessments and needs analyses during crises and emergencies. Each region in South Sudan is prominently represented by one or the other ethnic and communal group, each with its own methods of action. By overlaying various data layers, including demographic, environmental, and public infrastructure, organizations can quickly identify areas that require immediate specialized attention and allocate resources accordingly.

Resource Allocation and Planning - OSM enables efficient resource allocation. Humanitarian agencies can determine the best locations for setting up relief distribution centers, medical clinics, or emergency shelters based on the data. This leads to more effective and targeted assistance.

2.Reccomendations

Collaborative efforts between various organizations and government agencies in Democratic Republic of Congo have to leverage Open Street Mapping for humanitarian work, contributing to more effective disaster response, and conflict mitigation. These partnerships have been crucial in addressing the complex challenges in the region.

Link

My profile

The Wedge

The Wedge is one of the most dangerous beaches for BodySurfing. It breaks best on a South to Southwest swell. It will normally be 1 1/2 times bigger waves than the other beaches on Orange snd Los Angeles Countries. It breaks in very shallow water and people often get pulled up into the wave and thrown over the falls, and is why so many people break their neck or back here.

Not For The Faint of Heart

You’ve got to be ready to tackle The Wedge on a big day.

This wave is a mutant beast that provides towering peaks and vertical abysses and can only be categorized as a hardcore surf break.

The place produces brutal and sometimes funny and entertaining moments for those who watch the show unfold from a comfortable outside position or from behind a camera lens.

Naturally, The Wedge became one of the most photographed and filmed surf spots on the planet.

Even on medium-sized days, hundreds of amateur and professional lensmen eager to capture the flashy wipeouts and the perfect rides invade the surfing jewel of Newport Beach.

There are many ways of enjoying this wave. Today, The Wedge is known for attracting surfers, bodyboarders, skimboarders, and bodysurfers, and they all know what to expect.

During carnage days, only the youngest, the fittest, and the craziest names in town are willing to defy the massive A-frames on offer at The Wedge.

In other words, it’s the perfect place for war veterans and exhibitionists who think fear is nothing to fear.

“The Wedge is easily the heaviest wave in Southern California with hard sand and thick lips,”

##Jeff Hubbard, world bodyboarding champion, once said.

Although it is mainly a left-hand wave, you can also backdoor it on a finless craft like a bodyboard or a skimboard.

But make no mistakes: there are increased chances it will close out on top of you.

If you’re lucky, and when Mother Nature is on your side, you may end up claiming a successful barrel exit. But that is not the norm.

The Wedge explodes next to the Newport Harbor Jetty and one mile east of the Balboa Pier. It can be relatively easy to position for a flawless ride, but not all waves will allow you to escape the guillotine.

And to make things even harder, there’s always a tricky backwash that can propel you up into the sky.

On a typical day at The Wedge, the spot will be pumping giant walls of water in the 15-to-30-foot range.

Be careful: subtle changes in the wind speed and direction, swell size and direction, and tide can turn the place into a nightmare.

Who Shouldn’t Surf The Wedge?

If you’re a beginner, a weekend warrior, or a not-so-confident intermediate wave rider, do stay away from The Wedge. Newport Beach’s most famous surfing wave could be a deadly experience.

This is not a standard surf spot; The Wedge creates thick lips that will unexpectedly toss you down the face of the liquid mountain and side waves that will push you laterally.

If you’re willing to give it a go, spend a few hours studying the breaking of the wave and pay attention to the way riders tame it without getting beaten up.

When you feel you’re ready, paddle out and observe the behavior of the wave from the sidelines. Wait a few minutes or even a couple of hours.

Not all waves are surfable, and not all waves break the same way. You’ve got to feel it, commit to it, and then take off as soon as possible.

Two rules of thumb: avoid going right and stay away from the jetty. Otherwise, you’ll end up in a hospital bed or a cemetery.

Respect the locals, and wait for your turn. Luckily, you will score the wave of a lifetime. The Wedge is adrenaline in its purest form.

The Wedge, Newport Beach, California | When and How To Surf It

Swell Size: 5-10 feet Swell Direction: S/SW Wave Height: 6-20 feet Wind: Offshore (N/NE) Tide: Mid, But Varies Season: Late Summer/Early Fall

Keep removing my edits.. go ahead. I can just waste my time and add them back. I have no problem doing that.

Welcome to my fifth (and a half) OpenStreetMap NextGen development diary.
Tomorrow, I’m returning home and I’ll be able to resume work at full speed 🔥.

This is a short edition of the development diary.

🐙 This project is open-source and publicly available:
https://github.com/Zaczero/openstreetmap-ng

Intro

For the past 13 days I have been on a journey of finding a new place to rent. Without my home office, I wasn’t able to become productive. The place I’m staying at doesn’t have a good office spot and being on my laptop doesn’t help. However, I am now very motivated to get back to work and push even harder!

May Will Be Big

At the end of May, OpenStreetMap-NG will include necessary functionality to run on a testing server, as well as to invite new contributors into the project. Starting with 6th of May, I won’t have any time-consuming plans for this month so I’ll do my best to wrap everything up. What’s exactly left has been described in Diary #5 Short-Term Development Plan. I have already started to prepare the All-in-One Contributor Guide which will also be finished up (it currently lacks backend/frontend-specific guides). This is going to be the first major milestone of the project!

Project Sponsors 🏅

I was happily surprised to see new faces even during my lower activity period. I will do everything to deliver the promised results. As always, thank you for supporting the project, monetarily, and with staring the project on GitHub!

Currently, the project is sponsored by 13 people!
Five private and four public donors on Liberapay, and four public on GitHub Sponsors.

• ~1847430, gileri (Eric G.), LaoshuBaby (快乐的老鼠宝宝), RedAuburn (Harry Bond), ~1819511, AurimasF (Aurimas Fišeras), edpichler (Edu), starsep

Disclaimer

This project is not affiliated with the OpenStreetMap Foundation. It’s an independent and community-sponsored initiative.

How do I make a map of a California county showing population density?

https://community.openstreetmap.org/t/south-australia-road-classification-proposal/112068

Highway, Building, Barrier, Tower, Area, Landuse, and more.

if you history user of 2 years form highways this finding. was edit errors has been yandex map.

There is a long discussion happening in the United States section of the community forum regarding where to draw the line between the “main” populated place node values, and specifically the place=* values of city and town in New England. I thought it would be useful to do a bit of analysis to see how these values are distributed across the database when compared to population. Through this analysis, I include all tags which have place values of city, town, village, hamlet, and isolated_dwelling. I also only include nodes that have a population tag.

My overpass query for each category looks like this:

[out:csv(::id,place,population;true;"|")][timeout:60];
node[place=city][population];
out;

One of the challenges of analyzing this key is that because it represents order-of-magnitude differences, its distribution is log-normal. In other words, it forms a bell curve provided that the X-axis is drawn logarithmically.

To look at this data logarithmically, I grouped the place nodes logarithmically, in steps of 1, 2, and 5 per 10x jump. When viewing the distribution of place=town, the log-normal shape comes out quite clearly. The number on the X axis represents the upper limit of each bin.

Now that we’ve assessed that the data is distributed log-normally, the next question we want to be able to as is, for a populated place with a certain population, how are the place values distributed? For this, we look across each logarithmic “bin” and determine the percentage of each place value in use:

We can assess, for example, that for places with a population between 500 and 1,000 (the bin labeled “1,000”), it’s tagged place=village over 90% of the time. The village blip at 10,000,000 is the result of a data error - a single remote place node being erroneously tagged with a high population in a bin of size n=19. Needless to say, on the far right of this graph, there are fewer and fewer nodes in each bin.

Lastly, we would like to know the mean and standard devation of each place category. However, since this is log normal, we need to compute the mean and standard deviation in the logarithmic domain, and then convert it back. The mean, and plus or minus two standard deviations are computed in the table below:

place=	-2σ	-1σ	μ	+1σ	+2σ
city	8,341	33,118	131,496	522,118	2,073,119
town	782	2,814	10,124	36,422	131,039
village	13	71	393	2,173	12,011
hamlet	2	8	36	165	763
isolated_dwelling	0	2	6	22	84

Thus, this means that 68% of place=town nodes – one standard deviation – that are tagged with a population tag, have a population= value between 2,814 and 36,422. Taking this out to two standard deviations, 95% of all place=city nodes have a population= tag value between 8,341 and 2,073,119.

Clearly, there is considerable overlap between each category, no doubt because of differences in tagging conventions between places, differences in accounting for population, and differences in place tagging in areas of different population density.

One of the most rewarding aspects of being an OpenStreetMap contributor is the surprise of discovering where your contributions will be displayed next.

“I put that obscure, forgotten place back on the map, and now they’re everywhere!”

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Researching toponyms is fun because I have to analyze the daily conversations among the locals and try to triangulate the place name centroid based on this conversations. Bonus point if my centroid triangulation somehow coincides with ancient, forgotten, historical maps.

Traces of Sepatan, Djati, and Rawa Roko can still be found on old historical maps, circa 1900. Locals still use these toponyms right now, despite the government’s refusal to acknowledge their existence in the official addressing system.

---

It’s ‘forgotten’ in the sense that the local government over here has simplified the toponym system too much. Some toponyms have been upgraded to official administrative region names (and are now embedded in the official addressing system), while the rest have been left behind. People still use these ‘left-behind’ toponyms daily to refer to certain places, even though the government has somewhat ‘discouraged’ their use. (The colonial administration did it better, though. When I consulted the archives and library for old maps, all of these ‘left-behind’ toponyms were actually mapped quite well in the past.)

And this situation has led to mass confusion for people. Government-mandated addresses often don’t reflect reality. People still use all those ‘left-behind’ toponyms in their daily activities, yet they don’t appear on official, government-mandated maps and addressing systems.

That’s one of my personal missions right now: to put all those ‘left-behind’ toponyms back on the maps.

[Semi-automated translation of the Italian diary entry]

On OpenStreetMap, a tree can be represented as a natural=tree node.

Leaf type 🌿

leaf_type is the most common tree-related tag in the database, this is because it is easily verifiable and is supported with a quest by StreetComplete. The values are broadleaved and needleleaved. Some argue that palms should not be tagged as broadleaved but with a value of their own, =palm.

Leaf cycle 🍂

This tag describes whether a tree is deciduous or evergreen. Most needleleaved tree species are evergreen, but this is not always the case, so do not infer this value automatically. You can add this value easily if you know the species, or more simply if it is autumn/winter. If not, you can try to see if satellite or street-level imagery taken at those times are available.

Genus and species 🌳

If you are not an expert, there are tools that can help you recognise the genus/species of a plant, such as Pl@ntNet and iNaturalist, both of which also exist as mobile apps. On OpenStreetMap there are many trees with species (or species:wikidata or species:xx) that do not have leaf_type or leaf_cycle. These values are of course identical for each species (and also for many genus) and can therefore be easily deduced. There are two lists on the OSM wiki that contain these values for genus and species.

MapComplete has a dedicated tree theme that can be used to enter the species while SCEE has a dedicated quest (“What is the genus or species of this tree?”).

Monumental trees 🏛️

Monumental trees can be mapped adding denotation=natural_monument. In Italy they are recorded by the Ministry of Agriculture, Food Sovereignty and Forests (Masaf) with annual updates. You can map them by adding ref:masaf, which is a unique code assigned by the ministry.

In Poland they are recorded by the ‘Generalna Dyrekcja Ochrony Środowiska’ (GDOŚ) (you can find many of them with a query natural=tree+ref:inspire=*), in Serbia many zapis (sacred trees) are mapped with ref:zapis while b-unicycling has recently documented the place_of_worship=sacred_tree tag to map rag trees in Ireland and Scotland.

Rendering 🎨

Trees are rendered on Carto as green circles with a brown dot in their centre, regardless of their attributes.

The Straßenraumkarte Neukölln renderer shows the trees differently according to leaf_type, circumference and diameter_crown. However, the web map only works for the Berlin district of the same name.

F4 Map and Streets GL (no longer updated) are 3D renderers that use the height tag. F4 Map distinguishes trees by foliage type and also supports some values to differentiate palm trees. Esri also has a 3D map that uses OSM data, and which should support height and also some genus values.

On the IMAGICO.De blog, there is an interesting article that goes into detail on possible renderings to highlight the various attributes.

QA 🛠️

There are more than 20,000 mapped trees, but QA tools are still few. That is why I created this MapRoulette project: Tree Validation.

There are challenges that compare values in the database with the largest values ever recorded in nature, for example trees taller than Hyperion. Other challenges concern uncommon or incorrect values of denotation, species, genus, leaf_cycle or leaf_type.

The hope is that in the future more and more tools will check on tree tagging. For now there is at least one open issue for Name Suggestion Index and one for Osmose/JOSM.

If you want to discuss trees on OSM, you can use the “tree” tag on the OpenStreetMap Community Forum.

Have fun mapping!

I’ve finished (for a second time!) mapping all the paths in my local cemetery. The first time another mapped decided that concrete or gravel ways designed for walking on weren’t “paths” and deleted them all. He also seemed to have strong feelings about people cycling in cemeteries, which is odd as The Greater Metropolitan Cemeteries Trust actually endorse it as a use of their land. As my daughter is buried in the cemetery I feel some ownership of the place, and do love it and Merlynston creek that flows through it. I’m hoping no one vandalises my work again.

I received a request to update my previous list of people who map every single day. The top 3 places remain the same, with Aurimas Fišeras passing the 10 year mark of non-stop mapping! Congratulations on an amazing accomplishment, and a big thank you to all of these dedicated mappers!

Consecutive Days	First Day of Streak	User
3672	2014-04-03	Aurimas Fišeras
2810	2014-08-09	vichada
2687	2016-10-22	ika-chan!
2256	2018-02-17	LidaCity
2106	2018-07-17	Algebre gama
1943	2018-06-02	thetornado76
1943	2018-12-27	hendrik-17
1894	2014-06-15	roschitom
1860	2017-04-13	looniverse
1837	2019-04-12	bxl-forever
1828	2019-04-21	JJIglesias
1755	2019-07-03	Sammyhawkrad
1674	2019-09-22	Zrop
1631	2019-11-04	mstock
1629	2018-06-26	phiphou
1583	2018-10-06	fx99
1544	2018-10-29	BCNorwich
1501	2014-06-26	RoadGeek_MD99
1467	2017-11-04	dvdhoven
1450	2018-02-16	alkushi
1392	2019-10-25	marczoutendijk
1346	2017-10-28	piotrS
1336	2020-08-25	NieWnen
1318	2020-09-12	Strubbl
1305	2020-09-25	mindedie
1292	2017-12-11	vincent_95
1291	2020-10-09	Leonius_Bad
1286	2020-10-14	Grass-snake
1259	2020-11-10	SekeRob
1240	2020-11-29	seattlefyi
1211	2015-01-28	lodde1949
1159	2017-02-05	futurumspes
1153	2019-02-10	jmapb
1149	2021-02-28	MJabot
1092	2019-03-21	mmahmud
1070	2016-06-15	mindedie
1064	2018-08-12	ikiya
1063	2017-12-02	下り専門
1055	2019-07-22	Nesim
1031	2021-06-26	vincent_95
1026	2015-09-25	Nesim

SotM Latam 2024 - Belém/Pará - Brasil.

The State of the Map Latam 2024 will take place in the city of Belém, Brazil, from December 6 to 8, 2024 at the Instituto Federal do Pará, this being the sixth edition of the ‘Latin American OpenStreetMap Conference’ and aiming to promote the use of OpenStreetMap (OSM), and the integration of OSM mappers, developers, open data communities, free and open source software communities, students, researchers, geoinformation professionals, non-governmental organizations, companies and public institutions.

SotM Latam 2024 After the voting period for choosing the SotM Latam 2024 logo has concluded, I am pleased to announce that the chosen option is 1.

You can see the results and voting details in the document shared above: https://docs.google.com/document/d/15Kbotyc6UCcWQ2K7apPOnfqXSSDFfvlDQtZxM_Nkag4/edit

Website: http://2024.osmlatam.org

https://wiki.openstreetmap.org/wiki/ES:LatAm/Eventos/State_of_the_Map_Latam_2024

You can see the voting results and details in the previously shared document: https://lnkd.in/dQcDDFCv

an organization of UMBRAOSM Union of Brazilian mappers from Openstreetmap, Latam Community and Foss4g

SotM Latam 2024 organizing committee

Contact: state@osmlatam.org

http://www.umbraosm.com.br

Years ago when searching for a viewpoint from an old photo where I wanted to do rail photography I managed to locate the exact cliffside overlook and discovered a somewhat hidden gem of trail network in the process.

Though there is an official dirt road in the canyon below and a few desire paths offshooting from it the hills above remained relatively unmapped not showing up in the otherwise void of an area.

With the understatement of the local forecast for the afternoon/evening I packed my beloved eTrex 20x in the camera bag along with my trusty Nikon D700 planning to take in some of the views while there in between tracing runs expecting no more than some cloudiness and a light sprinkling.

I had originally planned to do some averaging as well to mark down 3 of my favorite spots on the cliff to photograph the scenery below but just as I reached the overlook the light rain that had accompanied me on most of my journey up started to thicken and become slanted. Just as I positioned myself on the side of the ledge for my first point reading spot the wind had picked up considerably to near constant 70kph gusts. The eTrex wobbled fiercely atop the perching rock and I had to scrap my plans of more photos just to hold it still for fear it may fall to it’s death below as my other hand clinged to the rockface hoping to avoid a similar fate.

After 5 minutes to allow a good average I quickly hit save and climbed back up to grab my gear bag and make my way back down the path for the second tracing run in the opposite direction to help iron out anomalies when mapping from the trace. As I set off the steadily increasing rainfall now started to turn to almost sideways hail from every direction as the many rockfaces perturbed the wind. Though hectic as it may seem the eTrex managed to maintain it’s nice 8 foot accuracy throughout most of the trip and likewise I was similarly high spirits.

In an almost serene state of mind while walking the trails my attention drew to the contrast between the dramatic weather and environment and the life which springs forth from otherwise dead plains because of it. Many cacti once shriveled from the dry winter were now stuffed with water, their beautiful flowers blooming brightly pink which would soon bear fruit. The normally tan plains dead with brush now teeming with fluorescent wildflowers of all colors in every direction. In that moment I remembered who I was when escaping here 11 years ago, hurt and tired of the chaos and sterility of the urban design seeking to hide from the world and in the process discovering a new one all to myself.

It’s these things the fuel my passion for exploring and desire to document what’s around me, the eTrex always at my side as a faithful companion for so long now. The amount of memories and many long adventure it holds being priceless to me.

Summary: What if AI creates the Changeset Comments? We could send locations, tag types, and quantities to get an output. AI would have to be run locally with small models for cost and be validated by the user.

Problem 1: Time I assume that 1,000 users create 2 changes in 1 day. We assume that each change set takes 3.5 seconds. 1000 users *2 changes * 3.5 seconds per change = 7000 seconds. OSM Users spend about 1.9 hours per day.

Problem 2: Skill Outsourcing Users should spend time on the things AI can’t do.

Problem 3: Server Side Peer Review We have human generated changeset comments. We could create AI generated changeset comments. We could ask the AI, “are these 2 changeset comments so different that it looks malicious”?

General AI Inputs: 1. Location: Where did the user map? 2. Feature Types: What tags did the user use?

AI Prompt: “You are an AI system. A user made edits in OpenStreetMap, a collaborative mapping project. They mapped locations[Mappleville, MN, USA; Bobville, MN, USA] with tags[50xSidewalks, 20xMarkedCrossings, & 10xReligous Areas]. You will create a changeset comment that concisely tells human reviewers what this changeset was about in 3 sentences or less. Exact numbers are not important. Changesets describe changes, so don’t request anything. Don’t mention anything that is common across all changesets.”

AI Response (https://www.meta.ai/): “Added sidewalks, marked crossings, and religious areas in Mappleville and Bobville, MN. Improved pedestrian and accessibility mapping. Enhanced local community information.”

Specific AI Inputs for Locations: 1. Cities[1 to 5], States[1 to 5], Countries[1 to 5]. 2. Is this a place with unclear boundaries? (What if somebody maps the ocean) 3. What is the size of the bounding box for this edit in KM?

Specific AI Inputs for Feature Types: Tags[1 to 6] & corresponding Quantities

Algorithms: 1. Sort the following tags by how frequently each was used in descending order and a limit of 5. 2. For each city, how often was each tag used? Create a table unless the table is huge.

Complexities of the process: 1. Disputed Boundaries: This was the changeset that changed the boarder. 2. Large Edits: Do not run this edit over changesets larger than 500 edits. 3. Malicious Inputs: Somebody named a building tag after a war crime. The AI received that as an input. What does the AI say? 4. Resource Allocation: Developer Time could be better spent doing something else. 5. Irregular Edits: I will use every tag in OSM only once. I will map an area the size of a continent.

Complexities of AI in general: 1. Uncommon Languages: Are these things only good at the 5 biggest languages? 2. Edit Safety: The user mapped religious areas in 2 different nations that share a disputed boarder and are in a war. 3. Money: Laptops with TPU’s are not common in 2024 (but will be in 2030). Mobile Editors with TPU’s are not common in 2024 (but will be on high end phones in 2030). Running AI costs money. Who will pay for it?

Solutions: 1. AI runs locally on a TPU. 2. If you use the outputs of an AI for changeset comments, you are responsible for safety.

Disclaimers: 1. I don’t work in AI. 2. I describe what I don’t have the resources to build. 3. I assume that developer resources should focus on high priority tasks.

Expected Development Difficulty: 1. Web to TPU is hard: Graphics have standard libraries (OpenGL). AI TPU’s are not common and don’t have standard libraries. 2. This can create giant tables if you are not careful.

The benefits of manual changesets: 1. Spam is harder to create in bulk. 2. Self reflection is encouraged. 3. Individuality is good to see. 4. Changesets are the alternative to the Change Approval Board (CAB meetings). It is supposed to take effort.

TLDR: OpenStreetMap (OSM) edits could be aided with AI-generated changeset comments, potentially saving users 1.9 hours daily. AI could analyze edit locations and feature types to generate concise comments, freeing users to focus on tasks that require human expertise. However, implementing AI-generated comments requires addressing complexities like disputed boundaries, TPU libraries, and malicious inputs.

There are some object categories in OSM whose exact classification is often a matter of contention and edit wars. Main highways are one of the most prominent examples. There was a small edit war in Poland which resulted in no less than 4 blocks, but I did not let that crisis go to waste:

Behold road-watcher, a quick Python project that regurarly queries Overpass API for highway=secondary and above within a specified boundary and then detects any classification changes, sending them to a Discord channel (though it’s trivial to substitute it with another means of notification).

The SMCoSE YouthMappers Chapter, renowned as one of Tanzania’s largest mapping communities, hosted a transformative mapathon on April 14, 2024, at the esteemed Sokoine University of Agriculture. This event marked a pivotal moment of collaboration, extending invitations to other YouthMappers chapters in Morogoro, thus amplifying the inclusivity and impact of the initiative. Central to the mapathon’s objective was the concerted effort to contribute to Project #15530 within the HOT Tasking Manager, focusing on mapping cities across the Eastern and Southern Africa Region. By leveraging the power of open data, participants aimed to craft detailed base maps crucial for diverse applications, ranging from urban planning to efficient disaster response strategies.

Amidst an atmosphere described as “fantastic,” the event witnessed a remarkable accomplishment, the successful mapping of approximately 25,000 buildings. This feat not only underscores the collective dedication of the participants but also showcases the tangible outcomes of community-driven endeavors. Moreover, the mapathon served as a platform for new mappers to acquaint themselves with essential mapping tools such as ID Editors and JOSM, empowering them to contribute meaningfully to the OpenStreetMap ecosystem.

Special recognition is duly owed to the Open Mapping Hub Eastern and Southern Africa (OMHESA) for their unwavering support, notably through the prestigious Spatial People Award. This acknowledgment not only highlights the significance of collaborative partnerships but also accentuates the pivotal role of organizations in facilitating impactful mapathons and community initiatives. In essence, the event epitomized the ethos of collaboration, learning, and contribution inherent within the mapping community, further advancing the cause of open data dissemination and spatial awareness in the region.

In conclusion, the SMCoSE YouthMappers Chapter’s mapathon stands as a testament to the transformative potential of collective action in harnessing the power of mapping for societal benefit. It exemplifies how collaborative efforts can foster tangible change, driving forward the agenda of open data accessibility and spatial literacy within Tanzania and beyond. “We don’t just build maps, we build Mappers”

Theatro da Paz, Belém/Pará - Brasil

O Theatro da Paz foi fundado em 15 de fevereiro de 1878, durante o período áureo do Ciclo da Borracha, quando ocorreu um grande crescimento econômico na região amazônica. Belém foi considerada “A Capital da Borracha”. Mas, apesar desse progresso a cidade ainda não possuía um teatro de grande porte, capaz de receber espetáculos do gênero lírico. https://www.theatrodapaz.com.br/

Credito da Foto; Wikipedia, https://pt.wikipedia.org/wiki/Wikip%C3%A9dia:Wiki_Loves_Par%C3%A1#/media/Ficheiro:Teatro_da_Paz_3.jpg Theatro da Paz, Belém/Pará - Brasil

Mais um ponto turístico da cidade de Belém atualizada na plataforma OpenStreetMap https://www.openstreetmap.org/changeset/150449820 através do projeto #MapeaiaBelem, https://projetomapeiabelem.my.canva.site/home que tem como objetivo disponibilizar dados atualizado para todos a comunidade local e também para aqueles que estarão presente aos grandes eventos que acontecerá entre 2024 e 2025, #SOTMLATAM #FOSS4G #COP30 sem contar que esses dados poderá ser utilizado por todos através de APPs como OsmAnd entre outros apps.

2024, SotM_Latam2024, FOSS4G 2024 , Belém, Cop30Belém

Projeto MapeaiaBelem,

Site do Projeto Mapeia Belém. https://projetomapeiabelem.my.canva.site/home Objeto mapeado no Openstreetmap, https://www.openstreetmap.org/changeset/150449820

esse é mas um projeto da UMBRAOSM - União dos Mapeadores Brasileiros do Openstreetmap

site: www.umbraosm.com.br

Instagram: https://www.instagram.com/umbraosmbrasil/

E-mail: contato@umbraosm.com.br

I am currently on a visit to Ireland 🇮🇪 and a lack of proper office space makes it difficult to stay productive. I will try to prepare something cool to show off this week. Sorry for keeping you waiting!

🍟