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Self-Advocacy Online
About Vaccinations James Meadours
Britney Spears and her "Hot Mess" Guardianship Situation ICI Self-Advocate Advisory Committee Members
Staff Matching Cliff Poetz
Budgeting and Personal Finance Cliff Poetz
Personal Safety Cliff Poetz
Thank You to Our Healthcare Workers Cliff Poetz
Remote Learning Cliff Poetz
Advocating for Yourself Cliff Poetz
Remembering Cliff Poetz Joe Meadours
Remembering Rick Cardenas Cliff Poetz
COVID-19: Where We Are and Where to Go from Here Cliff Poetz
Health & Wellness Cliff Poetz
Leadership Cliff Poetz
Self-Advocacy Online Blog
Cliff Poetz is a Community Liaison with the Institute on Community Integration and a founding member of People First Minnesota. He can be reached at poetz001@umn.edu
President Trump showed the country time and time again that he lacked the leadership needed to be the President of the United States. He did not listen to the advice of the CDC, doctors, experts, and professionals. He promoted the use of a drug that is not approved by the Food and Drug Administration. He was around a high-risk population at the war memorial without a mask on. He used the armed forces to push out peaceful protesters just for a photo op. He failed to concede when President-Elect Biden won the election and encouraged his followers to distrust our democracy, which resulted in violence at the capitol. As the president, you need to make tough decisions and you need to lead by example.
How are we supposed to get through this difficult time and the difficult times ahead without strong leadership? As the leader of our country, the president has to follow what the constitution says. Being a leader means not being afraid to tell people what they need to hear, whether it is good, bad, or very bad. The president has to make tough decisions and do the right thing. Some people may get upset at the decisions and some decisions may cause other problems and you have to live with that. Our next leader needs to listen to experts, and listen to what people need and want.
President Biden took leadership on January 20th and I hope we see a peaceful transition of power and steadier leadership in the new administration. President Biden needs to carefully consider all of his actions, including decisions about the pandemic. He will need to make sure everyone gets vaccinated in a timely manner and give everyone the support they need. Since he has spent a lot of time in Congress, he needs to use his knowledge of the process to make progress. The country expects President Biden to respond to current and future issues with common sense and logical action.
Goto Previous Page Health & Wellness Cliff Poetz


A reporter's memoir of what it's like to tell the truth about today's China.
My first day of work in Beijing, my boss asked if I knew the "Three Ts."
"You'll hear more about it from our censor," he said, and then, having inserted that tantalizing fragment into my head, sent me off to begin my new job.
For the next two years, I served as an editor, then managing editor, of an English-language business magazine called China International Business. The editorial staff was comprised of, at various times, two to four American and British editors, and two or three Chinese writers and research assistants. Supposedly, we had a print circulation of 45,000, though nobody I talked to had ever heard of us. In theory, there was a website too, but it was perennially under construction and, since the guy in charge of it didn't actually speak English, never quite readable. We ran briefs on current events; profiled businesses in China; interviewed executives of international companies with a presence in the country, like Crocs and Calvin Klein; and also did long analytical pieces spotlighting industries ranging from coal to lingerie to frozen foods. Our audience was mostly expat businesspeople in China; hence, in addition to being available by subscription, we were distributed in five-star hotels, international schools, and other expat enclaves.
Technically, we were the only officially sanctioned English-language business publication in mainland China. There were a handful of other English-language magazines in town, mostly listings and entertainment mags along the lines of Time Out. These were usually founded by foreigners who'd partnered up with private Chinese companies to secure a license from the General Administration of Press and Publications (GAPP), which oversees print publications in China. Unlike them, we were published not just under the umbrella of the publisher's private media company, but also in cooperation with the Ministry of Commerce (MOFCOMM). In other words, the government wanted us there.
Jobs like this are practically a rite of passage for young, aspiring writers in China who also happen to be native English speakers (and who are trying to avoid teaching English, the default job for most Westerners in Asia). Most start out as copyeditors at state-owned papers like China Daily, correcting the English on articles by Chinese reporters, and often making $1500 a month — enough to live comfortably in Beijing in the first decade of the 21st century (and two or three times the amount of native colleagues with decades' more work experience). I myself was hired as a copyeditor with no prior magazine experience (though I'd worked in book publishing in New York), promoted to editor two months later, then another eight months later found myself running the show as managing editor, at the ripe old age of 28. This was a fairly normal career trajectory in China. Despite the title on my business card, however, I was always technically an "English language consultant" — no foreigners are allowed to direct editorial content in Chinese media. Our censor got pride of place on the masthead, with title of managing editor.
Every legally registered publication in China is subject to review by a censor, sometimes several. Some expat publications have entire teams of censors scouring their otherwise innocuous restaurant reviews and bar write-ups for, depending on one's opinion of foreigners, accidental or coded allusions to sensitive topics. For example, That's Shanghai magazine once had to strike the number 64 from a short, unrelated article because their censors believed it might be read as an oblique reference to June 4, 1989, when the Chinese government bloodily suppressed a pro-democracy movement in Tiananmen Square. Many Chinese-run publications have no censor at all, but their editors are relied upon to know where the line falls — i.e., to self-censor.
Snow's name made for much late-night comedy in my office, along the lines of: "God, that article totally got snowplowed," or "Uh-oh, I predict heavy snowfall for this one." I met Snow for the first time during our inaugural editorial meeting at the office: the top two floors of a six-story, spottily heated building with a pool hall in the basement and what appeared to be fourteen-year-old security guards at the door, in central Beijing. Here, just as my boss had promised, Snow elaborated on the Three Ts, relaying an anecdote about a journalist friend of hers. A photo enthusiast, he once ran a picture he'd taken in Taiwan alongside an article, but had failed to notice a small Taiwanese flag in the background. As a result, the entire staff of his newspaper had been immediately fired and the office shut down.
Despite these words of caution, we didn't take the fact that we had a censor very seriously, at least for my first few months on the job, and evading Snow's changes became a game of sorts. This was easier back then; the August 2008 Beijing Olympics were a year-and-a-half away, and it behooved China to demonstrate that it was an open country. Besides, Snow was a small presence in our daily work routine. She did not come to our office, and aside from that first encounter, didn't attend our story meetings. Each month, we emailed her our list of article topics for the upcoming issue. After we had edited those articles, we emailed them to Snow, and she sent them back marked with her changes. She reviewed them again in layout, and, once satisfied, would give the printer the order to start the presses.
Business content is not censored as strictly as other areas in China, since it seems to be understood that greater openness is needed to push the economy forward and it doesn't necessarily deal with the political issues Chinese rulers seem to find the most sensitive. English-language content isn't censored as much either, since only a small fraction of the Chinese population reads English. (As foreigners reporting on non-sensitive subjects in English, we could worry much less about the dangers — threats, beatings, jail time — that occasionally befall muckraking Chinese journalists.) And, in the beginning, most of Snow's edits were minor enough that we didn't feel compromised. We couldn't say that a businessperson came back to China from the United States after "Tiananmen," but we could say "June 1989," knowing that our readers knew the significance of the month. We couldn't say "the Cultural Revolution" but could write "the late 1960s and early 1970s," to allude to then Communist Party chairman Mao Zedong launching his disastrous campaign that sent millions of intellectuals to the countryside. Writing that a company planned to expand into "foreign markets like Taiwan and Korea" was forbidden because it suggested that Taiwan was a separate country from China, but we could say "overseas markets," since, according to Snow, Taiwan literally is over a body of water from the mainland.
The waters around China were always touchy. In May 2007, we ran an article about wind power, and had an artist create a map of China dotted with wind turbines to illustrate it. Snow cautioned that if we were going to depict a map of China, we had to make sure it included Taiwan and various disputed territories, including the now hotly contested small chain of uninhabited islands that China calls the Diaoyu and Japan the Senkakus. "Just put in a couple dots around the bottom, but whatever you do, make sure they don't get cut off," she said. In lay-out those islands did, indeed, get cut off; but at Snow's advice, the designer haphazardly Photoshopped a few stray dots around the bottom of China's eastern coast. The small gray blobs were not terribly accurate from a cartographer's standpoint, but apparently they were good enough. Snow was satisfied and the illustration ran without incident.
Some of Snow's changes arose from the inherent absurdity of having English-language content reviewed by a non-native speaker. We gave an article the subtitle "Mo Money, Mo Problems," and Snow asked if we'd meant "No Money, No Problems." A December issue included the subtitle "'Tis the Season," which Snow corrected to "It's the Reason."
Once, Snow deleted the word "monster" from a piece that said the Hong Kong stock market had been "boosted by a trend of monster IPOs" from mainland Chinese companies. "I bet the government is trying to downplay these huge IPOs because speculation on the stock market is getting out of control," said our then executive editor, Gwynn Guilford. Later that afternoon, I walked by Guilford's office and heard her saying into the phone, "No, it's not monster, like, grrrrr," while she curled her fingers into a claw and pantomimed an angry bear. Then she hung up and said, "We can leave in 'monster.'"
Many changes were enigmatic. We were told not to use "Manifest Destiny" in a subtitle because, as Snow wrote in her somewhat offbeat English, "this is an anti-government sensitive words group." This provoked a flurry of excited calls from our end, exhorting Snow to tell us more about this "words group" — ideally in the form of a full, emailed list. We had heard that some publications received a weekly fax outlining what topics were taboo, and were dying to see something similar. But she never explained further.
In our December 2007 issue, we had a paragraph saying that the Chinese oil and gas giant PetroChina had been pushing forward aggressively in its overseas acquisitions. Earlier that year it had bought a 67 percent stake in PetroKazakhstan, and it had plans to buy more oil and gas assets in Africa, Northern Europe, and Southeast Asia. Snow wrote, "Better to delete, it is an oral request that the energy sector's overseas acquisition is not encouraged to report." In other words, we wouldn't find any overt directives in writing anywhere, but those in the know understood that this subject was touchy.
All of this pointed to the petty human dynamics that underscored the censorship. The things Snow flagged were rarely taboo because of any overt directive from above. More often, it seemed to me that she thought it might offend another government ministry, which would bring retaliation upon her own ministry. Or, if Snow personally didn't find a statement sensitive, she worried that her boss might, or her boss thought that his boss might. Everyone was guessing where the line fell, taking two steps back from it to be extra safe, and self-censoring accordingly.
Since we never knew when Snow was guessing about what might be off-limits, and when her comments stemmed from real political directives from above, every correction spawned wild conspiracy theories around the water cooler. One month, we ran a short news brief with figures on the number of mainland Chinese tourists that had visited the United States in 2007, and Snow flagged the number for deletion. We wondered what dirt we had unwittingly stumbled upon. Which government bureau oversaw tourism figures? What were they hiding? Finally, I called Snow, and learned that the numbers we had cited were for the number of Chinese tourists worldwide, not just in the United States.
So much for the would-be plot. Chagrined, I had to announce to my colleagues that we'd made a mistake.
We knew we were lucky to have the censor that we did, if we had to have one at all. Snow was patient with our push-backs, and, though she didn't have to, often went to great lengths to explain the "why" of her changes. When we wanted to run a piece that was somewhat critical of China's healthcare system, Snow spent days poring through it, typing up lengthy explanations for how we could rearrange the piece to pass muster. The changes were surprisingly minor. She reworded the subtitle "China's ailing healthcare system — and the government's plan to fix it" to "The Chinese government's plan to fix the ailing healthcare system." She replaced pull quotes (excerpts from the stories displayed in larger text to the side of the article), pointing to flaws in the system, like "High medical expenditure is the main cause of poverty in China in 30 percent of cases," with more positive ones that highlighted ways China was working to reform the system – "Reform of the healthcare system has been at the top of the political agenda for some time." But Snow allowed the more critical statements to remain within the body of the article itself.
She explained that we had to be careful not to offend anyone at the Ministry of Health, but also that nobody at any ministry was likely to ever read the piece. We just had to make sure there was nothing potentially offensive in large print (i.e. the headlines and pull quotes) or in the opening paragraphs that someone important might skim in passing. We made her changes, and ran the piece.
Snow even helped us with our research. When we wanted to write about something she felt was sensitive but doable — for example, a piece suggesting that tourism figures during the Olympics would be lower than expected due to tightened visa restrictions — she provided figures from official state media. "This way," she wrote, it "guarantees we won't make a mistake — even if we're wrong, it's following their error, and we won't be directly responsible."
Her reactions also provided a reliable marker of the political touchiness of an issue. One article about skyrocketing food prices around the world quoted economists saying that the rising quality of life in China, and the attendant increase in meat consumption, might play a role, because more arable land was being used to grow feed for animals. Snow called me to relate her changes, and grew so angry over that particular line suggesting a link between Chinese meat-eaters and worldwide food prices that she began to shriek, "Are they all vegetarian in the West? No! So many fat people in America, and they dare to say this is China's responsibility?"
Sometimes, just when we felt this was all a joke and had convinced ourselves that the censor changes were no big deal, something truly dispiriting would happen. A column titled "Why Joint Ventures Fail in China" got axed. The subtitle in a piece that mentioned a foreign company's failed attempt to buy a stake in a Chinese steelmaker — "Interest from abroad stymied" — was changed to "Interest from abroad still high." Occasionally, Snow would send something back with none of her colorful commentary or explanations, and simply write: "Wrong opinion."
After I became managing editor, though, and without particularly meaning to, I somehow won Snow's heart. I asked her for the contact info of someone I had assumed was a freelancer; Snow explained he was actually a high-ranking official at the Ministry of Commerce, who'd been contributing as a favor to her. My predecessor, Guilford, I learned, had once double-bylined one of the official's articles with one of our own reporters, and without thinking about it had listed that reporter's name first. Snow said her boss and her colleagues reprimanded her, and she had to write a self-criticism as punishment. We'd merely been listing the writers' names in alphabetical order, but I wrote back apologizing for the misunderstanding.
Until then we'd almost always communicated in English, because Snow's English was much better than my Chinese; but now she responded in Chinese saying she knew I hadn't been involved with that incident. She followed this up with a phone call, congratulating me on my new role. Her voice dropped to a conspiratorial hush and she added, "To tell the truth, I do not think [your predecessor] is a very good editor. I think you are much better, because you are Chinese. You can understand China, and why we must do things the way we do, because of your Chinese blood."
I was not sure how to take this. The implication to me seemed to be that, because I was of Chinese extraction, I would accept censorship more readily than my (white) predecessor had. Whatever her meaning, from that point onwards, I found myself in the odd position of having acquired an ally who was a censor for the Chinese government.
This was not the relationship I wanted to have with Snow. I believed in free speech. I'd spent a summer interning at the ACLU. I was beginning to question the morality of my paycheck, of playing any part, no matter how incidental, in a system of which I disapproved. Thinking of her as my adversary allowed me to feel I was fighting the system. But my adversary wanted to be friends.
She started to call more, and email less, about changes, then wanted to chat on the phone. She loved spicy food, Snow told me. Her husband was often away on business trips. I never figured out what he did, but it often seemed to involve playing golf, or wining and dining Japanese clients. She missed her old neighborhood up near where the "Bird's Nest" Olympic stadium now stood, but had moved to the west side to be closer to her son's school.
Sometimes, when the issue was running late, I took a cab to deliver the layouts to Snow myself. I'd meet her outside her son's swimming lessons or his weekend "Olympic math" tutoring, and she would prattle: Her son was taking $22-an-hour drum lessons. She'd gotten a $30 parking ticket the last time she drove, so now they took taxis, which were $5 each way. He always wanted McDonald's afterwards, so that was another five bucks. She was tempted to halt the lessons, but she had heard that music improved academic performance.
"The world is getting more and more competitive," she would sigh. "It takes so much work just to keep up, to make sure your child will be able to keep up."
Stars are definitely not okay either, please please do not take the risk.
I once published, in a newspaper, a picture of a book put out by the German embassy, introducing China and Germany's investment cooperation. The book's cover had a big stream on it, half of it the colors of the German flag, half of it red with yellow stars. I decided since it wasn't a flag it was okay, and sent it to print. Our newspaper office was slapped with a fine of 180,000 yuan [today, around $28,000] and I had to write a self-criticism and take a big salary cut.
Quite a lesson, yes? Sigh — we must remember it well.
Another time, in the fall of 2008, my phone rang and I picked up to find Snow in an excitable mood.
"Hi Snow," I said, trying to sound distant and professional.
"Are you busy?" she asked.
"Well, actually, I am a little…"
"Oh good. I was thinking: December will be the 30th anniversary of 'Reform and Opening.' It will be a big deal and there will be many celebrations in the media. Are you planning any articles about this topic? Because, I think, maybe you can interview people about their experiences from 30 years ago. Like me, for example — when I was young, we did not have meat to eat. And we lived in a building with many other families, and we had only had one phone for the whole building. If it rang, someone would answer it and shout your name. In those days, it was always for me. The man who answered the phone would yell, 'Snow, the phone is for you again!'" She laughed delightedly.
On a few occasions, Snow asked me to lunch, and I always said no. Keeping my distance became easier as the year progressed and my disillusionment increased. Media restrictions began to tighten severely in the wake of pro-Tibet protests that were following the Olympic torch around the globe. China had naïvely been caught off guard by the expressions of anti-Chinese-government sentiment, and had reacted strongly. Visa regulations tightened, and many younger expats who did not meet the new work experience requirements had to leave the country. The June issue of the English-language version of Time Out Beijing was, due to a licensing technicality (it did not have its own separate publishing license but was piggybacked onto the license of their Chinese-language edition), abruptly pulled from the presses, though their license structure had never been an issue before. And the changes at our magazine, which had always seemed generally comprehensible and rooted in logic even when I disagreed with them, veered into the realm of absurdity.
I was told that we could not title a coal piece "Power Failure" because the word "failure" in bold print so close to the Olympics would make people think of the Olympics being a failure. The title "The Agony and the Ecstasy" for a soccer piece was axed because agony was a negative word and we couldn't have negative words be associated with sports. We couldn't use the cover image I had picked out for a feature on the rise of chain restaurants, because it was of an empty bowl, and, Snow told me, it would make people think of being hungry and remind them of the Great Famine (a period from 1958 to 1961 when tens of millions of Chinese starved to death, discussion of which is still suppressed). Even our Chinese designers began to roll their eyes when I related this change to them, and set them to work looking for images of bowls overflowing with meat.
Finally, in July 2008, one month before the Olympics, I gave my notice and, knowing I might never see her again, accepted one of Snow's invitations. She picked me up from my apartment, and drove us across town to her favorite restaurant, Haidilao, a Sichuan hotpot chain. She complained about Beijing's terrible traffic, which I had somehow thought a censor wouldn't do, because it constituted criticism of the government.
A car cut her off, and she shook her head angrily, and exclaimed, "Look at this! They won't let me pass even though they can see I was in front. See, this is how Chinese people are." She asked me if this would happen in the United States. I said yes. "Really?" she replied. "I imagine in the United States everyone obeys the traffic rules. People are not so backwards there. That's what I hear."
Over lunch, she asked me about my plans. How would I support myself? I said I wanted to try freelance writing. If it didn't work out, I'd start looking for a new full-time job. I might move back to the United States, or maybe to a new country.
"Ah, you young people," she said. "So much freedom to do what you want. To tell you the truth, I would love to change my job too. But I can't — I have a family, I've been there too long, it's not the same for us old people."
She leaned forward, and looked intently into my eyes. "Have you ever considered opening your own research firm for foreign companies that want to invest in China? You would be very good at this, because you are Chinese, too. Even though you are born in America, you understand our Chinese thinking. You can be a big important consultant. And then you can hire me so I don't have to work at my job anymore. I'm serious — think of me if you ever do this someday. You should. And then you can hire me."
She was speaking lightly, and laughing, but she also seemed to mean it, and I suddenly wondered if this was the purpose of our lunch. I found this idea utterly depressing. I was a lost, aimless kid, drifting around China, and yet this older woman could look at me and see the possibility, however tenuous, of a lifeline.
I understood then the mundane nature of all that kept her in place. A job she didn't like, but worked hard to keep. A system that would never reward her for good work, only punish her for mistakes. And in exchange: Tutors. Traffic. Expensive drumming lessons. They were the same things that kept anyone, anywhere, in place — and it was the very ordinariness of these things that made them intractable.
After lunch, Snow asked me if I'd seen the Olympic stadium yet, and I said I hadn't, so she turned north to drive by it. A road was blocked off, and a traffic cop in a neon yellow vest waved us towards an alternate route. Snow remarked that with the Olympics imminent, the streets of Beijing resembled the United States, with cops everywhere. "In U.S. movies," she said, "whenever a crime happens, the cops always show up immediately. Is that true? Are they really so fast?" I said that I wasn't sure whether American cops arrived at crime scenes more quickly than Chinese cops, but that they definitely weren't as fast as they seemed in movies.
When the Bird's Nest stadium loomed into view, I murmured "Wow." I had been editing blurbs about the thing for so long, it had never occurred to me that I would be impressed by it in person, but I was.
Snow asked if she could drop me off at the nearest subway stop. I said it was no problem, and as we turned I asked how many siblings her husband had. She had been complaining towards the end of lunch that she and her husband had to support them.
"Twelve, but half of them died. So there are six of them, total."
"I'm sorry to hear that," I said.
"Oh don't be. It was like that for everyone back then. Because you know, Mao had probably gone crazy, and encouraged everyone to be a 'hero mother' by having five kids. They say that's what caused the famine. But Mao was crazy and…"
She broke off and laughed.
"You see," she said, "we can say this here, just you and me; we just can't say it in print." Then, suddenly, switching to English, she exclaimed, "That's China!"
We had reached the subway stop. I got out, and said goodbye, and then she went to get her son.


As a long time and vocal supporter of Podio, I was disappointed to read this blog post and related information that indicates Podio will soon be gone as we know it.
"I wanted to let you know that we are going to be reducing investment in Podio as a standalone product, and we will leverage the technology in other existing product. It will not be offered as a standalone product moving forward."
"reducing investment in Podio". We're not putting any more time or money into Podio.
"leverage the technology". We're using the code in our other brands.
Now as sad as I am about this, this is their right to do and obviously they don't feel Podio is worth pursuing any more.
I would seriously suggest those of you who use Podio as a fundamental part of your business, download all of your data ASAP and look for a solution that is as close to Podio as possible.
Please let me be clear, Podio will still be offered as a stand-alone product going forward. We will continue to support our current customers, future customers and improve the platform.
I can't believe I had to learn this terrible news in this way. What am I supposed to do without Podio, no other project management system even comes close.
You don't need to worry, Podio is still here and will continue to be here in the future.
Let me know directly via sara@podio.com if I can help.
Unfortunately direct quotes from Citrix executives contradict you. I can only hope that your little hint of news and a smile means there are plans underway for Podio to be purchased back by your original team and keep going. I really hope so because I love Podio and replacing it is something I do not want to do.
Brett, I hope you will still follow our blog and receive future updates with more detailed communication and confirmation on our investment in Podio.
Hi Sarah, thanks for your strong statements of support for the continued availability of Podio. And, I'm also very pleased to see the new features announced recently (though these were all under development prior to the above Citrix announcement, so their release doesn't do as much to alleviate my fears as I would like). I am praying there will be some more concrete statements forthcoming which will either retract or clarify the above mentioned "will not be offered as a standalone product" statement. As for myself, Citrix has already pulled the rug out from under me before. We were very happy users of a product called Hidef Conferencing (a teleconferencing service) until it was acquired by Citrix just so they could use the developers on gotomeeting. So, when Citrix felt they had milked Hidef Conferencing for what they could milk out of it as a standalone product, they just shut it down and told us to go to gotomeeting at 4x the price. When Citrix acquired Podio, I wanted to believe that they actually wanted to leverage the truly innovative Podio product to open up a new market opportunity. But, the recent announcement combined with our past experience with Citrix gives me much doubt. When your customers are recommending other products on your own community forums it is a horrible sign of the state of your customer base. If the passengers think the ship isn't sea worthy, it doesn't really matter what the crew thinks unless they can convince the passengers otherwise.


text:
How Gerald was supported to go on holiday for the first time
Sophie Cooper
Web & Digital Media Editor
My name is Jean and I'm a support worker for United Response. Just over a year ago we met Gerald, who came to our service in Salford as an emergency placement. At that time he was very anxious and had no confidence.
Gerald loved his films and had lots of DVDs which he liked watching. I asked, "Have you ever been to the cinema to watch a film?" He replied, "No, I have never been to a cinema."
The first trip to the cinema
Over the next few weeks we started to look at new films that were coming out at the cinema. He chose one and I arranged to take him to see it. He thoroughly enjoyed the experience, so when we arrived back at the house I applied online for a CEA cinema card which entitles the carer to go with him for free.
It took about two weeks for the card to arrive and as soon as it did the gentleman asked, "When can we go to the cinema?" He continues to go on a regular basis now - every week and sometimes twice a week for the last 10 months. Every time he goes he always thanks staff for taking him, telling us he thoroughly enjoyed himself.
Going to Blackpool
Gerald had never gone through with a holiday which had been booked in the past. I had a conversation with him and we started to look at places where he could go with support workers - he chose to go to Blackpool for four days. He helped to choose the hotel and we also discussed what he could do whilst he was away. We found something called the 'Blackpool Big Ticket' which lets you get in to seven Blackpool attractions - he decided he would like one of these.
We got all the details of what needed booking, how much it was, as well as spending money, new clothes and shoes. We then sent off a Purchase Approval for his first holiday. Gerald was really excited during the weeks leading up to it. Every time a new support worker came on shift, he kept asking, "Are we going to Blackpool today?"
He had the biggest smile
Eventually, we supported him to go to Blackpool and he enjoyed all the attractions. He did plenty of walking, and enjoyed the entertainment at the hotel each night. We also booked a magic show whilst we were there, and he went to the circus - all of which he thoroughly enjoyed.
Each night before he went to his room he would thank us for taking him on holiday and for making good memories that he can enjoy and tell people about. We took lots of pictures at all the places he visited and on every picture he had the biggest smile. He also bought a box of fudge for the people he lives with as a gift.
When we got home we took Gerald to get all his photographs printed off and he also bought an album to put them in. We then helped him put the photographs in an album.
A massive achievement
The following week he went back to his day centre and took the album to show the support workers there. They told us well done, as this is a massive achievement for him.
Gerald takes his album to all the places he goes to. He still smiles every time he looks at it multiple times a day. He is now planning his holiday for next year. He said he likes boats so he might want to go on a cruise. Here's to the next exciting holiday for Gerald and all the memories he will make on them!
Written by Jean Marshall, Support Worker at United Response.
Find out how we support people in Greater Manchester
People we support
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New figures reveal that a third of those with learning disabilities or autism fear being victimised in a new job.
CQC's shocking interim report 'exposes horrific experiences' endured by far too many people with learning disabilities or autism
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New Children's Commissioner report 'lays bare catastrophic failures in the health and social care system'
Today's new report from the Children's Commissioner shows that too many children are being unnecessarily admitted to hospital, spending years away from home when they should be growing up in the community.
Kate's graphic design talent lands local freelance work
Kate, a person we support in York, has landed some freelance graphic design work with a local theatre company. Find out how she was supported to learn the skill.
Wiltshire man celebrates anniversary of book release
Ben, a person we support who has autism, has celebrated the one year anniversary of the release of his children's book 'Chirp and Find'.
York group discover ancient artefacts in archaeological dig
People we support at the Boot Shop in Easingwold, York got their hands dirty and took part in a two day archaeological dig in September and October.
Young people with disabilities become radio DJs in Manchester
Young people with disabilities hosted their own radio show on Manchester's All FM 96.9.
Zoo day brings confidence to people in Ipswich
A bunch of exotic wildlife came to the sunny back garden of people we support in Ipswich for a special zoo day experience.
Therapy ponies visit Darlington to help improve the wellbeing of people we support
When a Service Manager in Darlington heard that someone she supports loved ponies as a child, she arranged for therapy ponies to visit. Little did she know the little horses would brighten up the lives of many others.

redpajama_set_name: RedPajamaCommonCrawl


Prior to joining the faculty of Oklahoma State University in 2018, Dr. Labrecque was a Postdoctoral Research Associate at the University of Southern California in the Department of Preventive Medicine at Keck School of Medicine. In collaboration with Dr. Kayla de la Haye (PI) and as part of a MURI team, she investigated how individuals' social intelligence and their emergent social networks predict group task performance. Jen completed her Ph.D. in social psychology in 2015 at the University of Southern California in the Dornsife College of Letters, Arts, and Sciences. In her graduate work, along with her advisor Dr. Wendy Wood, Jen studied behavior change challenges with a focus on habits. She continues to remain engaged in this area, currently focusing on the interplay between goals and habits.


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View of Partitional Clustering of Underdeveloped Area Infrastructure with Unsupervised Learning Approach: A Case Study in the Island of Java, Indonesia
ISSN 0853-9847 print/ 2442-3866 online © 20XX ITB, ASPI dan IAP
Partitional Clustering of Underdeveloped Area Infrastructure with Unsupervised Learning Approach: A Case Study in the Island of Java, Indonesia
Bambang Widjanarko Otok
, Agus Suharsono
, Purhadi
, Rahmawati Erma Standsyah
, Harun Al Azies
[Received: 08 September 2021; 1st revision: 19 May 2022; accepted in final version: 12 June 2022]
Abstract. This study attempted to identify underdeveloped areas in regencies/cities on the island of Java, Indonesia, based on a number of infrastructure indicators. An unsupervised learning approach was used to perform partition clustering with the K-Means, K-Medoids, and CLARA methods. In addition to technically obtaining clustering results and conducting a performance comparison of the three unsupervised learning methods, another objective of this research was to map the clustering results to make it easier to recognize the characteristics of the regions indicated as underdeveloped areas, which should be absolute priorities for infrastructure development. It was found that the best clustering method was the CLARA method, with a connectivity coefficient of 7.4794 and a Dunn's index value of 0.1042. The partition clustering of regencies/cities on Java Island using the CLARA method based on infrastructure indicators resulted in 99 regencies/cities included in the cluster of areas with underdeveloped infrastructure, while 12 regencies/cities were included in the cluster of areas with developing infrastructure, and 8 regencies/cities were included in the cluster of areas with developed infrastructure.
Keywords. CLARA clustering, infrastructure, underdeveloped areas, unsupervised learning.
Abstrak. Penelitian ini mencoba mengidentifikasi daerah tertinggal di kabupaten/kota di pulau Jawa, Indonesia, berdasarkan sejumlah indikator infrastruktur. Pendekatan unsupervised learning digunakan untuk melakukan klasterisasi dengan metode K-Means, K-Medoids, dan CLARA. Selain memperoleh hasil klasterisasi secara teknis dan melakukan perbandingan kinerja ketiga metode pembelajaran unsupervised learning, tujuan lain dari penelitian ini adalah untuk memetakan hasil klasterisasi agar lebih mudah dalam mengenali karakteristik daerah yang terindikasi sebagai daerah tertinggal, yang seharusnya mutlak. prioritas pembangunan infrastruktur. Ditemukan bahwa metode clustering terbaik adalah metode CLARA, dengan koefisien konektivitas 7,4794 dan nilai indeks Dunn 0,1042. Pengelompokan sekat kabupaten/kota di Pulau Jawa dengan metode CLARA berdasarkan indikator infrastruktur menghasilkan 99 kabupaten/kota yang termasuk dalam klaster daerah dengan infrastruktur tertinggal, sedangkan 12 kabupaten/kota termasuk dalam klaster daerah dengan infrastruktur berkembang, dan 8 kabupaten/kota termasuk dalam klaster daerah yang infrastrukturnya berkembang.
1 Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya, 60111, Indonesia. Email: dr.otok.bw@gmail.com or bambang_wo@statistika.its.ac.id (*Corresponding Author)
Kata kunci. CLARA clustering, daerah tertinggal, infrastruktur, unsupervised learning.
The inequality between rural and urban areas has resulted in the emergence of poor and underdeveloped areas. The development of an area within a region cannot take place uniformly, which is due to many factors, both natural (geography, human resources, natural resources) and artificial (including infrastructure and social resources), that are not evenly distributed throughout the region (Otok et al., 2021). Apart from causing inequalities in development, this condition can also be one of the causes of the emergence of underdeveloped areas. In Indonesia, as a developing country, the problem of development inequalities is one of the government's work priorities, as regulated in Presidential Regulation (Perpres) Nr. 131 of 2015 and the National Development Plan Medium Term 2015-2019 (RPJMN), which states that there are 122 backward regions in Indonesia, six of which are in Java (Al Azies, 2021).
The Ministry of Villages, Development of Disadvantaged Regions, and Transmigration (KNPDT) categorizes underdeveloped areas in four groups, namely, slightly underdeveloped; underdeveloped; very underdeveloped; and severely underdeveloped;
The grouping is based on the weighted average of six measured indicators: community economy, human resources, infrastructure, local financial capacity (budget gap), accessibility, and regional characteristics. One of the weaknesses of the weighted average is the subjective determination of the weight assigned to each indicator (Al Azies, 2022).
In addition, the KNPDT data having many outliers affects the grouping results, and a large amount of data also affects the grouping results.
The present study used cluster analysis to identify, classify and map underdeveloped areas on the island of Java. To overcome large datasets ('big data'), they can be analyzed with various techniques, one of which is unsupervised learning using a clustering algorithm (Sidey-Gibbons, 2019). Therefore, in this study, clustering was used, specifically partition clustering. The method used makes an initial partition k at the beginning, where the parameter k is the number of partitions to be made. Several popular partitioning clustering methods were compared in this research, i.e., K-Means, K-Medoids, and CLARA.
Several other studies related to the comparison of partitional clustering methods have been conducted. For example, the research of Gupta (2019), who compared K-Means clustering and CLARA clustering on the Iris Dataset. The conclusion was that the CLARA method had better clustering results because it is sensitive to outliers. In addition, Arorra (2016) found that the time required to select the cluster centroid point and the complexity of the overlapping cluster space is much shorter using K-Medoids compared to K-Means in big data analysis. Martin (2015) studied the performance of GMM compared to other methods, which resulted in the conclusion that a mixed growth model has better performance than simple clustering to study longitudinal heterogeneity. Otok et al. (2021) conducted research related to underdeveloped areas by modeling factors that affect infrastructure in Java using Meta-Analysis Structural Equation Modeling
(MASEM) (Cheung, 2013) with three indicators, namely accessibility, regional characteristics, and economy. Therefore, using an unsupervised learning approach with partitional clustering for the problem of identifying underdeveloped areas in Indonesia is suitable. In addition, Otok et al. (2020) used the meta-CFA approach to investigate factors that affect underdeveloped areas on the island of Java.
A novelty in this study consisted of grouping the identified underdeveloped areas of all regencies/cities on Java based on several infrastructure indicators.The reason for using a partitional (non-hierarchical) clustering method was that it deals better with large data sets compared to hierarchical clustering methods (Nakayama, 2020). Another advantage is that it can perform analyses of larger samples more efficiently (Govender, 2020).
However, it has some weaknesses related to outliers (Violán et al., 2018), the distance measure used (Park et al., 2018), and irrelevant or inappropriate variables (Violán et al., 2018).
The objectives of this study were: (1) to evaluate the clustering performance of three unsupervised learning methods, (2) to know the characteristics of each region on Java Island identified as underdeveloped based on several basic infrastructure indicators, (3) to map the clustering results so that they can be used as a reference for the government in determining policy directions to overcome the problems of these underdeveloped areas, which should be the main priorities for infrastructure development.
The rest of this paper is divided into the following parts. Section 2 describes the methodology used, including a description of the concept of the clustering algorithm as well as the data sources and research variables used in the study. Section 3 explains the experiment that was carried out and the results that were obtained. Finally, Section 4 concludes the study.
Data Sources and Research Variables
This study used a data set derived from the Statistics Indonesia database. Two data sets, were used, both of which contained secondary data: (1) a dataset collected from the 2014 edition of the Village Potential Data Collection (PODES), which is data and information related to the existence of infrastructure and the potential possessed by each village-level administrative area in Indonesia, and (2) a data set collected from the 2014 edition of the National Socio-Economic Survey (SUSENAS), which is data on various socio-economic aspects and the fulfillment of life needs such as clothing, food, housing, education, health, security, and employment. The results of the aggregation of these two data sets consisted of 119 observations representing the number of regencies/cities on the island of Java, where each regency/city had nine features or variables to represent the infrastructure conditions.
Table 1. Research Variables
Variable Unit
Asphalt/concrete roads (X1) Number of villages Paved roads (X2) Number of villages Dirt roads (X3) Number of villages Other roads (X4) Number of villages Markets without building (X5) Number of villages Health facilities (X6) Number of units Health workers (X7) Number of persons Basic education facilities (X8) Number of units Telephone user households (X9) Number of households
The distribution of regencies/cities in each province on the island of Java is as follows:
Capital Special Region of Jakarta Province (6 cities), West Java Province (27 regencies/cities), Central Java Province (35 regencies/cities), Special Region of Yogyakarta Province (5 regencies/cities), East Java (38 regencies/cities), cities) and Banten (8 regencies/cities). Figure 1 below visualizes the study area in this research, namely the Island of Java, Indonesia, which consists of six provinces, where each province consists of several regencies/cities as described in the previous explanation.
Figure 1. Java Island study area. (Source: authors, 2022)
As mentioned above, the level of development of regions is assessed by the Indonesian government using five dimensions, namely, economy, human resources, infrastructure/infrastructure, accessibility, and regional characteristics. This study focused on the infrastructure dimension using nine constituent indicators, as described in Table 1. These nine indicators were based on the indicators used by the Ministry of Villages, Development of Disadvantaged Regions, and Transmigration (KNPDT) to determine the underdeveloped status of a region.
Unsupervised learning and clustering algorithms
Supervised learning and unsupervised learning are the two main types of machine learning techniques. Supervised learning uses a known data set (training data set) to make predictions (Trishnanti and Al Azies, 2019). This research used unsupervised learning, which uses a machine learning algorithm to analyze data sets made up of labeled responses of input data. The most common unsupervised learning method is cluster analysis. Clustering algorithms can be broadly divided into two groups, namely hierarchical and partitional (non-hierarchical). The partition method first sets the variable k, where k is the number of partitions to be formed. Then, it iteratively uses relocation techniques, repeatedly trying to move objects from one group to another, to achieve the optimal partitioning. Examples of this type of method are K-Means, K-Medoids, and CLARA (Han et al., 2011).
The K-means algorithm aims to divide data into several clusters. This algorithm accepts data without class labels as input (unsupervised learning). The algorithm for performing K-Means clustering is (Likas et al., 2003):
1. Determine the value of k as the number of clusters to be formed.
2. Determine the initial value of the centroid, or the center point, of the cluster. At this point, the centroid value is randomly determined for the next step, using the following equation:

= Ni
k kj
ij X
Vij : i-th centroid cluster for the j-th variable N : number of data in the i-th cluster i
i, : cluster index
j : index of the j-th variable
Xkj : value of the k-th data in the cluster for the j-th variable
3. The calculation of the distance between the centroid and the point of each object can be done using the Euclidean distance with the following equation:
= n
jk ik y
e x x
( (2)
) , (xy
de : the distance between the i-th and j-th objects.
n : number of data in the i-th cluster x ik : the i-th data object out of k data.
xjk : the j-th data object in the k-th data, or what is often called the centroid (value of the midpoint) cluster for the number of data n
4. Group the data to form k clusters with the centroid point of each cluster being the closest centroid point. The determination of the cluster members must consider the minimum distance from the object.
5. Update the centroid value of each cluster.
6. Repeat Step 2 to the end until the centroid value no longer changes.
K-Medoid clustering
The K-Medoid method is similar to the K-Means method. Both include a partitioning method, which is a method of grouping data into multiple clusters without any hierarchical structure between them. The K-Means algorithm and the K-Medoids algorithm determine the center of the cluster in a different way. The K-Means algorithm uses the mean value (means) in each cluster, while K-Medoids uses data objects as representations (medoids) (Kaur et al., 2014). K-Medoids has the advantage of being more robust against outliers compared to K-Means, which is not robust against outliers because these are usually far/very far from the majority of the other data of the cluster's mean (Han, 2006). The K-Medoids algorithm is executed using the following steps:
1. Initialize k cluster centers (k = number of clusters).
2. Calculate the nearest cluster for each object using the Euclidean distance with Equation (2).
3. After calculating the Euclidean distance, initialize new random cluster centers for each object as non-medoid candidates.
4. Calculate the distance of each object that is in each group with the non-medoid candidates.
5. Calculate the total deviation (S) by subtracting the old total distance from the new total distance. If S < 0, swap the objects with non-medoid cluster data to form a new set of k objects as medoids.
6. Repeat Steps 3 through 5 until there is no change in the medoids so that the clusters and their respective cluster members can be obtained.
Clustering Large Applications (CLARA) Clustering
Clustering Large Applications (CLARA) is a clustering method that is a development of the working method of K-Medoids and uses sampling to manage large data sets combined with the PAM method developed by Kaufman and Rousseeuw (Schubert and Rousseeuw, 2021). Unlike other medoid methods such as PAM, CLARA is resistant to outliers and can be used on large amounts of data. CLARA is more efficient in terms of computation time and storage when handling large data sets. The CLARA algorithm is executed using the following steps:
1. Determine the number of clusters (k), then randomly divide the data set into several subsets of fixed size.
2. Determine the initial medoid.
3. Calculate the distance of non-medoid objects to the medoid of each cluster.
4. Place objects according to the distance closest to the medoid.
5. Calculate the total distance obtained.
6. Randomly select non-medoid objects in each cluster as new medoid candidates.
7. Calculate the distance of each non-medoid object to the new medoid candidates and place the objects based on the distance closest to the new medoid.
8. Calculate the difference between the medoid candidate's total distance for the new medoid and the total distance for the old medoid. If the total distance of each object to the new candidate medoid is smaller than the total distance of each object to the old medoid, then the candidate medoid becomes the new medoid.
9. Repeat Steps 6 to 8.
10. Calculate the distance between all non-medoid objects and objects are medoids until the subset with the smallest number is selected.
Gap Statistics
One method to determine the optimal number of clusters in the clustering process is the gap statistics method (Clayman et al., 2020). This method displays a graph to determine the optimal number of clusters. The gap statistics is the most consistent measure for determining the number of clusters relative to other measures (Yang et al., 2019). The distance between paired objects in a cluster is defined as:
' i i
r d
where d is the square of the Euclidean distance. The number of squares in a cluster is formulated as follows:
= k
W n
The gap value is obtained by estimating the optimum number of clusters in the Wk standardization approach:
 ( )
 ( )
n k E W W
Gap =  log −log (5)
where En is the expected value of the distribution of the number of samples. The criterion of the optimal number of clusters is given by the gap statistics value (k), which is the highest or the first to indicate a minimal gap increase when the gap is still increasing (Yang et al., 2019).
Cluster evaluation
Evaluation of the clusters consists of external and internal evaluations, where external evaluation can be done when the cluster has a label (supervised). However, the present study used only internal evaluation because the data used were not labeled. Internal evaluation uses internal information on the data to assess the clustering results, such as the density, relationship, and separation of the clusters. The internal evaluation criteria used here were the silhouette coefficient, Dunn's index, and the connectivity coefficient (Brock et al., 2008):
a. The silhouette coefficient is a measure of the degree of confidence in grouping an observation with a cluster. The formed cluster will be classified as 'good' if the resulting coefficient is close to 1 and as 'bad' if the coefficient is close to -1 (Monica et al., 2021).
b. Dunn's index is a validation measure of clustering results obtained by measuring the distance between two clusters and the diameter of the cluster. Dunn's index is the ratio between the greatest distance between two clusters and the smallest distance in a cluster. A formed cluster is better when the Dunn's index value is higher (Brock et al., 2008).
c. The connectivity coefficient is related to the cluster homogeneity, typically assessed using intra-cluster variance. This relationship shows the position of the observational data, called the nearest neighbor, in a cluster. The connectivity coefficient is used to measure the density value. The connectivity value is between zero and ∞. A formed cluster is better when the value of the connectivity coefficient is lower (Brock et al., 2008).
Results and Discussion Descriptive Statistics
In general, the data distribution of the nine indicators showed many outliers. Figure 2 presents a grid diagram of the infrastructure indicators consisting of the nine research variables. All nine variables had extreme outliers. These outliers show that infrastructure indicators in some regions are still important. In the regional characteristics indicator, the diversity of each variable was roughly the same, except for the variables X4 (number of villages with 'other' roads) and X9 (number of telephone user households), whose diversity was slightly narrower than the other variables. In this case, the outliers were always included in the next analysis because the clustering method used (CLARA) is robust against outliers, so the presence of outliers will not affect the result.
Figure 2. Boxplot of each research variable (Source: authors, 2021).
In addition, Table 2 provides descriptive statistics on the varying state of road infrastructure (X1-X4) in Java, Indoesia.
Table 2. Descriptive Statistics of Road Infrastructure Condition Variables (X1-X4)
Variable Mean Variance Minimum Maximum
Value Regency/City Value Regency/City Asphalt/
concrete roads (X1)
3,326.50 6,715,380.66 12,950 Semarang City 0 Kep. Seribu Regency Paved roads
(X2) 257.64 150,868.16 2,268 Ngawi Regency 0 34 area Dirt roads (X3) 13.01 885.03 189 Ngawi Regency 0 77 area
Other roads
(X4) 0.45 8.56 29 Sumenep
Regency 0 113 area
Based on Table 2, the following is an explanation of each of these variables:
a. Descriptive statistics of the Asphalt/concrete roads variable (X1)
The average value of the Asphalt/concrete roads variable (X1) was 3,326.504, or 3,327 villages had asphalt or concrete roads with a variance of 6,715.381. The area of Semarang City in Central Java Province had the highest number of villages with paved roads, i.e., 12,950 villages. Meanwhile, Kepulauan Seribu Regency in the Capital Special Region of Jakarta Province had no villages with asphalt/concrete roads.
b. Descriptive statistics of the Paved roads variable (X2)
The average value of the Paved roads indicator (X2) was 257.638, or 258 villages had paved roads. Ngawi Regency in East Java Province had the highest number of villages with paved roads, while 34 regencies/cities had no villages with paved roads.
c. Descriptive statistics of the Dirt roads variable (X3)
Ngawi Regency also had the highest number of villages with dirt roads (X3).
Meanwhile, 77 regencies/cities had no villages with dirt roads.
d. Descriptive statistics of the Other roads variable (X4)
Sumenep Regency in East Java Province had the highest number of villages with
'Other' roads (X4), namely 29 villages. The remaining 113 regencies/cities out of 119 regencies/cities did not have villages with 'other' roads.
In addition to the descriptive statistics on road conditions in areas on Java Island, Table 3 presents descriptive statistics on other infrastructure conditions, such as infrastructure for the education, health, technology, and economic sectors (X5-X9).
Table 3. Descriptive Statistics of Each Research Variable on Infrastructure Conditions of Various Sectors
Value Regency/City Value Regency/City Markets
without building (X5)
40.39 2810.39 404 Tangrang
Regency 0 Banjar City Health
facilities (X6) 2,308.06 1,554,035.85 6,932 Bogor
Regency 47 Kep. Seribu Regency Health
workers (X7) 694.57 166,560.82 2,288 Surabaya City 44 Kep. Seribu Regency Basic
education facilities (X8)
715.03 162,403.08 2,310 Bogor
Regency 15 Kep. Seribu Regency Telephone
user households
24,022.49 2,162,469,751.12 253,308 North Jakarta
City 0 Kep. Seribu
The following is an explanation of Table 3, which contains descriptive statistics of each variable that reflects the state of the infrastructure for various sectors on Java (X5-X9).
a. Descriptive statistics of the Markets without buildings variable (X5)
Tangerang Regency, Banten Province, had the largest number of villages with markets without buildings (X5), namely, 404 villages in Tangerang Regency. Meanwhile, the town of Banjar in the province of West Java is an area that has no markets without building.
b. Descriptive statistics of the Health facilities variable (X6)
The sixth indicator is Health facilities (X6). The average number of health facilities in Java was 2,308,058, or 2,309 units, with the Bogor Regency in West Java Province had the most health facilities in Java, i.e., 6,932 units. The regency of Kepulauan Seribu had the fewest health establishments, i.e., only 47 health facilities.
c. Descriptive statistics of the Health workers variable (X7)
The average number of health workers (X7) in Java was 694.57, or 695 people.
Surabaya City in East Java province had the highest number of health workers, i.e., 2,288, while Kepulauan Seribu Regency had the lowest number of health workers, i.e., only 44, which is logical given that this region also had the fewest health facilities.
d. Descriptive statistics of the Basic education facilities variable (X8)
Bogor Regency had the largest number of basic education institutions (X8), namely 2,310 units.
e. Descriptive statistics of the Telephone user households variable (X9)
In terms of the indicator of households using a telephone (X9), two areas of the Capital Special Region of Jakarta Province stood out. The city of North Jakarta had the highest number of households using the telephone in Java. Meanwhile, another area in the Capital Special Region of Jakarta Province had the lowest number of households using
the telephone, namely Kepulauan Seribu Regency, where none of the households in this area uses the telephone.
Gap Statistics Value Validation
The purpose of gap statistics is to determine the number of clusters, i.e., to determine the optimal value of k. The criterion of the optimal number of clusters is given by the statistical deviation value that is the highest or the first to indicate a minimal increase in deviation while the deviation is still increasing (Yang et al., 2019). The results of the validation are shown in Figure 3.
Figure 3. Graph of gap statistics value for number of clusters k (Source: authors, 2021) Figure 3 shows an example of finding the optimal value of k with a statistical deviation approach. From these results, the recommended value of k is k = 3, which has the highest deviation value compared to the other cluster points. Therefore, the recommended number of clusters from both methods is 3.
Selecting the best clustering method
Before determining the most optimal cluster method, the next step in this research consisted of clustering with the K-Means, K-Medoids, and CLARA methods using the optimal value of k based on the statistical validation of the gap statistics. The results of clustering for each method with k = 3 are shown in Figure 4 below.
Figure 4. Cluster plot using the K-Means (a) K-Medoids (b) and CLARA methods (c) (Source: authors, 2021)
Based on the three graphs in Figure 4, the results of K-Means clustering led to twelve regencies/cities as members of cluster 1, while eight regencies and cities were members of cluster 2, and 99 regencies/cities were members of cluster 3. Meanwhile, the results of K-medoids clustering showed that 99 regencies/cities were members of the first cluster, the second cluster had 11 regencies/cities as members, and nine regencies/cities were members of the third cluster. The results of clustering using CLARA clustering resulted in 99 regencies/cities that were members of cluster one, while 12 regencies/cities were members of cluster two, and eight regencies/cities were members of cluster three.
After clustering with the K-Means, K-Medoids, and CLARA methods, the next step was to determine the best clustering method by performing a validation test, or cluster evaluation. This study used internal cluster evaluation because the data used was unlabeled, where the quality of a cluster is determined based on internal information in the data. Three evaluation indicators were used, namely the connectivity coefficient, Dunn's index, and the silhouette coefficient (Manchanda et al., 2020). The three indicators respectively reflect the cohesion, connectivity, and separation of the clusters to determine the best clustering method (Ohan Uba et al., 2021). Each evaluation indicator has a threshold value. A formed cluster will be classified as 'good' if the resulting value of the silhouette coefficient is close to 1 (Al Azzie's, 2021).Meanwhile, for the value of Dunn's index, a formed cluster is better when its Dunn's index value is higher than that of the other formed clusters (Brock et al., 2008). Then, based on the connectivity
coefficient, a formed cluster is better when the value of the connectivity coefficient is lower, where the value is between zero and ∞(Landin et al., 2021). The results of the cluster assessment can be seen in Table 4.
Table 4. Results of Comparison of Internal Evaluations Based on Methods Method Connectivity Silhouette Width Dunn Index K-means clustering 11.8710 0.8423*) 0.0915 K-medoids clustering 8.1187 0.7971 0.0841 CLARA clustering 7.4794*) 0.8063 0.1042*) Note: *) = best cluster method
The table shows the validation results for the clusters of regencies/cities on the island of Java using the K-Means, K-Medoids, and CLARA methods. The best clustering methods according to the three validations were:
a. Silhouette coefficient validation – The maximum valid value, or the value closest to 1, was obtained by the K-Means method, i.e., 0.8423. This indicator assesses the homogeneity of the clustering, usually by examining intra-cluster variance.
b. Connectivity coefficient validation – The lowest validation value obtained by the criteria of this indicator threshold was obtained by CLARA's method, i.e., 7.4794.
This metric represents the extent to which observations are placed in the same cluster as the nearest neighbor.
c. Dunn's index validation – The highest validation value for Dunn's index was obtained by the CLARA method, i.e., 0.1042. This indicator measures the degree of separation between clusters by measuring the distance between the centroids of the clusters.
Based on the evaluation of the selection of the cluster method with three evaluation indicators, namely connectivity coefficient, Dunn's index and silhouette coefficients, it can be concluded that the best results for clustering based on infrastructure data to identify underdeveloped areas on Java Island were obtained by the CLARA method for three clusters (k = 3), because it had the best value for two of the three validation indexes used.
Besides this, CLARA can also be recommended because of the reliability of this method.
CLARA is a development of the K-Medoids clustering method, which has the particularity of being excellent at handling data sets that contain a large number of objects, as the data set used in this research.Another unique characteristic is that CLARA is not sensitive to outlier data, many of which were contained in the data set in this study, as shown in Figure 1. Furthermore, the step to determine the medoid with the CLARA method uses sampling on the results of cluster evaluation with the Dunn index indicator, which is very efficient in the computational process. Additionally, the connectivity indicator is used to represent the extent to which observations are placed in the same cluster as the nearest neighbor. The CLARA method is very good at separating each
observation in a certain group of clusters and excluding outlier values or data from a cluster. In other words, outliers entering a cluster is highly minimized.
Regency/city clustering using the best method
Based on the validation criteria, the best clustering method was CLARA. Thus, we further explored the clustering results obtained by CLARA. Based on the Regulation of the Ministry of Villages, Development of Disadvantaged Regions, and Transmigration Nr. 3 of 2016, Article 17, every indicator set by KNPDT has two lagging directions, i.e., positive and negative. An indicator with a positive lagging direction means that the higher the value of the indicator, the worse the condition of the area and the more it is lagging behind. An indicator with a negative lagging direction means that the lower the indicator's value, the worse the condition of the area and the more it is lagging behind. Table 5 presents descriptive statistics (averages) of the infrastructure indicators for each cluster and the direction of the lagging.
Table 5. Descriptive Statistics (Averages) of Infrastructure Indicators for Each Cluster
Indicator Lagging
Direction Cluster 1 Cluster 2 Cluster 3 Asphalt/concrete roads (X1) Negative 3015.79*) 4086.33 6031.88
Paved roads (X2) Positive 303.80*) 48.58 0.00
Dirt roads (X3) Positive 15.13*) 4.17 0.00
Other roads (X4) Negative 0.55 0.00*) 0.00*)
Markets without building (X5) Positive 36.23 59.33 63.38*) Health facilities (X6) Negative 2,209.68*) 2,774.00 2,826.63 Health workers (X7) Negative 600.46*) 920.83 1,519,75 Basic education facilities (X8) Negative 713.33 711.92*) 740.63 Telephone user households (X9) Negative 6,962.25*) 62,025.25 178,138.75 Note: *) = potentially lagging
Based on the results of the CLARA clustering method in Table 5, the following is an explanation of each infrastructure indicator:
a. Asphalt/concrete roads indicator (X1)
This indicator has a negative lagging direction. This means that the fewer villages there were with asphalt/concrete roads in an area, the poorer the state of the infrastructure in that regency/city. Cluster 1 had an average number of asphalt/concrete roads of 3,015.79, or 3,016 villages, which was the lowest number compared to the other clusters. Thus, the members of cluster 1 are potentially very underdeveloped areas according to the Asphalt/concrete roads indicator (X1).
b. Paved roads indicator (X2)
The Paved roads indicator (X2) has a positive lagging direction. Therefore, the more villages there were with paved roads in an area, the poorer the state of the infrastructure
in that regency/city. Cluster 1 had an average of 303.80, or 304 villages with paved roads, which was higher than for the other clusters.
c. Dirt roads indicator (X3)
The direction of lagging of the Dirt roads indicator (X3) is positive. This means that the larger the number of villages that have dirt roads in an area, the worse the state of the infrastructure in that regency/city. Cluster 1 had an average number of villages with dirt roads of 15,130, or 15 villages, which was the highest number compared to the other clusters.
d. Other roads indicator (X4)
The fewer villages that have 'Other' roads, the poorer the state of the infrastructure in regency/city, because this indicator has a negative lagging direction. Clusters 2 and 3 had an average number of 'Other' roads of zero. Cluster 1 is potentially a very underdeveloped area in terms of this indicator.
e. Markets without building indicator (X5)
The Markets without building indicator (X5) has a positive lagging direction.
Therefore, the higher the number of villages who have a market without building, the worse the state of the infrastructure in that regency/city. Cluster 3 had an average number of 63.38, or 64 villages that had a market without building, which was the highest number compared to the other clusters. Thus, cluster 3 is potentially a very underdeveloped area according to this indicator.
f. Health facilities indicator (X6)
With a negative lagging direction, the fewer health facilities an area has, the poorer the state of the infrastructure in that regency/city. Cluster 1 had an average number of health facilities of 2,209.68, or 2,210 units, which was the lowest number compared to the other clusters. Thus, cluster 1 is potentially an underdeveloped area according to this indicator.
g. Health workers indicator (X7)
The Health workers indicator has a negative lagging direction. Therefore, the lower the number of health workers in an area, the poorer the state of the infrastructure in that regency/city. Cluster 1 had an average number of 600.46 health workers, or 601 people, which was the lowest number compared to the other clusters.
h. Basic education facilities indicator (X8)
The lagging direction of the Basic education facilities indicator is negative. Therefore, the fewer basic education facilities an area has, the poorer the state of the infrastructure in the regency/city. Cluster 2 had an average number of Basic education facilities of 711.92, or 712 units, which was the lowest number compared to the other clusters.
i. Telephone user households indicator (X9)
The Telephone user households indicator has a negative lagging direction.
Consequently, the lower the number of telephone user households in an area, the worse the state of the infrastructure of that regency/city. Group 1 had an average number of
phone user households of 6,962.25, or 6,963 households, which was the lowest number compared to the other groups. Thus, cluster 1 has a strong potential to be an underdeveloped area.
Table 6. Members (Regencies/Cities) of Each Group from Underdeveloped, Developing to Developed
Cluster Members of each cluster (regency/city)
Underdevelope d (Cluster 1)
Bandung Barat Jombang Tegal City Purbalingga
Bangkalan Karanganyar Kudus Purwakarta
Banjarnegara Karawang Kulon Progo Purworejo
Bantul Kebumen Kuningan Rembang
Banyumas Kediri Lamongan Sampang
Banyuwangi Kendal Lebak Semarang
Batang Kepulauan Seribu Lumajang Serang
Blitar Klaten Madiun Situbondo
Blora Banjar City Magelang Sleman
Bojonegoro Batu City Magetan Sragen
Bondowoso Blitar City Majalengka Subang
Boyolali Bogor City Malang Sukabumi
Brebes Cilegon City Mojokerto Sukoharjo
Ciamis Cirebon City Nganjuk Sumedang
Cianjur Kediri City Ngawi Sumenep
Cilacap Madiun City Pacitan Tangerang
Cirebon Magelang City Pamekasan Tasikmalaya Demak Mojokerto City Pandeglang Tegal Garut Pasuruan City Pangandaran Temanggung Gresik Pekalongan City Pasuruan Trenggalek
Grobogan Probolinggo City Pati Tuban
Gunung Kidul Salatiga City Pekalongan Tulungagung Indramayu Serang City Pemalang Wonogiri
Jember Sukabumi City Ponorogo Wonosobo
Jepara Tasikmalaya City Probolinggo
Developing (Cluster 2)
Central Jakarta
City Semarang City
Tangerang Selatan City
Bekasi Cimahi City Surakarta City Yogyakarta City
Bogor Malang City
City Sidoarjo
Developed (Cluster 3)
West Jakarta City East Jakarta City Bandung City Depok City South Jakarta
City North Jakarta City Bekasi City Surabaya City
Based on Table 5, it can be seen that the members of clusters 1 had the potential to be lagging behind according to all six indicators. Clusters 2 and 3 had the potential to be lagging behind according to two indicators each. The order of the clusters in development (underdeveloped, developing, and developed) is as follows: cluster 1, cluster 2, and cluster 3. Table 6 lists the members of each group from underdeveloped, developing to developed.
In Table 6, it can be seen that 99 regencies/cities were included in the cluster of areas with an underdeveloped infrastructure, while 12 regencies/cities were included in the cluster of areas with a developing infrastructure, and 8 regencies/cities were included in the cluster of areas with a developed infrastructure. The latter cluster consists of four
cities of the Capital Special Region of Jakarta Province (West Jakarta, East Jakarta, South Jakarta, and North Jakarta), three regions of West Java (Bandung City, Depok City, and Bekasi City) and one region of East Java, namely Surabaya City. The underdeveloped, developing, and developed areas are mapped in Figure 5.
Figure 5. Mapping of development levels on Java Island based on clustered infrastructure indicators. (Source: authors, 2021)
The results for four of the infrastructure indicators (asphalt/concrete roads, markets without building, health facilities, and basic educational institutions) in this study were consistent with the previous research by Otok et al. (2021) on infrastructure in underdeveloped areas of the island of Java, which suggests that the underdevelopment of regional infrastructure on the island of Java is influenced by the accessibility dimension, which includes the average road distance to the regency/city as well as access to health services. The research concept used in Otok et al. (2021) was modeling with the Meta- Analytical Structural Equation Modeling (MASEM) approach using generalized least squares (GLS).
Based on unsupervised learning with a partitional clustering approach to identify underdeveloped areas based on infrastructure metrics, the best clustering results were obtained using the CLARA clustering method because it produced the best values for both the connectivity coefficient and Dunn's index. The result of determining underdeveloped areas on the island of Java from clustering using CLARA was that 99 regencies/cities were underdeveloped areas in terms of infrastructure, while 12
regencies/cities were developing areas, and 8 regencies/cities were areas with an advanced infrastructure. The characteristics of the areas designated as underdeveloped that need to be improved to keep them from lagging behind are:
Cluster 1 – Repairs to asphalt/concrete roads, paved roads, dirt roads, and improvement of the numbers of health facilities, health workers, and phone user households.
Cluster 2 – Improvements for areas without road access and more basic education facilities.
Cluster 3 – Improvements for areas without road access and more markets with building.
With these results, it is hoped that the central government and local governments of the six provinces of Java will focus more on formulating policies to improve infrastructure development in areas designated as underdeveloped areas based on the characteristics of each region. To gain a deeper understanding, other researchers could develop a more detailed conceptual framework that includes other infrastructural variables defined by the Ministry of Villages, Development of Disadvantaged Regions, and Transmigration. In addition, future research could make comparisons with other clustering methods, given the wide range of available cluster analysis methods, which could be further developed by applying them to other relevant fields.
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