What waffle grid map is, and compare it with stacked bar chart
TL;DR
What is a waffle chart? What is a waffle chart on map? When should this type of map be used? This article serves as a gentle introduction to waffle chart and waffle grid map, using a dataset of convenience stores in Hong Kong. Part I explains what waffle chart is, while Part II compares the pros and cons of using waffle grid map and stacked bar chart from the lens of data visualisation and cartography.
Part I — Waffle Grid Map
Here’s a waffle grid map showing the number and proportion of two major convenience stores (CVS), 7-Eleven and Circle K, by district. …
Visualising spatial distribution of the two largest convenience store chains in Hong Kong
TL;DR
7-Eleven and Circle K are two major convenience store chains in Hong Kong. Where could you find more store in one chain compared to the other? And what is the magnitude of differences?
Note: Chinese version of this article is also available in my Medium (i.e. here!).
Hong Kong is packed with two major convenience store chains, 7-Eleven (7–11) and Circle K (OK). While there are nearly 1,000 7–11 stores, there are only 350 Circle K stores in the whole territory.
總有一間喺左近
我搵唔返（歡迎讀做「我唔想做literature review」）呢句slogan嘅出處，但佢都証明咗香港嘅7–11開到成行成市。
咁到底7仔開得有幾密？呢個係第一個問題。
同樣係便利店，全香港有接近成千間7–11，但OK就只係得300幾間，足足差成700間。咁就帶出第二個問題：會唔會有某啲地方係OK多過7–11？（劇透：有）。如果有嘅話，咁OK會多過佢幾多間？（劇透：3間左右）
數據來源
兩間集團嘅官網都有每間便利店分店嘅位置，所以只要做少少data scraping就可以砌到一個便利店dataset。Data scraping嘅話，Python人就用Beautiful Soup；我寫開R，所以用rvest。
免責聲明：set data 1. 無validate過；2. 唔係最新version；3. 可能visualise嗰陣copy漏咗幾行。有錯有漏唔係意外，係我懶。
地圖
Interactive charts and (sort-of) scrollytelling with R & Shiny
TL;DR
I wrote a webpage (with interactive charts!) using Shiny and R to visualise the commute pattern dataset I used in the previous article.
Are you interested to play and mess with the commute pattern dataset of Hong Kong? Want to select your area of focus without touching the dataset? Or, you are just looking for examples of web applications developed using Shiny? This is the article for you.
This article serves as a place to introduce the webpage I created for interactively visualising the commute pattern dataset. The webpage is created using Shiny and available at the following link:
What trends do the origin-destination matrix tell us about the supply of jobs in terms of location?
TL;DR
There are more than 2.8 million workers with a fixed place of work in Hong Kong. What does the overall travel pattern from their home to the working place look like, when visualised? The location of the jobs and the living place of the workers are revealing the job-housing imbalance problem of the new towns of Hong Kong.
From home to working place
Here’s an O-D matrix showing the number of workers grouped by their living and working place in 2016¹. Each cell represents the number of workers living in that row and working in that column. Say, the number 48,647 in the top-left corner…
Examining the history of new town development with commute pattern maps
TL;DR
How the commuting patterns vary among the new towns in Hong Kong? Why are there more bus travellers or subway travellers in each new town? This article presents the dominate commuting type in the selected new towns. You could dig deeper into the design pattern of the new towns.
A year ago, I made a map and wrote an article to show the overall commute pattern of Hong Kong, aiming to show how dominant MTR is as a commuting transport. …
What is the distribution of the 436,000 pedestrian walkways in terms of length and gradient?
TL;DR
About 436,000 pedestrian walkway segments are scattered around the whole territory of Hong Kong. The distribution of both length and gradient are uneven. Most of them are on flat terrain and short paths between 0–20 m. There are around 333,800 normal footways, 27,500 crossings, 7,200 footbridge and 1,000 subway segments in the network. These numbers could help us get a rough image of the topology of the pedestrian network in Hong Kong.
Note: The full version of this article (with codes written in
Rto reproduce the charts) is available on my personal blog.
Mapping and animating how sea level rising floods the urban area of Hong Kong
TL;DR
How will be the urban land in Hong Kong flooded when sea level continues to rise? Here are several maps simulating the flooded area when sea level rises to a given level.
Sea level rising, revisited
Sea level rising is never a new term for us. It is estimated that when we are still doing the “business-as-usual” scenario, more than 150 million people living on land will have their living place below high-tide line by 2050.
Mitigation and adaption are both essential to climate changes. Urban planners and designers have proposed several visions to adapt the expected increase amount of water running through the…
Land utilisation map with a little twist in hexagons
TL;DR
Are there any other ways to describe land utilisation of the city besides merely looking into the ubiquitous land utilisation maps and classification diagrams? I created a layer of hexagonal bins covering Hong Kong, then used it to count different types of land uses. These maps “with a little twist” provide a fresh way to look into land use distribution and presents an uncommon mapping technique of grouping data into regular grids.
Land use maps are, literally, thematic maps that draw the classified land resources/uses of the land. Among the 1100 sq.km. …
Kenneth Wong
Urban Data Science Enthusiast | Urban Planning | GIS | Maps | Data Visualisation | kennethwong12.netlify.app
