职住 通勤 大数据_通勤者数据可视化

NZ Statistics hosted a data visualization competition which ran for one month from mid June to August. https://www.stats.govt.nz/2018-census/there-and-back-again-data-visualisation-competition/

NZ Statistics举办了一场数据可视化比赛，比赛从6月中旬到8月进行了一个月。 https://www.stats.govt.nz/2018-census/there-and-back-again-data-visualisation-competition/

Eager to portray data, I made a submission which took this data and presented it in the sleekest and simplest manner possible. I quickly bought a domain ‘nzcommute.com’ and set out to work.

渴望描绘数据，我提交了一份意见书，将这些数据并以最流畅，最简单的方式呈现出来。 我很快买了一个域名“ nzcommute.com”并开始工作。

Here is my submission

这是我的意见书

I focused on understanding the data and processing it using pandas before loading it onto a web server deployed through Google App Engine.

在将数据加载到通过Google App Engine部署的Web服务器上之前，我专注于了解数据并使用熊猫进行处理。

The data shows commute lines from A to B, and each location can be selected to present more data. Not all of the data could be presented at once, so filters were placed in the server to handle where everything flowed from.

数据显示了从A到B的通勤线，可以选择每个位置以显示更多数据。 并非所有数据都能一次显示，因此将过滤器放置在服务器中以处理所有数据的来源。

When you load the map, it shows all of the different types of commutes colour coded. I noticed that other entries had each location displaying the different commute types, that was an alternate option. Each commute path’s thickness is dictated by the number of people in that route.

加载地图时，它会以颜色显示所有不同类型的通勤。 我注意到其他条目的每个位置都显示不同的通勤类型，这是一个替代选择。 每条通勤路径的厚度由该路线中的人数决定。

You can select/deselect the commute types to only display a few, below is ‘working/studying from home’ category combined with being a passenger.You can also toggle between work or education commutes, or display both together, which is interesting to view contrasts in the data. As expected, people who are working commute to work via car, whereas students would be commuting as a passenger or via bus more than their working counterparts.

您可以选择/取消选择通勤类型，仅显示少数几种，下面是“ 在家工作/学习 ”类别并兼有乘客 。您还可以在工作或教育通勤之间切换，或同时显示两者，这很有趣数据中的对比。 正如预期的那样，工作的人通勤通勤上班，而学生作为乘客或乘公共汽车通勤的通勤率要高于工作中的同伴。

You can also search for a location, these are based on the regional names as determined by the provided dataset. Each search results also shows the number of commutes, and when clicking there, it immediately takes you to that location on the map. The URL is coded into the site, so whenever it changes the map will navigate to that location. This allows for sharing of links with friends.

您也可以搜索位置，这些位置基于提供的数据集确定的区域名称。 每个搜索结果还显示通勤次数，单击此处可立即将您带到地图上的该位置。 该URL已编码到站点中，因此，每当更改URL时，地图都会导航到该位置。 这允许与朋友共享链接。

When you select a location it displays the colour coded information, highlights the commute path on the map, and displays crucial data like how far the commute distance was, how many people are doing this commute, and where it is departing and arriving from.

当您选择一个位置时，它会显示颜色编码的信息，突出显示地图上的通勤路径，并显示重要数据，例如通勤距离有多远，有多少人在进行通勤以及通勤的出发地和到达地点。

A problem I ran into was that sometimes the data became too intense and it was difficult to discern what was going on.

我遇到的一个问题是，有时数据变得太密集，很难分辨发生了什么。

It’s kind of beautiful isn’t it?

是不是很漂亮？

After selecting a location, a popup shows up, beside the search if the screen is wide enough, otherwise it will show up vertically on mobile. This displays interesting statistics about a location, such as how many commutes are outbound/inbound from here, what locations are the most popular destinations, and what kind of commute is more common.

选择位置后，如果屏幕足够宽，则会在搜索旁边显示一个弹出窗口，否则它将在移动设备上垂直显示。 这将显示有关位置的有趣统计信息，例如从此处出站/入站的通勤次数，最受欢迎的目的地是哪些位置以及更常见的通勤方式。

Sometimes though, this happens and it’s hard to understand :(

虽然有时会发生这种情况，但很难理解:(

After the results were announced, I ran my eyes over a few of the entries and found something to learn from many of them. Most of us had similar ideas, but here are some valuable insights

结果公布后，我将目光投向了其中的一些条目，并从许多条目中学习了一些东西。 我们大多数人都有类似的想法，但这是一些有价值的见解

• Usage of a CDN to load data instantaneously.
  使用CDN即时加载数据。
• Usage of additional datasets to display statistical areas on the map so that regions can be identified. Then comprehensively display information on each region.
  使用其他数据集在地图上显示统计区域，以便可以识别区域。 然后全面显示每个区域的信息。
• The issue of having too many commute paths displayed at once can be solved by using clusters, when zoomed out, multiple markers would be combined into one cluster with the number of child markers listed, this helps to reduce clutter and let the eye focus on the most important details. It allows for a better overview of the information.
  可以通过使用聚类来解决一次显示太多通勤路径的问题，缩小显示时，多个标记将合并为一个聚类，其中列出了多个子标记，这有助于减少混乱并使眼睛专注于最重要的细节。 它可以更好地概述信息。

I think these changes would make the allow the product to more effectively portray key to the user and give them the power over what to view.

我认为这些更改将使产品能够更有效地向用户描绘密钥，并赋予他们查看内容的权力。

I’m looking forward to future challenges in visualising data.

我期待着在可视化数据方面的未来挑战。

This article was also posted on LinkedIn https://www.linkedin.com/post/edit/6699637671855296512/

这篇文章还发布在LinkedIn https://www.linkedin.com/post/edit/6699637671855296512/