Category: Environmental studies

How to use the online map tool for investing in sustainable rubber cultivation in tropical Asia如何利用在线地图工具投资热带亚洲可持续天然橡胶种植

On By Zhuangfang YiIn Big Data, Data mining, Data Science, Data visualization, Ecological Economics, Environmental studies, Geo-Cases, Geocoding, Great Mekong region, Green rubber, Imagery analysis, Interactive Map, Natural rubber value chain, R, Spatial Analysis, Thailand, Value chain of tropical crops and products, Web GISLeave a comment

Please go ahead and play with the full-screen map here.

This map Application is developed to support the Guidelines for Sustainable Development of Natural Rubber, which led by China Chamber of Commerce of Metals, Minerals & Chemicals Importers & Exporters with supports from World Agroforestry Centre, East and Center Asia Office (ICRAF). Asia produces >90% of global natural rubber primarily in monoculture for highest yield in limited growing areas. Rubber is largely harvested by smallholders in remote, undeveloped areas with limited access to markets, imposing substantial labor and opportunity costs. Typically, rubber plantations are introduced in high productivity areas, pushed onto marginal lands by industrial crops and uses and become marginally profitable for various reasons.

请在这里播放全屏地图。

这个应用地图集的开发是为了支持由中国五矿化工进出口商会和世界农用林业中心等部门联合编制的《可持续天然橡胶指南》。亚洲天然橡胶的产量占全球的90%，且主要是在有限的种植地区内，通过单一的种植，达到最高的产量。橡胶主要是由小农户在偏远的、欠发达的、市场有限的地区通过利用大量的劳动力和机会成本获得的。一般来说，橡胶只应该种植在高产量的地区，但已经被工业化的发展推到了在边缘土地上种植，并因种种原因已经边缘到无利可图。

Rubberplantation

Fig. 1. Rubber plantations in tropical Asia. It brings good fortune for millions of smallholder rubber farmers, but it also causes negative ecological and environmental damages.

图1：亚洲热带橡胶种植园。它给数以万计的小橡胶农民带来收入，但它也造成了负面的生态和环境的破坏。

The online map tool is developed for smallholder rubber farmers, foreign and domestic natural rubber investors as well as different level of governments.  

The online map tool entitled “Sustainable and Responsible Rubber Cultivation and Investment in Asia”, and it includes two main sections: “Rubber Profits and Biodiversity Conservation” and “Risks, SocioEconomic Factors, and Historical Rubber Price”.

该在线地图工具开发是为了小胶农、国内外天然橡胶投资者以及政府层面的政府使用。

这个标题为“亚洲可持续和负责任的天然橡胶种植和投资”的在线地图工具，包括两个主要部分：“橡胶利润和生物多样性保护”和“风险、社会经济因素和历史橡胶价格”。

The main user interface looks like the graph (Fig 2). There are 4 theme graphs and maps.

主用户界面看起来像图表（见图2）。有4个主题图和地图。

p1_section intro

Fig. 2. The main user interface of the online map tool.

图2：在线地图工具的主要用户界面。包括上图可见的“简介”，“第一部分”，“第二部分”，和“社交媒体分享”。

. Section 1 第一部分内容

This graph tells the correlation between “Minimum Profitable Rubber (USD/kg)” (the x-axis of the graph, and “Biodiversity (total species number)” in 2736 county that planted natural rubber trees in eight countries in tropical Asia.  There are 4312 counties in total, and in this map tool, we only present county that has the natural rubber cultivated.

这张图显示了亚洲热带地区八个国家种植天然橡胶树的2736个县的最低橡胶成本（美元/千克）（图的X轴）和生物多样性（总种数）之间的关系。共有4312个县，在这个地图工具中，我们只提供了有天然橡胶种植的2736县相关的内容。

p1_section intro_high

Fig. 3. How to read and use the data from the first graph. Each dot/circle represents a county, the color, and size of it indicates the area of natural rubber are planted. When you move your mouse closer to the dot, you will see “(2.34, 552) 400000 ha @ Xishuangbanna, China”, 2.34 is the minimum profitable rubber price (USD/kg), 552 is the total wildlife species including amphibians, reptiles, mammals, and birds.  “400000 ha” is the total area of planted natural rubber plantation from satellite images between 2010 and 2013. “@ Xishuangbanna, China” is the geolocation of the county. 

图3：如何阅读和使用第一个图中的数据。每个圆点/圆代表一个县，其颜色和大小表示天然橡胶种植面积。当你移动你的鼠标时，比如你会看到“（2.34，552）400000公顷的“西双版纳、中国”，2.34是最低盈利（成本）橡胶价格（美元/公斤），552是总的野生物种，包括两栖动物、爬行动物、哺乳动物和鸟类。“400000公顷”是2010～2013年间卫星影像种植天然橡胶种植园的总面积。“西双版纳、中国”是本县的地理位置。

Don’t be shy, please go ahead and play with the full-screen map here. The minimum profitable rubber price is the market price for national standard dry rubber products that would help you to start makes profits. For example, if the market price of natural rubber is 2.0 USD/kg in the county your rubber plantation located, but your minimum profitable rubber price is 2.5 USD/kg means you will lose money by just producing rubber products. However, if your minimum profitable rubber price is 1.5 USD/kg means you will still make about 0.5 USD/kg profit from your plantation.

请不要拘谨，可以在这里浏览全屏地图。最低橡胶成本换算成国家标准的干橡胶产品的市场价格，这将有助于你理解您所属橡胶园的盈利起始点。例如，如果你所在的橡胶种植区的天然橡胶市场价格是2美元/公斤，但你的最低成本橡胶价格是2.5美元/公斤，意味着你生产橡胶产品就会亏本。然而，如果你的最低成本的橡胶价格是1.5美元/公斤意味着你的种植园仍然会赚约0.5美元/公斤的利润。

The county that has a lower minimum profitable price for natural rubber is generally going to make better rubber profit in the global natural rubber market. However, as scientists behind this research, we hope that when you rush to invest and plant rubber in a certain county, please also think about other risks, e.g. biodiversity loss, topographic, tropical storm, frost as well as drought risks. They are going to be shown later in this demonstration. 

那些天然橡胶经营平均成本最低的县，在全球天然橡胶市场上将获得较好的橡胶利润。然而，作为这项研究背后的科学家，我们希望，当你在某个县匆忙投资成本较低的县市种植橡胶时，也要考虑其他风险，例如生物多样性丧失、地形、热带风暴、霜冻以及干旱风险。这些将被显示在这个演示之后。

p2_section intro_high.gif

Fig. 4.  The first map is the “Rubber Cultivation Area”, which shows the each county that has rubber trees from low to high in colors from yellow to red. The second map “Minimum Profitable Rubber Price”(USD/kg), again the higher the minimum profitable price is the fewer rubber profits that farmers and investors are going to receive. The third map is ” Biodiversity (Amphibians, Reptiles, Mammals, and Birds)”,  data was aggregated from IUCN-Redlist and BirdLife International.

图4：第一张地图是“橡胶种植区”，它显示了每个县的橡胶树种植数量从低到高的颜色，即从黄色到红色。第二张图“最低成本”（美元/千克），橡胶的平均成本越高，橡胶园的经营者就会获得更少的利润。第三地图是“生物多样性（两栖动物、爬行动物、哺乳动物和鸟类）”，数据来自世界自然保护联盟红色名录IUCN-Redlist和国际鸟盟聚集BirdLife International。

. Section 2 第二部分

We also demonstrated different types of risks that investors and smallholder farmers would face when they invest and plant rubber trees. Rubber tree doesn’t produce rubber latex before 7 years old, and the tree owners won’t make any profit until the tree is around 10 years old in general. In this section, we presented “Topographic Risk”, ” Tropical Storm”, “Drought Risk”,  and “Frost Risk”.

我们还展示了投资者和小农投资种植橡胶树时会面临的不同风险类型。橡胶树种植前7年在橡胶树不生产任何胶乳的情况下是没有任何盈利的，甚至橡胶园的经营者一般在橡胶树种下10年之前都不会获利。该部分中，我们提出了“地形风险”、“热带风暴”、“干旱风险”和“霜冻风险”。

p3_section intro_high.gif

Fig. 5. Section 2 ” Risks, SocioEconomic Factors and Historical Rubber Price” has seven different theme maps and interactive graphs. They are “Topographic Risk”, ” Tropical Storm”, “Drought Risk”,  and “Frost Risk”, “Average Natural Rubber Yield (kg/ha.year)”, “Minimum Wage for the 8 Countries (USD/day)”, and ” 10 years Rubber price”.

图5：第2节“风险、社会经济因素和橡胶价格历史”有七种不同的主题地图和互动图表。它们是“地形风险”、“热带风暴”、“干旱风险”、“霜冻风险”、“平均天然橡胶产量（千克/公顷）”、“8个国家的最低工资（美元/天）”和“10年橡胶价格”。

If you are interested in how the risk theme maps were produced, Dr. Antje Ahrends and her other coauthors have a peer-reviewed article published in Global Environmental Change in 2015.  “Average Natural Rubber Yield (kg/ha.year)” and “Minimum Wage for the 8 Countries (USD/day)” dataset was obtained from  International Labour Organization (ILO, 2014)  and FAO.” 10 years Rubber price” was scraped from  IndexMudi Natural Rubber Price.

这个互动地图集中展示的所有内容都是有科学依据的。如果你想知道风险专题地图是如何编制的，Antje Ahrends博士和其他合作者有一篇同行评审的论文，发表在2015年的国际期刊《全球环境变化》。“平均天然橡胶产量（公斤/公顷/年）”和“8国家最低工资（元/天）”的数据来自国际劳工组织（ILO，2014年）和联合国粮农组织。“10年橡胶价格”来自于天然橡胶的价格indexmudi。

Dr. Chuck Cannon and I are wrapping up a peer-reviewed journal article to explain the data collection, analysis, and policy recommendations based on the results, and we will share the link to the article once it’s available. Dr. Xu Jianchu and Su Yufang have shaped and provided guidance to shape the online map tool development. We could not gather the datasets and put insights to see how we could cultivate, manage, and invest in natural rubber responsibly without other scientists and researchers study and contribute to field for years. We appreciated Wildlife Conservation Society, many other NGOs and national department of rubber research in Thailand and Cambodia for their supports during our field investigation in 2015 and 2016.

Chuck Cannon博士和我正在撰写一篇同行评议的科研期刊文章，用来解释该地图集生成的数据收集、分析等等，还包括了政策建议。文章一旦发表，我们会和您分享文章的链接。许建初博士和苏宇芳博士为在线地图集的开发提供了非常宝贵的意见和建议。我们无法收集数据集、并在没有其他科学家和研究人员的研究和贡献的情况下深入了解如何才能负责任地种植、管理和投资天然橡胶。我们感谢野生动物保护协会和许多其他非政府组织，以及泰国和柬埔寨国家橡胶研究院在2015和2016年的实地调查中给予的支持。

We have two country reports for natural rubber in Thailand, and natural rubber and land conflict in Cambodia, a report support this online map tool is finalizing and we will share the link soon when it’s ready.

我们有两份关于泰国天然橡胶和柬埔寨天然橡胶和土地利用冲突的国家报告，一份支持这一在线地图工具的报告正在定稿，我们将很快分享这一链接。

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Technical sides 技术层面

The research and analysis were done in R, and you could find my code here.

The visualization is purely coded in R too, isn’t R is such an awesome language? You could see my code for the visualization here.

研究和分析是利用R完成的，您可以在这里找到我的代码。

可视化地图也是在R中利用纯编码编写的，难道R不是一个很棒的语言吗？你可以在这里看到我的可视化代码。

To render geojson format of multi-polygon, you should use:

library(rmapshaper)
county_json_simplified <- ms_simplify(<your geojson file>)

My original geojson for 4000+ county weights about 100M but this code have help to reduce it to 5M, and it renders much faster on Rpubs.com.

我原来的GeoJSON 4000 +县级文件大小约100兆，但是这行代码有效的使文件降低到5兆。

I learnt a lot from this blog on manipulating geojson with R and another blog on using flexdashboard in R for visualization. Having an open source and general support from R users are great.

我从这个使用R的博客上和另一个博客的可视化学到了很多东西。开放性平台和R给予大家更大的创作空间。

Artificial intelligence on urban tree species identification 人工智能在市区树种识别上的应用

On By Zhuangfang YiIn AWS, Big Data, Data Science, Deep Learning, Ecological Economics, Environmental studies, Imagery analysis, Machine Learning, Neural Network, Python, QGIS, Satellite Imagery ProcessingLeave a comment

It doesn’t matter which part of the world you are living now,  very diverse tree species are planted around the urban area we live.  Trees in the urban areas have many functions, for example, trees provide habitats for wildlife, clean air and water, provide significant health and social benefits, and also improve property value too.  Wake up in a beautiful morning that birds are singing outside your apartment because you have many beautiful trees grow outside of your space. How awesome is that!

However, tree planting, survey, and species identification require an enormous amount of work that literally took generations and years of inputs and care. What if we could identify tree species from satellite imagery, how much faster and how well we could get tree species identified and also tell their geolocations as well.

A city has its own tree selection and planting plan, but homeowners have their own tree preference, which the identification work a bit complicated, though.

chicagoTrees

(Photo from Google Earth Pro June 2010 in Chicago area)

It’s hard to tell now how many tree species are planted in above image. But we could (zoom in and) tell these trees actually have a slightly different shape of tree crown, color, and texture. From here I only need to have a valid dataset basically tell me what tree I am looking at now, which is a tree survey and trees geolocation records from the city. I will be able to teach a computer to select similar features for the species I’m interested in identifying.

GreeAsh

These are Green Ash trees (I marked as green dots here).

LittleleafLiden.png

These are Littleleaf Linden, they are marked as orange dots.

Let me run a Caffe deep learning model (it’s one of the neural networks and also known as artificial intelligence model) for an image classification on these two species, and see if the computer could separate these two species from my training and test datasets.

Great news that the model could actually tell the differences between these two species. I run the model for 300 epochs (runs) from learning rate 0.01 to 0.001 on about 200 images for two species. 75% went to train the model and 25% for testing. The result is not bad that we have around 90% of accuracy (orange line) and less than 0.1 loss on the training dataset.

nvidia_d_modeltest

I threw a random test image to the model (a green ash screenshot in this case) and it tells the result.

test_trees2

I will be working on identifying other 20 trees species and their geolocations next time.

Let’s get some answer what trees are planted in Chicago area and how it related to the property value (an interesting question to ask), and also what ecological benefits and functions these tree are providing (leave this to urban ecologist if my cloud computer could identify the species)? Check my future work ;-).

 

PV Solar Installation in US: who has installed solar panel and who will be the next?

On By Zhuangfang YiIn Big Data, Data mining, Data visualization, Ecological Economics, Environmental studies, Leaflet, R, Solar Energy, Spatial Analysis, Web GISLeave a comment

Project idea

Photovoltaic (PV) solar panels, which convert solar energy into electricity, are one of the most attractive options for the homeowners. Studies have shown that by 2015, there are about 4.8million homeowners had installed solar panels in the United States of America. Meanwhile, the solar energy market continues growing rapidly. Indeed, the estimated cost and potential saving of solar is the most concerned question. However, there is a tremendous commercial potential for the solar energy business, and visualizing the long term tendency of the market is vital for the solar energy companies’ survival in the market . The visualization process could be realized by examining the following aspects:

  1. Who has installed PV panels, and what are the characteristics of the household, e.g. what’s the age, household income, education level, current utility rate, race, home location, current PV resource, existing incentive and tax credits for those that have installed PV panels?
  2. What does the pattern of solar panel installation looks like across the nation, and at what rate? Which household is the most likely to install solar panels in the future?

The expected primary output from this proposal is a web map application . It will contain two major functions. The first is the cost and returned benefit for the households according to their home geolocation. The second is interactive maps for the companies of the geolocations of their future customers and the growth trends.

Initial outputs

​
The cost and payback period for the PV solar installation: Why not go solar!

NetCost

Incentive programs and tax credits bring down the cost of solar panel installation. This is the average costs for each state.

Monthly Saving

Going solar would save homeowners’ spending on the electricity bill.

Payback Years

Payback years vary from state to state, depending on incentives and costs. High cost does not necessarily mean a longer payback period because it also depends on the state’s current electricity rate and state subsidy/incentive schemes. The higher the current electricity rate, the sooner you would recoup the costs of solar panel installation. The higher the incentives from the state, the sooner you will recoup the installation cost.

How many PV panels have been installed and where?

Number of Solar Installation

The number of solar panels installed in the states that have been registered on NREL’s Open PV Project. There were about 500,000 installations I was able to collect from the Open PV Project. It’s zip-code-based data, so I’ve been able to merge it to the “zip code” package on R. My R codes file is added here at my GitHub project.

Other statistical facts : American homeowners who installed solar panels generally has $25,301.5higher household income compare to the national household income. Their home located in places that have higher electricity rate, about 4 cents/kW greater than the national average, and they are also having higher solar energy resource, about 1.42 kW/m2 higher than the national average.

Two interactive maps were produced in RStudio with “leaflet”

Solar Installation_screen shot1

An overview of the solar panel installation in the United States.

Solar Installation_screen shot2

Residents on the West Coast have installed about 32,000 solar panels from the data registered on the Open PV Project, and most of them were installed by residents in California. When zoomed in closely, one could easily browse through the details of the installation locations around San Francisco.

Solar Installation_screen shot3

Another good location would be The District of Columbia (Washington D.C.) area. The East Coast has less solar energy resource (kW/m2) compared to the West Coast, especially California. However, the solar panel installations of homeowners around DC area are very high too. From maps above, we know that because the cost of installation is much lower, and the payback period is much faster compared to other parts of the country. It would be fascinating to dig out more information/factors behind their installation motivation. We could zoom in too much more detailed locations for each installation on this interactive map.

However, some areas, like DC and San Francisco, have a much larger population compared to other parts of US, which means there are going to be much more installations. An installation rate per 10,000 people would be much more appropriate. Therefore, I produced another interactive map with the installation rate per 10,000 people, the bigger the size of the circle is the higher rate of the installation.

Solar Installation_screen shot4

The largest installation rate in the country is in the city of Ladera Ranch, located in South Orange County, California. Though, the reason behind it is not clear and more analysis is needed.

Solar Installation_screen shot5

Buckland, MA has the highest installation on the East Coast. I can’t explain what the motivation behind it yet either. Further analysis of the household characteristics would be helpful. These two interactive maps were uploaded tomy GitHub repository, where you will be able to see the R code I wrote to process the data as well.

Public Data Sources

To answer these two questions, datasets of 1670M (1.67G) were downloaded and scraped from multiple sources:
(1). Electricity rate by zip codes;

(2). A 10km resolution of solar energy resources map, in ESRI shapefile, was downloaded the National Renewable Energy Laboratory (NREL); It was later extracted by zipcode polygon downloaded from ESRI ArcGIS online.

(3). Current solar panel installation data was scraped from the website of open PV website, a collection of installations by zip code. It requires registration to be able to access the data. It is part of NREL. The dataset includes the zip code of the installation, the cost, the size of the installation and the state of each location.

(4). Household income, education, the population of each zip code was obtained from US census.

(5). The average cost of the solar installation for each state was scraped from the website: Current cost of solar panels and Why Solar Energy? More of datasets for this proposal will be downloaded from the Department of Energy on GitHub via API.

Note: I cannot guarantee the accuracy of the analysis. My results are based on two days of data mining, wrangling, and analysis. The quality of the analysis is highly depended on the quality of the data and on how I understood the datasets in such limited time. A further validation of the analysis and datasets is needed.

For further contact the author, please find me on https://geoyi.org; or email me:geospatialanalystyi@gmail.com.

My web map application is online

On By Zhuangfang YiIn ArcGIS Online, Big Data, Ecological Economics, Environmental studies, ESRI, Great Mekong region, Green rubber, Spatial Analysis, Web GISLeave a comment

Hi friends,

I’ve been working on a web application for Chinese Ministry of Commerce on rubber cultivation and risks will be out soon, and I just wanna share with you the simplified version web map API here. I only have layers here, though, more to come.

Screen shot for web map application

Web map application by Zhuangfang Yi: Current rubber cultivation area (ha) in tropical Asia

This web map API aims to tell the investors that rubber cultivation is not just about clearing the land/forests, plant trees and then you could wait for tapping the tree and sell the latex. There are way more risks for the planting/cultivate rubber trees, including several natural disasters, cultural and economic conflicts between the foreign investors and host countries.

We also found the minimum price for rubber latex for livelihood sustainability is as high as 3USD/kg. I define the  minimum price is the price that an investor/household could cover the costs of establishing and managing their rubber plantations. While the actual rubber price is lower than the minimum price, there is no profit for having the rubber plantations. The minimum price for running a rubber plantation varies from country to country. I ran the analysis through 8 countries in Asia: China, Laos, Myanmar, Cambodia, Vietnam, Malaysia and Indonesia. The minimum price depends on the minimum wage, labour availability, costs of the plantation establishments and management, average rubber latex productivity throughout the life span of rubber trees. The cut-off price ranges from 1.2USD/kg to 3.6USD/kg.

We could make an example that if rubber price is 2USD/kg now in the market, the country whose cutoff price for rubber is 3USD/kg won’t make any profit, but the investors in the country might lose at least 1USD/kg for selling every kg of rubber latex.

 

The natural rubber value chain and foreign investments in Thailand: how can we achieve sustainable and responsible rubber cultivation and investment?

On By Zhuangfang YiIn Environmental studies, Geo-Cases, Great Mekong region, Green rubber, Natural rubber value chain, Spatial Analysis, Thailand, Value chain of tropical crops and productsLeave a comment

I have an opportunity worked for Chinese Ministry of Commerce with ICRAF last fall, and have been studying natural rubber value chain since then. I led four technic reports on natural rubber value chain: the first report is for Thailand natural rubber value chain (please see the title);the second one  is about natural rubber value chain, foreign investments and land conflicts in Cambodia; the third report is the a comparison study between Thailand and Cambodia, the biggest natural rubber producer and the emerging rubber producer; the last report will concentrate on the risks of natural rubber cultivation and investment in Asia, from geosnatially perspectives. As I mentioned in the reports that there are no winner in the natural rubber value chain: we lost biodiversity and ecosystem services from covering natural forests to rubber monoculture (upstream of the value chain); and emitted million tons of polluted air and water, and carbon dioxide back to nature from rubber processing (the midstream); at the end, without sustainable livelihood for the poor who grows rubber; and limited competitiveness in the end products market (the downstream). We should go back the source and really think about how we can improve the whole value chain, and why.

The following content is the abstract of Thailand report in English. These reports are in Chinese recently, if you are interested in the content please contact Dr. Zhuang-Fang Yi, geospatialanalystyi@gmail.com and yizhuangfang@mail.kib.ac.cn.

Upper Mekong Region

Figure 1. The great Mekong region and also the global nature rubber producers. 

Asia supplies 93% of natural rubber demand globally. As the world No.1 natural rubber producer, Thailand has exported nearly 40% of global rubber production demands, which is 87% of its domestic rubber production. The production improvement in Thailand is not only depending on its biophysical suitability of rubber growing, but also relying on its policy supports and subsidies to millions of upstream rubber farmers. Thailand has spent about 21.3billion Baht (586million USD) from Sep. 2013 to Mar. 2014 to subsidize its rubber farmers while the price of natural rubber went down. However, lack of manufacturing and financial supports for its midstream and downstream of the natural rubber value chain, Thailand highly depends on rubber exporting to other countries, e.g. China, US, EU and Japan.

The long history of natural rubber cultivation and supports from Thai government has grown Thai rubber farmers a better rubber economic resilience cultivation systems, which is rubber agroforestry. Rubber agroforestry is a rather complex intercropping system compare to rubber monoculture. Rubber monoculture refers to the rubber plantations that only have rubber trees, and other plant species has been killed and get rids constantly by using herbicide and manual clearance. Rubber agroforestry sustains better ecosystem services and also bring more economic returns. But the labour requirement and knowledge gaps from rubber monoculture to rubber agroforestry are the main constrains for a greener cultivation system. It means rubber farmers only need to intensively take care rubber trees in rubber monoculture system, but need other knowledge and time inputs for rubber agroforestry. However, there are about 21 intercropping systems and more than 300 farms are practicing the intercropped rubber agroforestry by the rubber famers without authority supports like rubber monoculture in Thailand. Urgent research and institution support are need for rubber agroforestry in Thailand and globally.

The merging economies and natural rubber producer countries, e.g. Vietnam, Cambodia, Laos, and Myanmar in Mekong region, are following Thailand’s foot steps, only practicing rubber monoculture, that highly support its upstream value chain but lack of rubber manufacturing and supporting financing systems for mid-stream and downstream. It leads to heavily depend on Chinese and the rest of world rubber demands. It leads to very weak economic resilience for millions of smallholding rubber farmers when the price goes down. In China market, rubber price dropped from 6.3USD/kg to less than a dollar in 2014. China, as the biggest natural rubber importer, consuming nearly 40% of global rubber supply. On the other hand, 20% of imported taxes are charged and have dramatically increased the cost of rubber end products, and loss its global competitiveness in the natural rubber market. There are no winner in the natural rubber value chain: we lost biodiversity and ecosystem services from covering natural forests to rubber monoculture (upstream of the value chain); and emitted million tons of polluted air and water, and carbon dioxide back to nature from rubber processing (the midstream); at the end, without sustainable livelihood for the poor who grows rubber; and limited competitiveness in the end products market (the downstream). We should go back the source and really think about how we can improve the whole value chain, and why.

While more and more Chinese state-owned and private enterprises follow “Go Global” strategy by Chine central government who have heavily invested outside of China. Natural rubber end products, especially tires industry is one of them. In this reports, we scrutinized the natural rubber value chain in Thailand and its foreign investments , especially Chinese investments. We tried to answer:

  1. If there are the best rubber cultivation systems that combine economic returns and a better ecosystem services supporting system;
  2. The relationship between Chinese investors and Thai natural rubber value chain;
  3. The possible ways of sustainable and responsible rubber cultivation and investment.

Coming reports in Chinese

泰国橡胶种植面积.jpg

Figure 2. Thailand as the biggest rubber producer, produce 4.5millions ton of natural rubber, and 80% of Thailand domestic natural rubber is from Southern Thailand. Each polygon represents of a province in the map and the darker of the color represents the bigger area of rubber cultivation.

Everything is spatial and they all can be mapped and my Geo-Case 3.

On By Zhuangfang YiIn Environmental studies, ESRI, Geo-Cases, Spatial AnalysisLeave a comment

I majored in Geography for my bachelor degree and did Ecological Economics for my PhD. I often got questions from people asking if I am more an economist or an ecologist, I would say I am more a geographer.  “Oh, I never knew Geography is a major” or “what is that?” If you ever asked me these kind of question, please don’t feel bad about it, because you’re not the only one, and I bet my peers got these questions and response all the time.

One thing you might not know is Geospatial Technology has been listed as the top emerging industry in next decades by United States Department of Labor. To me Geospatial Technology is a combination of art, science and engineering. It’s the art of data visualization, and the thought and skills the analyst/cartographer put behind it. I usually got question about what is my ideal job from friends and family, I would say it’s probably Geographic Information System (GIS) analyst or geospatial analyst within the Geospatial Technology since I moved to the states . It’s difficult to become a good GIS analyst in China, even though China got a huge group of GIS analysts and good Geo-programers because it’s more difficult to access to geo-data there, and the field develops just recently and the fundamental data sources are so limited. You have to work for top research institutes or universities to be able to get the data to do further geospatial analysis, and of course you have to build up the network with the state agents first to be able to have the access to the data. However, the story is so different in the states that you have so much open source database run by the state department, and the people are so willing to share their data, the analysis. I got too excited I guess lol and decide to be one of them….This is a link to more than 300 free GIS datasets shared by Prof. Robin Wilson: http://freegisdata.rtwilson.com. Of course, like you’ve just guessed most of free datasets are run and shared by the research institutes, NGO, state lab and universities in the states.

You’re probably would ask about ‘what is exactly geospatial technology?’ I would say they are just the maps you’re using every day, for example, the fastest or least traffic driving routs that google map tells you to get to airport; the map of the best Thai/Mediterranean/Mexican restaurants Yelp shows you; The price of housing around your neighborhood that your real estate agent tells you; or how about the crime map for police station in your area, some area attracts more serious crimes and some are low. Actually, you only need a very basic mapping skill with good platform to map these data that I mentioned above. Of course, you could do more sophisticated analysis with the geo-datasets you have access to, e.g. the medicare spending in some states (countries) are higher than others? Is that because they have less accessibility to good and clean water/food? Or there are just more health facilities happen to be there? Do people become healthier  after they spend more on the medicare and health? Or how about having a protected area in this location and why not a thousand miles away? How much we need to spend on protecting this wildlife? And where would be the most cost-efficient way to fund it?

Everything is spatial and they all can be mapped. Map is so powerful that you definitely could tell the story from the data presents on it. Even though a lot of data is open in the states, but to become a GIS analyst, who works for states department, needs to have a security clearance. It becomes impossible for a non us citizen then. It’s such a dilemma, right? However, there are already such a good and open data source in the states, and I’ve seen so many cool analysis have been presented. Therefore, the work ethics have been highly valued in the field, including what message you’re presenting in the maps and who you present to.

Back to my Geo-Case 3, I had an experience to work with a national nature reserve (state protected area) in China this July. I tried to quantify the habitat quality for future biodiversity conservation under human disturbance and climate change, and to target a location for avoid the impacts from both human disturbance and climate change. We’ve been working with the natural reserve for years and have able to road network, villages location, socioeconomic and demographic data, and historical land-use maps, soil map, vegetation type and so on. My colleagues in World Agroforestry Centre run climate change models, and they impacts on land-use change, which were the land-use change simulation rule for me to run the land-use scenarios.

Data description:  To run this geospatial model of wildlife habitat degradation under human disturbance would need the land- use maps in recent years, climate change data, soil, vegetation type, socioeconomic and demographic and so on. I run the habitat degradation models though InVest (you could get more information through here: http://www.naturalcapitalproject.org). It a platform that allow you to value natural capital.

Knowledge need: remote sensing imagery processing for the land-use mapping; Geo-statistics/geospatial through ESRI ArcGIS; InVest, especially, habitat quality and habitat risk management. (Leave me a comment if you need more detail information).

Result maps:

land－use

hibitat degredation

No surprise that human disturbance have more impacts on habitat quality in the nature reserve, which you could tell the degraded areas are along the road network; and the core area for the nature reserve, which has the least human disturbance, would maintain the highest habitat quality (yellow corner on the left corner of the map.).

*** I am now open for a job position in Geospatial Analyst/GIS Analyst around DC area (or telecommuting). I have knowledge on: 

  • Utilize advanced modeling techniques
  • Apply fundamental spatial statistics
  • Perform advanced vector and raster analysis
  • Perform surface modeling and analysis
  • Understand and apply python scripting
  • Perform fundamentals of spatial database design
  • Determine and define an appropriate coordinate system
  • Demonstrate proficiency in application configuration
  • Apply advanced visualization techniques
  • Understand fundamental network analysis concepts
  • Perform some basic network analysis

I mainly using ESRI ArcGIS 10.2,  ArcGIS online, server, and have experience using open GIS softwares and platforms on QGIS and GRASS, and spatial modeling scripting via R and Python. I will present the knowledge above in the  geo-cases I’m doing in my future blogs. English is my third language, and any comments, proof reading, and advice on language polishing and even the content revision are welcomed.

Geo-Case 2: analyzing a household hazardous waste participant for a NGO

On By Zhuangfang YiIn Environmental studies, ESRI, Spatial AnalysisLeave a comment

Case description: The Pennsylvania Resources Council (PRC) is a non-profit organization (NGO) that into environmental protection. Recently, PRC is helping homeowners in many counties in Pennsylvania collecting and disposing of common household products such as paint, solvents, automotive fluids, pesticides, insecticides, and cleaning chemicals, which could not recycling easily. To better develop education and outreach materials response to their work,they want to map out the participants who are volunteering for the program.

Geospatial skill needed: Geocoding.

Data: In this study, only zipcode of the household participants have been recorded. 

Result: the location

Geocode household hazardous waste

In this case study, only participants around Allegheny county are presented. The result shows that more people who can access to PRC are more willing need to participant the program at this point. 

I would try to write more blogs here. The purposes are:

  1. wanna get better on writing in English;
  2. trying to sale my Geospatial analysis skill to the potential employers and customers;
  3. Put up the geospatial cases I’ve done and passion about, and would help me to build my own GEO case file in the future.

I am doing case study instead of putting up the programming,coding and mapping skill, because I believe that mapping is a kinda art and you won’t really interested in how I made them.If you are interesting in these skills, you could contact me for lectures, mapping and analysis work through: yi.zhuangfang@gmail.com. Any language editing, case presenting and map cartography comments are welcomed.

Geo-Case 1: how to promote your art activities for the potential market of your company

On By Zhuangfang YiIn Environmental studies, ESRI, Geo-Cases, Spatial AnalysisLeave a comment

(In my blog, I will use a lot of abbreviation, one of the most frequent ones would be GIS, which stands for Geographic Information System)

GIS knowledge needed: Geocoding;

Data source: Pennsylvania (GIS Tutorial BOOK 1)

Analysis purpose:  There is an art event sponsored by an arts’ organization in Allison Park, Pittsburgh, called FLUX. The event planners of FLUX would like to know where the event attendees reside for planning and marketing activities in the future. There are only attendees’ ZIP codes have been recorded. So simply, we are going to produce a map for FLUX by using ZIP code that attendees provided to tell where they are from in the area.

Data summery: 1487 attendees, and about 86% of them are from Pennsylvania;

Map result:

Tutorial8-1

Conclusion: FLUX needs more planning and marketing for Pittsburgh, especially the southeast corner, since there are more attendee from that area, and they could be the main customers for the future art events marketing who drove far up to north for the art event. However, there are always more information could be visualized, for example, the age, occupation and so on!