Windows 10, Shared Network Folders Fail

I have my desktop computer I use at home, and my work laptop I take back and forth with me. I mostly work from home, transferring relevant files, like lectures and exam to the work laptop--this requires properly working shared folders. This should be EASY. However, every time Windows rolls out a new update, this seems to break, with the evidence being the amount of sites and length of pages that discuss this exact issue, spiking every few months with new updates. The situation was apparently so bad that Windows discontinued the HomeGroup functionality, believing that would solve the problem.

If you are here, it's likely your shared network folders aren't working. Last week I had to reinstall Windows 10 on my work laptop, and of course I lost my network folders. I worked for four whole days Googling and trying various ways to bring back this functionality. While I can't claim to know what the problem is with your folders, this page finally fixed mine--you have to go on both computers in Group Policy Editor (gpedit.msc, assuming your version of Windows has this function), and "Enable insecure guest logons."

Guest access in SMB2 disabled by default in Windows 10 Fall Creators Update and Windows Server 2016 version 1709

The only reason I was able to find this solution is that (again, after four days) my computer gave me a new error message about unauthenticated guest access. I hope your solution search is quicker than mine. Good luck!

The Bindweed Horror

My house had been abandoned for 10 years when I bought it. Thus the yard was simply a jungle of monstrous vegetation. After several weeks of cutting and pulling the overgrowth, I thought I had it under control. I wanted a sustainable, natural yard, not a manicured lawn, so I largely allowed it to do what it wanted after that, just the occasional trimming. I was pleasantly surprised when a a vine produced pretty white flowers in the morning, which I (incorrectly) believed was morning glory. For years I allowed it to flourish, tearing it up when it started to take over. This year I realized the plant was not morning glory--it is bindweed. A quick Google could have solved this early on--bindweed produces a white flower, morning glory is other colors. I committed myself to its eradication.

Several websites offer suggestions for the removal of bindweed. The most extreme suggestion was simply to move away. The vigorous growth and nefarious root system makes it near impossible to eradicate. The University of California Extension says, "bindweed is one of the most persistent and difficult-to-control weeds in landscapes and agricultural crops." They suggest cultivating (i.e., pulling) it every 2-3 weeks to prevent germination. Other sites similarly recommend frequent pulling, claiming this will eventually "exhaust the roots." I don't know if this is accurate, but it sounds good to me, so that's what I have been doing for the last month.

The bindweed is mostly confined to the backyard, and specifically, only the north half, which is far sunnier, and separated from the south by a sidewalk. I have spent about 20 minutes in the morning, and another at night, scouring the yard for the telltale diamond-shaped leaves and/or viny appearance. Almost every site I found simply said to pull the weed, with no indication of trying to dig it out. However, after 2 weeks of picking about 75 bindweed plants per day, I dug one up and discovered the size of the root/rhizome--it's huge, as shown above. The UC site claims a plant can spread 10 feet from its root system. I switched my simple pulling to digging out as much as I can of each plant. Once you know what to look for, they are very easy to spot in a hunk of upturned dirt--they are thick, white, and make weird 90-180 degree turns, which can make them difficult to simply pull out of the soil without breaking off.

So far I haven't seen a significant decline in the number of bindweed found per day, but I can only assume it will eventually start working. The graph below (May 12-June 5; update on June 26 is below) shows the number of bindweed that I picked/dug up per day. The decline in the last few days could be a number of different factors--perhaps my hard work is paying off, perhaps I didn't spot as many as I should have, or the cooler nights might be inhibiting their growth (the last several nights have been in the high 50s, compared to nights in the upper 60s & low 70s for the previous few weeks).

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Update: June 27, 2018 (3 weeks later)

For the first 2 weeks of this project, from May 12-May 24, I was only picking the bindweed from the ground up, i.e., I was not digging up the root system. Starting May 25, seeing no clear decline in the counts, I began digging up the easy roots. Again, as the count failed to significantly decline, I have become more aggressive in my digging. The picture above shows the perpendicular rhizome--it is fairly easy for the vertical component to snap off, leaving the horizontal rhizome intact. While one web site claims that picking the stems will "exhaust the roots," they provided no evidence for this, or if true, how long it might take. The picture below shows how large these systems can become. In this picture, I show what was under a stone block--bindweed kept coming up from along the sides, and when I finally moved the block, this was underneath. The graph below that shows the count to date, along with a trendline. As can be seen from the trendline, the daily count decreases, but very slowly.

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Update: July 26, 2018 (7 weeks from the original post)

July in Indianapolis this year was very hot and dry. The various cover plants in my yard started to shrivel and I had larger patches of dirt. My twice-daily bindweed digs have significantly decreased, from the 50s at the beginning of July, to the 20s by the end of July. However, I have no way of proving it is the digs, or simply the dry heat, or even just a natural decrease for this time of year. I have caught each plant before they reach 6", mostly before 3", and no plants have flowered. I think I have increased my proficiency at digging out the white, fleshy rhizomes, although sometimes they are too deep to find efficiently, especially in the grassy areas. One artifact in the data, is that I realized I have been digging up 2 types of bindweed--field and hedge. The former has lighter colored and smaller leaves

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Update: Sept 4, 2018 (6 weeks from the last post, 15 weeks since the original post)

While the end of July brought daily dig totals down to the 20s, increased rain and cooler temperatures bumped them back up into the mid-30s for two weeks, finally falling into the mid teens by the end of August. The trajectory seems to continue a steady decline as seen in the most recent graph. Using a regression model since the beginning of the count (May 19; the regression equation is on the chart), I should have already dropped down to 10 plants per day on Sept 2, while in actuality I remain in the mid-upper teens as of Sept 4. However, the decline of the count seems to have leveled off around mid-July, so a revised equation, beginning from July 20, predicts that I should average 10 plants/day by Sept 15, and 5 plants/day by Oct 5. By that late in the year, it would difficult to differentiate the natural decline from digging the plants up versus the declining temperatures and shorter days. Finally, it seems worth noting, that as mentioned in my previous update, I have 2 types of bindweed, field & hedge, and that while initially I was almost exclusively picking field bindweed, now it's about half & half.

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Update: Oct 25, 2018 (7 weeks from the last post, 5 months since the original post)

This is the final report, at least for 2018. Over the last 10-days I have only found 3 bindweed. I would claim success, but my guess is that the significantly cooler temperatures and shorter days are the cause. I can only hope that by May of next year I will have far fewer bindweed than I had this year. At least one internet source claimed that the rhizomes can last for 3 years, which, if correct, means that I have another 2 years of digging up bindweed. However, about half-way through this year's experience, I began a concerted effort to dig all the way down to the rhizome. They are tricky to get, since they grow horizontally, and the plants snap off the rhizome with too much tugging, including the movement of digging down to get the rhizome, many of which were more than 6" below the surface. Here is the final graph for this year.

Slavery in Mississippi, Native Land Act in South Africa

In the Africa class I teach, a student recently asked me about a map I showed about demographics in South Africa--I was oblivious to the question that was being asked, and it took several hours afterwards for me to figure out that it was a great question that needed to be explored further. At issue is the Native's Land Act of 1913--it legally mandated that Black South Africans could only live in 7% of the country, despite the fact that Whites were only 22% of the population. This law was not repealed until 1991, at which point, Whites were only 11% of the population. The first map shown here is the country of South Africa, and the areas where Blacks were legally allowed to live. They were not allowed to be in any other areas of the country, unless they had work permits, and this racial movement through the country was strictly policed.

A second map shows the racial population distribution as of the 1970 South African Census. In hindsight, the student's confusion was obvious--if non-White South Africans were only allowed to live in certain locations in the eastern part of South Africa, why were there such high concentrations of non-Whites all throughout South Africa?

The simplest response, but one that is least informative, is that there were traditional nomadic groups, primarily in the West, that were allowed to continue their ways of life. They did not own property, and interacted very little with other segments of the population. They largely resided on lands that were otherwise not very useful to White South Africans, such as the Kalahari Desert, as shown in this third (land use) and fourth map (vegetation).

However, arguably a far better answer, in the context of the Apartheid regime instituted in 1948 by the National Party, is that high populations of non-Whites were allowed on White lands because of their use as cheap labor. At first glance, it might seem impossible to have such a high population of non-Whites in such large territories of South Africa, while the National Party maintained their tight grip on the country, and were to be able to impose their brutal, racist ideology. However, this same pattern was seen in the United States under slavery.

In order to demonstrate this I constructed a county-level map of Mississippi from 1860, using a GIS shapefile that was generously sent to me by the state GIS office (at the time of writing, their automated web site was not working--I notified them of the problem, so it may be fixed by the time this analysis is posted). Merging this file with the 1860 Census county-level data, produced the following map of Mississippi. As can be seen, a number of counties were over 75% slaves--the numbers shown in each county represent the total population in that county that were slaves in 1860. In fact, 55% of the total population of Mississippi in 1860 were slaves. With this example in mind, it should be easier to understand how such a large section of South Africa in 1970 could be non-White, when only Whites were allowed to own property in, or even legally be in (without a work permit), 90% of the country.

Two-Week Weather Prediction Accuracy

Last month I posted an analysis of the accuracy of several weather forecasting services, focusing just on temperature predictions. By the time they got to Day 7 in their predictions, the various forecasters were from 8-13 degrees (F) off from the actual temperatures for that day. The goal of this new analysis is to test predictions after that--from day 7-16, their 'long-range' forecasts. Most sites do not offer this service. For this test, I used Accuweather & The Weather Channel. Additionally, I compared those predictions to the historical averages provided by the National Weather Service. As can be seen in the charts, their long-range forecasts ranged from 4-12 degrees (F) off, and simply going by the historical averages were about as accurate as long-range predictions.

Data & Methods
For this analysis, I collected high and low temperature predictions from Accuweather and The Weather Channel from Jan 18-Jan 27 (2018), and historical temperature averages from the National Weather Service (NWS). Temperatures were typically collected at the same time of day, around 10am. I missed one day, Jan 19, so this analysis represents 9-days of forecast data, followed by another two weeks (until Feb 11) of recording the actual high-low temperatures as documented by the NWS.

For each of the 9 days of collecting forecast data, I recorded the predicted high and low temperatures the two services provided for days 7-16 into the future. Interestingly, starting with the 16th day of predictions, The Weather Channel no longer reported an attempt to predict the temperature, but simply reverted their forecast to the historical average temperature. Accuweather continued to provide a unique forecast for 3 months into the future.

Analysis & Results
The statistical analysis is based on taking the (absolute value of the) average difference of the forecasted temperature compared to the actual temperature over Days 7-16 into the future. That difference is plotted in Graph 1. Then the average of temperature deviations for all 10-days is plotted in Graph 2.

As can be seen in Graph 1, the deviations from the predicted versus actual high & low temperatures range from 4-12 degrees (F) over the course of the 10 days of predictions. What is striking in the graph is that there is no trend in the data--in other words, the further into the future the forecast goes, the predictions do not get worse (or better). This implies that by Day 7, the accuracy of the temperature forecasts are as good (or bad) as they are going to get from that point on, and that accuracy is not very good. While there is significant deviation in how badly the forecasts are off over the course of the 10-days of predictions, for example, Accuweather's low predictions range from being 5-degrees off at Day 8, versus 12-degrees off on Day 12, their low-temperature forecast did not continue to worsen--in fact, it got better. Similarly, on Day 7, The Weather Channel's low-temperature prediction was almost 10-degrees off, but decreased to about 7-degrees of after that, reaching a 6-degree deviation by Day 16. This lack of a trend implies a high-degree of randomness in the models used to predict temperatures by Day-7 into the future.

As can be seen in Graph 2, the average deviation in the forecasted temperatures over the 10-days of predictions was about 7-degrees off for both the lows and the highs. The average deviation from the historical averages is also about 7-degrees. This implies that with the current technology and weather modelling, the long-range temperature forecast is no better than simply relying on the historical averages, at least for the low-temperatures. The high-temperature predictions were slightly better--The Weather Channel's deviation was only 6-degrees off, while the historical averages were 9-degrees off. However, my guess is that this is simply a data-collection anomaly--in other words, collecting data over a longer time-perioud would likely have erased these differences.

Conclusion
While this sample size is limited, it seems reasonable to conclude that the accuracy of long-range temperature forecasts (defined as 7-16 days into the future) is no better than simply relying on the historical temperature averages. While sites like Accuweather provide temperature forecasts as far as 3-months into the future, those forecasts likely are as useless as flipping a coin to guess the temperature, or reading your horoscope to predict how your day will go.

One question I did not pursue, is the impact of climate change on the accuracy of 'historical average' temperatures. For example, assuming that climate has been changing over the last century, what if, instead of using the entire historical average highs and lows as provided by the NWS (I do not know how far back they take their averages), we simply used the highs and lows for the last 10 years? Would that data be closer to the actual temperature than the current historical average?

Temperature Prediction Accuracy for Five Weather Services

With the recent arctic-temperatures here in Indiana, I wondered how accurate weather services were at predicting high and low temperatures. This study represents 12-days of data, collecting 7-day temperature predictions from 5 different weather prediction services: local NBC-affiliate WTHR, Intellicast, weather.com, the National Weather Service (NOAA), and Accuweather. All of these predictions are available online, and I have no specific reasons for choosing these services, except that over the years I have placed them in my browser bookmarks.

The data shown in the graph represents an average of the high and low temperature predictions for the given day, up to 7 days ahead, in Fahrenheit. Some services offer predictions 10 days or more, but two (WTHR & NWS) only offer only 7 days of predictions, so I chose that as my limiting factor. One exception to this is that WTHR and NWS do not offer the 7th-day low prediction (at least not when I collected the data at about 9 AM each day), so the graph below represents a full 7-day prediction for the other 3 services, but only represents 6.5 days for WTHR and NWS. Further, while the data was collected for a total of 12 days, there are not 12 days of data for each of the 7 days of predictions: there are 12 data points for day 1, 11 data points for day 2, and so on, until there are only 6 data points for day 7. The high and low temperature for each day comes from the NWS official record (click "Get Climate Data" for January 2018--a pdf of the month's summary so far will download). The temperature deviations on the graph represent an average of the difference between the high and low temperatures predicted, versus the actual high and low temperatures as recorded by the NWS.

As  can be seen, all services perform about the same from Day 1-Day 5, about 3-6 F deviation for each day, on average, although the local NBC-affiliate, WTHR, performs slightly worse by the 4th-5th days. By the 6th day, WTHR and the NWS have clearly started to diverge, and by day 7, all services range from 8-13 F from the actual temperature, with the NBC local-affiliate being by far the least successful, and Intellicast and Weather.com being the most. The 7th-day temperature deviance is even more striking for WTHR and NWS given that they alone do not provide a 7th-day low temperature prediction, but the other services do, yet they still perform better.

This was different from what I expected--I presumed that the local TV station and NWS predictions would be the best, since they represent local experts on-the-ground making a prediction, whereas the other web-based services I presume are simply mass algorithms produced by a computer for each zip code. However, my assumptions about how the data is produced by each service may not be correct. Further, there may be differences in how each day's temperatures are differentiated. For example, in the past, WTHR used to cut-off it's daily low temperatures at midnight, but that has now changed, so that the low temperature prediction extends into the next morning. The lowest nightly temperature often is not until about 6-7 AM. In a technical sense, this is the "next day's" temperature low, however, intuitively, when we look at a low prediction, we are expecting the lowest temperature for a given "next night," not the "previous early morning." The prediction services do not specify when their cut-off time for low predictions are, however, they seem to be relatively consistent. However, this may impact how accurate my data collection indicates each service being. Further, there may be micro-geographic differences in where temperatures are collected that produce significantly different results--northern Indianapolis may be cooler than southern Indianapolis. The services do not specify the locations from where they provide their temperatures. Since the services do not all provide a "past-observations" feature, my decision was to only gauge past observations from the National Weather Service to compare all services. This clearly does not benefit the NWS predictions outcome in this study, given that they perform the 2nd-worst of all 5 services.