text:
A quick look at GHCN version 4
GHCN version 4 beta is available. Using the GHS population dataset the ~27000 GHCNV4 sites were filtered to collect only rural stations. GHS combines two datasets, a 10meter built surface satellite dataset and a human population dataset. https://ghsl.jrc.ec.europa.eu/. using site locations the population within 10km of the site was extracted. two cases were considered: A case where rual was defined as less than 16 people per sq km, and a case where there were less than 7 people per sq km, this filtering led to two subsets of the ~27K ghcnv4 stations. One with 15K stations and a second with 12K stations. The temperature data used was the unadjusted monthly T Avg.
Modis QC Bits
In the course of working through my MODIS LST project and reviewing the steps that Imhoff and Zhang took as well has the data preparations other researchers have taken ( Neteler ) the issue of MODIS Quality control bits came up. Every MODIS HDF file comes with multiple SDS or multiple layers of data. For MOD11A1 there are layers for daytime LST, Night LST, the time of collection, the emissivity from ch 31 and 32, and two layer for Quality Bits. The various layers of MODIS11A1 file is available here. Or if you are running the MODIS package, you can issue the getSds() call and pass it the name of a HDF file.
As Neteler notes the final LST files contain pixels of various quality. Pixels that are obscured by clouds are not produced, of course, but that does not entail that every pixel in the map is of the same quality. The SDS layers for QC — QC_day and QC_Night– contain the QC bits for every lat/lon in the LST map. On the surface this seem pretty straighforward, but deciding the bits can be a bit of a challenge. To do that I considered several source.
2. The Modis groups user guide
4. LP DAAC Tools, specifically the LDope tools provided by guys at MODLAN
I will start with 4, the ldope tools, If you are a hard core developer I think these tools would be a great addition to your bag of tricks. I won't go into a lot of detail, but the tools allow you to do a host of processing on HDF files using a command line type interface. They provide source code in c and at some point in the future I expect that I would want to either write an R interface to these various tools or take the C code directly and wrap it in R. Since they have the code for reading HDF directly and quickly it might be a source to use to support the direct import of HDF into raster. However, I found nothing to really help with understanding the issue of decoding the QC bits. You can manipulate the QC layer and do a count of the various flags in the data set. Using the following command on all the tiles for the US I was able to poke around and figure out what flags are typically reported
comp_sds_values -sds=QC_Day C:\\Users\\steve\\Documents\\MODIS_ARC\\MODIS\\MOD11A1.005\\2005.07.01\\MOD11A1.A2005182.h08v05.005.2008040085443.hdf
However, I could do the same thing simply by loading a version of the whole US into raster and using the freq() command on raster. Figuring about what values are in the QC file is easy, but we want to understand what they mean. Let's begin with that list of values. By looking at the frequency count of the US raster and by looking at a large collection of HDf files I found the following values for the QC layer
values <-c(0,2,3,5,17,21,65,69,81,85,129,133,145,149,193)
The question is what do they mean. The easiest one to understand is 0. A zero means that this pixel is highest quality. If we want only the highest quality pixels, then we can use the QC map, turn it into a logical mask and apply it to the LST map such that we only keep pixels that have 0 for their quality bits. There, job done!
Not so fast. We throw away many good pixels that way. To understand the QC bits lets start with the table provided
If you are not clear on how this works every number, every integer in the QC map is an unsigned 8 bit integer. The range of numbers is 0 to 255. The integers represent bit codes which requires you to do some base 2 math. That is not the tricky part. The number 8 for example would be "0001" and 9 would be 1001. If you are unclear about binary representations of integers I suppose google is your friend.
Given the binary representation of the integer value we are then in a position to understand what the quality bits represent, but there is a minor complication which I'll explain using an example. Let's take the integer value of 65. In R the way we turn this into a binary representation is by using the call intToBits.
[1] 01 00 00 00 00 00 01 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
[25] 00 00 00 00 00 00 00 00
We only need 8 bits, so we should do the following
> intToBits(65)[1:8]
[1] 01 00 00 00 00 00 01 00
and for more clarity I will turn this into 0 and 1 like so
> as.integer(intToBits(65)[1:8])
[1] 1 0 0 0 0 0 1 0
So the first bit ( or bit 0 ) has a value of 1, and seventh bit ( bit 6 ) has a value of 1. We can check our math 2^6 =64 and 2^0 = 1, so it checks. Don't forget that the 8 bits are numbered 0 to 7 and each represents a power of 2.
If you try to use this bit representation to understand the QC "state", you will go horribly wrong!. It's wrong because HDF files are written in big endian format. If you are not familiar with that, what you need to understand is that the least significant bit is on the right. In the example above the zero bit is on the left, so we need to flip the bits so that bit zero is on the right . Little Endian goes from right to left, 2^0 to 2^7. Big Endian goes left to right 2^7 to 2^0.
Little Endian: 1 0 0 0 0 0 1 0 = 65 2^0 + 2^6
Big Endian 0 1 0 0 0 0 0 1 = 65 2^6 + 2^0
The difference is this:
If we took 1 0 0 0 0 0 1 0 and ran it through the table, the table would indicate that the first two bits 10 would mean the pixel was not generated, and bit 1 in the 6th position would indicate an LST error of <= 3K. Now flip those bits
> f<-as.integer(intToBits(65)[1:8])
> f[8:1]
[1] 0 1 0 0 0 0 0 1
This bit order has the first two bits as 01 which indicates that the pixel is produced, but that other quality bits should be checked. Since bit 6 is 1 that indicates the pixel has an error > 2Kelvin but less than 3K
In short, to understand the QC bits we first turn the integer into a bit notation and then we flip the order of the bits and then use the table to figure out the QC status. For MOD11A1 I wrote a quick little program to generate all the possible bits and then add descriptive fields. I would probably do this for every Modis project I worked on especially since I dont work in binary everyday and I made about 100 mistakes trying to do this in my head.
QC_Data <- data.frame(Integer_Value = 0:255,
Bit7 = NA,
Bit6 = NA,
Bit5 = NA,
Bit4 = NA,
Bit3 = NA,
Bit2 = NA,
Bit1 = NA,
Bit0 = NA,
QA_word1 = NA,
QA_word2 = NA,
QA_word3 = NA,
QA_word4 = NA
for(i in QC_Data$Integer_Value){
AsInt <- as.integer(intToBits(i)[1:8])
QC_Data[i+1,2:9]<- AsInt[8:1]
QC_Data$QA_word1[QC_Data$Bit1 == 0 & QC_Data$Bit0==0] <- "LST GOOD"
QC_Data$QA_word1[QC_Data$Bit1 == 0 & QC_Data$Bit0==1] <- "LST Produced,Other Quality"
QC_Data$QA_word1[QC_Data$Bit1 == 1 & QC_Data$Bit0==0] <- "No Pixel,clouds"
QC_Data$QA_word1[QC_Data$Bit1 == 1 & QC_Data$Bit0==1] <- "No Pixel, Other QA"
QC_Data$QA_word2[QC_Data$Bit3 == 0 & QC_Data$Bit2==0] <- "Good Data"
QC_Data$QA_word2[QC_Data$Bit3 == 0 & QC_Data$Bit2==1] <- "Other Quality"
QC_Data$QA_word2[QC_Data$Bit3 == 1 & QC_Data$Bit2==0] <- "TBD"
QC_Data$QA_word2[QC_Data$Bit3 == 1 & QC_Data$Bit2==1] <- "TBD"
QC_Data$QA_word3[QC_Data$Bit5 == 0 & QC_Data$Bit4==0] <- "Emiss Error <= .01"
QC_Data$QA_word3[QC_Data$Bit5 == 0 & QC_Data$Bit4==1] <- "Emiss Err >.01 <=.02"
QC_Data$QA_word3[QC_Data$Bit5 == 1 & QC_Data$Bit4==0] <- "Emiss Err >.02 <=.04"
QC_Data$QA_word3[QC_Data$Bit5 == 1 & QC_Data$Bit4==1] <- "Emiss Err > .04"
QC_Data$QA_word4[QC_Data$Bit7 == 0 & QC_Data$Bit6==0] <- "LST Err <= 1"
QC_Data$QA_word4[QC_Data$Bit7 == 0 & QC_Data$Bit6==1] <- "LST Err > 2 LST Err <= 3"
QC_Data$QA_word4[QC_Data$Bit7 == 1 & QC_Data$Bit6==0] <- "LST Err > 1 LST Err <= 2"
QC_Data$QA_word4[QC_Data$Bit7 == 1 & QC_Data$Bit6==1] <- "LST Err > 4"
Which looks like this
Next, I will remove those flags that don't matter. All good pixels, all pixels that don't get drawn, and those where the TBD bit is set high. What I want to do is select all those flags where the pixel is produced but the pxel quality is not the highest quality
FINAL <- QC_Data[QC_Data$Bit1 == 0 & QC_Data$Bit0 ==1 & QC_Data$Bit3 !=1,]. Below see a part of this table
And then I can select those that occur in my HDF files.
Looking at LST error which matters most to me, the table indicates that I can use pixels that have a QC value of 0,5,17 and 21. I want to only select pixels where LSt error is less than 1 K
Very quickly I'll show how one uses raster functions to apply the QC bits to LST. using MODIS R and the function getHdf() I've downloaded all the tiles for US for every day in July 2005. For July 1st, I've used MRT to mosiac and resample all the SDS. I use Nearest neighbor to insure that I dont average quality bits. The mosaic is the projected from a SIN projection to a Geographic projection ( WGS84) and a geotiff output format. Pixel size is set at 30 arc seconds
The SDS are read in using R's raster
## get every layer of data in the July1 directory
SDS <- list.files(choose.dir(),full.names=T)
## for now I just work with the Day data
DayLst <- raster(SDS[7])
DayQC <- raster(SDS[11])
## The fill value for LST is Zero. That means Zero represents a No Data pixel
## So, we force those pixels to NA
## Units in LST need to be scaled to be put into Kelvin. The adjustment value is .02. See the docs for these values
## every SDS has a different fill value and a different scaling value.
DayLst <- DayLst * .02
Now, we can plot the two raster
plot(DayLst, main = "July1 2005 Day")
plot(DayQC, main="Day Quality COntrol")
And I note that the area around texas has a nice variety of QC bits, so we can zoom in on that
Next we will use the QC layer and create mask
m <- DayQC
m[m > 21]<-NA
m[ m == 2 | m== 3]<-NA
Good <- mask(DayLst, m)
For this mask I've set all those grids with QC bits greater than 21 to NA. I've also set QC pixels with a value of 2 and 3 to NA. This step isnt really necessary since QC values of 2 and 3 mean that the pixel doesnt get produce, but I want to draw a map of the mask which will show which bits are NA and which bits are not NA. The mask() function works like so. "m" is laid over DayLst. bits that are NA in "m" will be NA in the destination ("Good") if a pixel is not NA in "m" and has a value like, 0 or 17 or 25, then the value of the source (DayLst) is written into the destination.
Here are the values in my mask
> freq(m)
value count
[1,] 0 255179
[2,] 5 12
[3,] 17 8539
[4,] 21 3
So the mask will "block" 189147 pixels in DayLst from being copied from source to destination, while allowing the source pixels that have QC bits of 0,5,17,21 to be written. These QC bits are those that represent an LST error of less than 1K
When this mask is applied the colored pixels will be replaced by the values in the "source"
And to recall, these are all the pixels PRIOR to the application of the QC bits
As a side note, one thing I noticed when doing my SUHI analysis was that the LST data had a large variance over small areas. Even when I normalized for elevation and slope and land class there were pixels that were 10+ Kelvin warmer than the means. Essentially 6 sigma cases. That, of course lead me to have a look at the QC bits. Hopefully, this will improve some of the R^2 I was getting in my test regressions.
Modis R: Package tutorial
The MODIS package for R
For people who work in GIS in R there has been a bit of challenge in working with data from Modis. I'll use an example from my own work on UHI to illustrate. A while back I decided that I wanted to look at albedo data from MODIS. If you want to get MODIS data there are basically two approaches. You can use the web services like REVERB which is basically a web interface that allows you to construct queries for a wide variety of data ( or similar services shown here ) or you can download files directly from the datapool ftp.
For my Albedo project I decided to download directly from ftp because I could write a simple program in R to collect the entries in the ftp directories and then download them over the course of a few hours. It's essentially the same process I use in my study of SUHI for small cities. Getting the data locally is not a big problem, except for the storage issue. You can imagine what it would take to download 90 days of LST data for the US. with 10-14 tiles of data required that ends up being a nice chunk of data.
The next challenge is getting MODIS data out of its native format into a format that is friend for R GIS packages. In the case of Albedo that data was stored in a format known as "HDF" or HDF-EOS. I work with the raster package and raster ( along with rgdal) have no way of reading HDF directly. This meant I had to find a way to transform HDF into a more common format like "geoTiff". There was also the issue of tiling multiple hdf files into a single raster. Unlike other datasets that might give you a monolithic file of the whole world, the MODIS collections are distributed as tiles using their tiling system.
To solve this problem there are a couple tools: MRT or the modis reprojection tool. or GDAL, the Geospatial Data abstraction Library. For Albedo I decided to use GDAL and I used the distribution of GDAL provided by FWTools. With a few hundred albedo files I had to write a script to load every Hdf file and transform it to a 'geotiff' file. That worked fine, but now I had a set of R code for doing my statistics and a "bat" file for controlling Gdal. With my SUHI project I decided to use MRT. In that case I used the GUI they provide to select all the files, mosaic them, crop them, resample and reproject. I quickly learned that duplicating the same steps with a GUI is not always easy. Further, if I am trying to create work that is reproduceable, it makes it hard. Plus I could not see using that tool to create 365 maps for every day in the year.
What's needed is an R package that allows us to write R code and achieve the same results as we get with GDAL or MRT. We want to write R code that controls these external applications and tools. That's what MODIS R does.
The team: The MODIS R team consists of Matteo Mattiuzzi, Jan Verbesselt, Forrest Stevens, Tomislav Hengl, Anja Klisch, Bradley Evans and Agustin Lobo. Some of the seminal work was done by Tomislav at his wonderful page
My Tutorial. I had some discussions with Matteo who is the package maintainer and suggested that I might do a tutorial to help people get started with the package. At this stage the package is stable, so I decided to start a tutorial. The way I work is pretty simple. As I try new things I write them up. For now the tutorial starts with the basics of R, R studio and preparing your system for MODIS R. That includes downloading a the tools and testing them out and finally downloading and building a WINDOWS version of MODIS R.
Then I will start to go through the API and test and explain all the calls in more depth than the manual and write some sample code.
A note. If you have questions about what I am doing go ahead and leave comments. I can collect up questions and those I cant answer I can pass on to the maintainers.
Terrain effects on SUHI estimates
Zhang and Imhoff (2010) pdf here utilized NLCD impervious surface area (ISA), Olson biomes, and MODIS Land Surface temperature (LST) to estimate the magnitude of UHI in large cities across the US. Peng employed a similar approach in studying 419 large cities ( population greater than 1m ) around world. Peng's work suggests a limit or threshold to the Surface Urban Heat Island (SUHI) effect in that he found that city size and population was not an effective predictor of SUHI for very large cities. Imhoff's study on somewhat smaller cities suggest there is a range at which city size is an effective predictor of SUHI
In addition, Zhang and Imhoff found that SUHI is related to the surrounding environment. A city that is developed in a forest biome, for example, will show a greater SHUI than one embedded in a desert.
Zhang and Imhoff's curve is, as they note, somewhat similar to Oke's postulated curve for the relationship between population and the Urban Heat Island ( UHI as measured by air temperature as opposed to surface temperature ) and it shares a certain weakness with Oke's analysis with respect to the sample size at very small populations/small size. My goal in this study is to enlarge the sample of smaller sized cities and towns and refine the estimate in much the same way that Peng focused his study on cities over 1M in population. My working hypothesis is that just as there is a plateau of SUHI above a certain size that there may be a plataeu at smaller sizes, suggesting that a logistic relationship might be a better representation of the underlying process.
Sites were selected for study by utilizing MODIS ( MOD12Q1 2005) (15 arc second) Landcover data. The 17 class landcover product was reprojected from its native format into a WGS84 GeoTiff using nearest neighbor filtering and with a 15 arc second ( 500 meter) output format. The landcover data was reclassifed into urban and non urban pixels and a cluster analysis was performed to create patches of continguous urban pixels. Various size patches, from 10 to 100sq km, of urban areas were then selected for further processing. In addition, Census gazetter information was used to select locations of even smaller size from .5sqkm to 10 sq km. A 1km Digital Elevation model derived from 90m SRTM data was utilized to compute elevations, slopes and aspects. Finally, 8 day 1km Day LST and Night LST from MODIS was used. For this pilot study one 8 day period from July 4th 2006 was utilized ( MOD11A2 v 5 ) . The LST product was reprojected from its native format into a WGS 84 Geotiff using nearest neighbor filtering and a 30 arc second (~1km) output format.
Census gazetter information was used to select a sample of small towns and CDPs that had areas less than 10 sq km and no substantial water area within the areas boundaries. That list was culled further using NLCD 2006 impervious area and Nightlights derived ISA to ensure that the small towns were at least 10km from larger urban areas. Urban areas larger than 10 sqkm were selected by created an urban/ rural mask from MODIS landcover. The mask was processed by finding patches of urban areas using a queens case clustering approach. The urban areas were sorting according to size and then classified according to their isolation from other urban areas. The goal of this processing was to identify urban areas that were surrounded predominately by rural pixels. A 20 km boundary zone was established around every urban patch and to qualify for selection 99% of the pixels outside the urban zone had to be classified as rural by MODIS landcover. This selection process resulted in a database of 99 sites. Given what I've learned in this process I will probably loosen the restriction regarding "satellite" urban pixels surrounding the urban core and include more sites in the final analysis.
The selected sites where then processed as follows. For every location a distance map was created for a 100km radius around the site center. The distance map provided the distance that every rural pixel was from the closest urban pixel. In the end, analysis showed that the distance from an urban pixel was not a significant driver in the land surface temperature of the rural pixel. With regards to Surface Air Temperatur (SAT) and UHI measures, it is likely that distance from the nearest urban pixel would be significant due to horizontal advection, but with Land Surface Temperture and SUHI, the physical ability of heated land surface in the urban area to influence the LST in adjacent rural cells is apparently limited. In the final analysis, nevertheless, I will probably add a screen to remove rural pixels that are within 2km of a urban pixel.
After distance maps are created out to 100km, the elevation data is processed within the same radius. In addition to retrieving the elevation for each cell the slope and aspect of the cell is generated. Zhang and Imhoff (2010) utilized elevation as screen for selecting both urban and rural pixels. Their screen required that a pixel be with a +-50 meter window of the mean urban pixel elevation. As the results below show elevation does have an impact on LST so that before comparing urban to rural pixels some form of normalization must be used. In addition to elevation, however, the slope and aspect of a grid cell can influence the LST. In examining differences between the ASTER LST product and the MODIS LST product Liu (2009) pointed to terrain effects such as slope as part of the reason for differences between these products. The difference in slopes between urban pixels and rural pixels is evident and generally speaking urban pixels tend to have lower slopes. The difference in slope, of course, will impact the amount of direct solar radiation that a given cell "sees" and the interaction between the slope and the view angle of the sensor apparently impacts the emissions sensed. The slope effect is small but statistically significant. For this analysis no screen was employed, however a screen of less than 1 degree slope will eliminate only 15% of urban pixels and 20 percent of rural pixels. A screen at .5 degrees eliminates 35% of all urban pixels and 40% of all rural pixels.
After terrain processing the Day LST and Night LST is retrieved for every pixel within 20km of the site center. As this radius is less than the 100km radius used for distance map and terrain processing we are assured of having good values for both the distance map and the slope and aspect maps at the 20km boundary. The cell centers of the LST product are then used to retrieve the corresponding distance map values, landcover values, and terrain values.
Zhang and Imhoff(2010) approached the problem estimating SUHI by classifying the urban pixels using ISA and the rural pixels using Olson Biomes. The approach I take is slightly different. The temperature product MODIS LST which we both use is a product that estimates the LST based on other MODIS products. Most importantly the split window algorithm uses estimates of emmisivity that are derived from Modis landcover product. Such that if MODIS Landcover believes the pixel is urban or croplands it does not matter what the ISA product thinks the pixel is and it doesnt matter how Olsen classified the pixel. Modis Landcover is used in the processing chain to to determine the LST and is the more appropriate classification tool. What we also see here is that Urban/Rural differences are to some extent built into the results. If Modis Landcover classifies a pixel as urban that classification will drive the emissivity estimate which in turn drives the output of the split window alogorithm employed to determine LST.
A box plot of Daytime LST by landcover type shows the range of temperature differences across the various land classes. It should be noted that this sample has not been screened for elevation, slope, or latitude and includes all pixels processed for the 99 sites
The x axis represents the IGBP code for land cover class, but its instructive to ask if the urban class is identifiable on inspection. If you think that class 7 is urban you are wrong. class 7 is open shrublands. Class 10 is grasslands. Urban is class 13. Below is the nighttime LST
What both figures illustrate is that the differential between Land surface temperature in the urban setting and Land surface temperature in the non urban setting is not single number. The implication for UHI studies is clear. The difference in temperature between a urban area and a rural area depends as much upon the rural setting as it does upon the urban setting. It is possible to select areas, such as grasslands and open shrubland that are both rural and warmer than the urban setting. It is also possible to amplify the difference by selecting other classes such as wetlands ( class 11 ) which are at the cooler end of rural landscapes. And its possible to get confusing answers by selecting croplands or mixed croplands ( class 12 and 14 ) which have high variability where some exhibit warmer LST and some exhibit cooler LST than urban areas.
At this stage with only 8 days of temperature there is not enough data to draw any conclusions. So the next step will be to download an entire year of LST daily data to construct seasonal averages for these sites. One thing we can do as a sort of sanity check is some simple regression on the entire dataset. Below, we can see that 40% of the variation is explained by looking at the urban area ( which lumps all rural pixels together with and area of zero ), elevation, latitude, and slope.
lm(formula = Sites$Day ~ Sites$UrbanArea + Sites$Elevation +
Sites$y + Sites$Slope)
Min 1Q Median 3Q Max
-34.419 -3.099 -0.543 2.947 20.985
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.196e+02 8.395e-02 3806.62 <2e-16 ***
Sites$UrbanArea 5.640e-02 2.046e-03 27.57 <2e-16 ***
Sites$Elevation 7.864e-03 1.941e-05 405.24 <2e-16 ***
Sites$y -4.809e-01 2.140e-03 -224.73 <2e-16 ***
Sites$Slope -7.718e-01 5.962e-03 -129.44 <2e-16 ***
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.202 on 272367 degrees of freedom
(12251 observations deleted due to missingness)
Multiple R-squared: 0.4448, Adjusted R-squared: 0.4448
F-statistic: 5.456e+04 on 4 and 272367 DF, p-value: < 2.2e-16
> model <- lm(Sites$Night~Sites$UrbanArea+Sites$Elevation+Sites$y+Sites$Slope)
> summary(model)
lm(formula = Sites$Night ~ Sites$UrbanArea + Sites$Elevation +
Sites$y + Sites$Slope)
Min 1Q Median 3Q Max
-23.2246 -1.3937 0.0242 1.5774 7.7279
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.080e+02 3.670e-02 8391.708 < 2e-16 ***
Sites$UrbanArea 2.634e-02 8.862e-04 29.724 < 2e-16 ***
Sites$Elevation -1.542e-03 8.662e-06 -178.006 < 2e-16 ***
Sites$y -4.316e-01 9.388e-04 -459.731 < 2e-16 ***
Sites$Slope -1.695e-02 2.667e-03 -6.355 2.08e-10 ***
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.324 on 281416 degrees of freedom
(3202 observations deleted due to missingness)
Multiple R-squared: 0.483, Adjusted R-squared: 0.483
F-statistic: 6.573e+04 on 4 and 281416 DF, p-value: < 2.2e-16
What we see here is that Urban Area has a positive coefficient of roughly .056 for daytime LST and a nighttime coefficient of roughly .026. This approach is different than the approach used by Zhang and Imhoff in that rather than screening for the same elevation, I've accounted for the elevation and slope via regression. Of course, I'll check it both ways. Finally, The estimate for small town UHI provided by Imhoff was on the order of 1.75C for a town of size 1 sq km. In their study they found that UHI was fit by 3.48*log(area) + 1.75. That particular study had only two datapoints for towns at 1km. Looking at the data for the twons with urban areas less than 2km, the figure of 1.75C for Day SUHI can't be supported. The difference in Day LST at that size is effectively zero.
Now that I have the processing chains down to the last few bits I need to push forward on several bits.
1. More small area sites. The restriction I imposed, that 99% of all surounding pixels in a 20 km radius limits the number of small sites that can be found. This restriction was motivated by a concern that distance from an urban pixel might influence the LST. With a suitable buffer around each site I should be able to process more than the 30 or so small sites I have. Also, elevation screening and slope screening also reduces the number of sites that are usable in the end, so I'll need to start with more sites.
2. Increase the sampling out to 50 sq km. Preliminary analysis indicated that there may be a break in SHUI around the 10 sq km mark. Although I had limited data ( 8 days in july when the signal should be high ) The UHI signal appeared to show up more clearly at around 10 sq km, So I should probably focus not only on the sub 10 sq km region, but out to 50. Beyond 50 sq km, my results were pretty much in line with Imhoff, despite the differences in our methods.
3. Eliminate water pixels and wetlands. I believe Imhoff eliminates water pixels. There are a bunch of ways that the data can be cut as one can see from the land class figures.
4. Regress on SUHI directly. This entails processing each site separately. Constructing an average for the small town ( which can be 1-10 pixels) and then construct a rural average across all land classes or some reduced set of land classes. In this case one would want to screen for altitude and slope and perhaps regress with respect to latitude to see of the there is a latitude component. One difficult of course here is that you are averging over a small number of urban pixels and then comapring that to an average over a large number of rural pixels. Kinda messy.
Pilot Study: Small Town Land Surface Temperature
Zhang and Imhoff (2010) pdf here utilized NLCD impervious surface area (ISA), Olson biomes, and MODIS Land Surface temperature (LST) to estimate the magnitude of UHI in large cities across the US. Peng employed a similar approach in studying 419 large cities ( population greater than 1m ) around world. Peng's work suggests a limit or threshold to the Surface Urban Heat Island (SUHI) effect in that he found that city size and population was not an effective predictor of SUHI for very large cities. Imhoff's study on somewhat smaller cities suggest there is a range at which city size is an effective predictor of SUHI
In addition, Zhang and Imhoff found that SUHI is related to the surrounding environment. A city that is developed in a forest biome, for example, will show a greater SHUI than one embedded in a desert.
Zhang and Imhoff's curve is, as they note, somewhat similar to Oke's postulated curve for the relationship between population and the Urban Heat Island ( UHI as measured by air temperature as opposed to surface temperature ) and it shares a certain weakness with Oke's analysis with respect to the sample size at very small populations/small size. My goal in this study is to enlarge the sample of smaller sized cities and towns and refine the estimate in mch the same way that Peng focused his study on cities over 1M in population. My working hypothesis is that just as there is a plateau of SUHI above a certain size that there may be a plataeu at smaller sizes, suggesting that a logistic relationship might be a better representation of the underlying process.
Data from a wide variety of sources was used to identify a set of candidate small cities and towns. The 2010 US census gazetter of places in the US was used to identify town and census designated places (CDP ) with land areas less than 10 sq km. NLCD 2006 ( ISA) 30 meter data and Nightlights derived ISA (1km data ) was used to narrow the list to small towns that are relatively isolated from larger urban areas. For the final processing Landcover provided by MODIS ( MOD12Q1 2005) (500 meter data ) was used to classify urban versus non urban pixels. As noted by Imhoff a cut off at 25% ISA by NLCD standard is consistent with Modis classification of urban pixels. A 1km Digital Elevation model derived from 90m SRTM data was utilized to compute elevations, slopes and aspects. Finally, 8 day 1km Day LST and Night LST from MODIS was used. For this pilot study one 8 day period from July 4th 2006 was utilized ( MOD11A2 v 5 )
Census gazetter information was used to select a sample of small towns and CDPs that had areas less than 10 sq km and no substantial water area within the areas boundaries. That list was culled further using NLCD 2006 impervious area and Nightlights derived ISA to ensure that the small towns were at least 10km from larger urban areas. As Zhang/Imhoff Olson biomes were examined within .5 degrees of the site to ensure that the area existed in a singular biome and not an area that contained more than one biome. The final sample contained 169 sites spread out over the US as depicted below
Individual datasets were then built for every site. MOD12Q1, MODIS landcover was collected for a ~60 km radius around each site. The landcover data was used to create several masks. An urban pixel mask, a rural pixel mask, and a water pixel mask. The urban pixel mask and the water pixel mask were used to create distance maps: Distance from urban pixel maps and distance from water maps. The elevation data was processed to create a slope map and aspect map for each 60km grid. Finally MODIS11A2 LST data was downloaded and reprojected to a geographic projection ( WGS84) at 1km resolution in GeoTiff format. Data "bricks" in Rs package "raster" were created for each site. Each brick consisted of a urban mask, rural mask, water mask, distance maps, terrain maps and temperature maps.
Some fun charts
The purpose of the pilot study was to assemble the data and start to build the analysis code base for a final production run. During that process some of my ideas and approaches to the problem have defininately changed. The first change was the decision to use MOD12Q1. The reason is clear. Looking at the processing chain for MOD11A2 ( LST ) it is clear that this data product relies on MOD12Q1 for its estimate of emmissivity. To put it simply if NLCD thinks the pixel is Urban or impervious and MOD12Q1 thinks the pixel is trees, then MOD11A2 will assume an emissivity of trees to calculate the final LST. The other thing that changed was my decision to include more terrain data than Zhang Imhoff. In their approach they masked off pixels that were 50meter higher or lower than the average in the urban area. In my approach I will look at using that terrain information to explain the variance we see. Finally, I will probably change my method of looking at the 50km radius around each site. In part this was done to make the calculations of distance maps less intense. I Will probably just go ahead and calculate distance maps for the whole US, a process that might take a couple days at 500meter resolution. That will allow me to sample a wider geographic variety of sites than I currently have which tends to be concentrated in the middle of the country and tends to over sample certain areas with many small isolated towns.
Now for some charts. First we see a sample chart from a given "brick" of data:
And next we see the Day LST and Night LST
And for grins we look at the following: The ralationship between Temperature and Elevation for day and night, and below that we have box plots of the temperature versus the land cover types in the RURAL pixels. That is one other difference between Zhang/Imhoff and my work. Where they looked at the "biome" which is really a historical context I will look at the current land cover. The reason is simple: MODIS LST is dependendent opon MODIS landcover in its determination of emissivity and finally temperature.
You should note that I have not looked at the Urban rural difference yet. At this stage in the pilot study I don't want to be looking at any of those results. I just want to get the processing chain bug free and the main routines ironed out. There will be a little more code to pull out the "area" of the small towns but that is pretty trivial. Next step now I think will be finding ways to speed up the calculation of the distance maps.
Sample Input Data.
Just a couple quick examples.
Starting with 30 meter impervious surface
Followed by MODIS Land cover ( "red" is urban )
And finally Day LST
Google earth
More Fun With Modis
I've started a tutorial on using the MODIS package in R the first few steps are here. While I wait on a release of the package I thought I would play around a bit with the MRT tool and see how it worked. Let's recall where we are going. I have an inventory of around 150 small cities and we are going to take a look at SUHI in those locations. I have the DEM data, The Modis Land Class data, Olson Biomes, NLCD 30 meter land class and impervious surface. The last few bits I will need are MODIS LST and perhaps MODIS albedo ( If I get motivated) and EVI. We also have population data from a couple sources and housing data.
The first step to getting MODIS LST data is deciding what Tiles I need and then selecting dates. I had hoped that I might get away with using just a few tiles for the US, but that doesnt look like its going to happen. To figure out which Tiles I need there is a function in the MODIS package called getTile. But before I figured that out I tried to do it on my own. With some help from the geo sig list I have the following to figure out the tiles.
First I get the bounding coordinates for every tile
gring <-"http://landweb.nascom.nasa.gov/developers/sn_tiles/sn_gring_10deg.txt"
GRING <- read.table(gring, skip = 7, stringsAsFactors =FALSE, nrows=648,
GRING <- GRING[!is.na(GRING[,3]),]
DF <- GRING[GRING$h < 15 & GRING$h > 6 & GRING$v < 7 & GRING$v > 3,]
The table is downloaded and then I remove those rows that are NA. Next, I select those parts of the table that relate to CONUS between h(horizontal) 15 and 6 and vertical 7 and 3.
Next, I use Robert Hijmans code for generating a spatialpolygondataframe.
m <- as.matrix(DF)
z <- apply(m, 1, function(x) Polygon(matrix(x[c(3:10, 3:4)], ncol=2, byrow=T)))
n <- names(z)
y <- sapply(1:length(n), function(i) Polygons(z[i], n[i]))
sp <- SpatialPolygons(y, proj4string=CRS('+proj=longlat'))
spdf <- SpatialPolygonsDataFrame(sp, DF)
then we make some corrections for being on a sphere and we read in the lat/lon of all 150 sites
spdfc <- makePoly(spdf)
Inv <- read.table("SiteInventory.inv",stringsAsFactors = FALSE)
The next bit of clumsy code creates a spatial points data frame that has 4 points for every site. I'm going to look +- .5degrees from the site center.
myExtent <- data.frame(lon=rep(NA,4*nrow(Inv)),lat=rep(NA,4*nrow(Inv)))
for( site in 1:nrow(Inv)){
dex <- ((site-1)*4)+1
myExtent$lon[dex]<- Inv$Lon[site] +.5
myExtent$lat[dex]<- Inv$Lat[site] -.5
myExtent$lon[dex+1]<- Inv$Lon[site] +.5
myExtent$lat[dex+1]<- Inv$Lat[site] +.5
myExtent$lon[dex+2]<- Inv$Lon[site] -.5
myExtent$lat[dex+2]<- Inv$Lat[site] -.5
myExtent$lon[dex+3]<- Inv$Lon[site] -.5
myExtent$lat[dex+3]<- Inv$Lat[site] +.5
Finally we will intersect the points with the SpatialPolygonsDataframe and get out the tiles
pts <- SpatialPoints(myExtent)
proj4string(pts)<- proj4string(spdf)
Tiles <- over(pts,spdfc)[1:2]
Tiles <- unique(Tiles)
nrow( Tiles)
Depending on the increment I used (.5 versus .2) I basically had to collect 10-12 tiles to cover my bases for the next step.
With that list of tiles I downloaded the tiles I needed for July4th 2006. In the future with the MODIS package this will happen just by writing code, but for now I did it by hand. With those 12 tiles downloaded I then fired up MRT to mosaic , resample, and reproject the tiles into a monolithic GeoTiff for daytime LST for the whole Conus.
Reading that it we get the following
And then we can zoom in on the midwest and then go down chicago
On the left at lon -87.9 lat 41.65 is the Paw Paw Woods Nature Preserve. Pretty Cool


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By GEORGE AUSTIN
By GEORGE AUSTIN
SWANSEA — In the classroom at E.S. Brown School, fifth grade student Jennifer Velez was reading aloud the story "The Sometimes Friend" as she patted Marco, a pit bull mix dog that has been brought into class to help encourage students to work on their reading skills.
According to special education teacher teacher Marina Jackson, the dog seems to be having a positive impact on improving the reading of children.
"They get so excited and it's another opportunity for them to get to read and practice reading and their confidence is boosted big time," Ms. Jackson said of how the program with the dog has affected the students. "They think they are doing something big and they like to see his reaction."
The program at the school is being provided by Kerri Lopes who is part of a therapy dog team with Marco that comes from Dog B.O.N.E.S. (Building Opportunities for Nurturing and Emotional Support). The program is being run in the self contained classroom for children in grades three through five who have different needs. Ms. Lopes said the animal-assisted program is meant to be a way for children to work on their reading skills in a very non-judgemental environment by reading to the dog.
"If they make a mistake, Marco is not going to tell them they made a mistake," Ms. Lopes said. "There's no pressure."
While the children were reading to him last week, Marco mostly just was laying on a blanket, looking up at them as Ms. Lopes sat by his side. Ms. Lopes gave one of the children some treats to feed Marco after the student finished reading.
"He loves kids and I think he tends to know that when he's around kids, he needs to be calm, cool and collected and they'll really love him," Ms. Lopes said.
When the children say a certain color is the favorite of someone in the story they are reading, Ms. Lopes said she may tell the child at that point that the color is also the favorite of Marco and she said that encourages them.
"I think most kids like an animal of some sort and they just tend to open up to them more," Ms. Lopes said.
Marco has been a registered therapy dog for seven months and has been participating in the reading program at the Brown School for two months. Ms. Lopes is a volunteer for Dog B.O.N.E.S. which is based in Scituate.
Ms. Lopes said "Lemon the Duck" seems to be Marco's favorite book, as well as "Walter the Farting Dog."
Before Marco was adopted and trained to be a therapy dog, he was in a foster home with a 4-year-old boy. Ms. Lopes said the dog had been abandoned and was hit by a car. Marco has a severed spinal cord and gets around with a wheelchair.
Ms. Lopes said when children are put in non-judgemental situations, they tend to want to do more. The Brown School is the first school where Marco has done the reading program. On the first day of class, Ms. Lopes told the students about Marco's accident and how that shows why it is important to look both ways before crossing the street.
Ms. Lopes said the program does not seem to be any more effective on children who have pets at home, although if they do have animals at home, she said after the activity with Marco, she can tell them to read to their pet when they go home. Ms. Jackson said students who do not have pets at home seem to get more excited that the dog is in the classroom. There are 12 stud3nts involved with the reading program with the dog. Last week, Ms. Lopes gave bookmarks with a photograph of Marco on them them to Ms. Jackson to give to the students. Ms. Jackson said Marco is being used to help improve the speed of reading and expression for the children. She lets the students choose books to read to Marco. She said most of them choose books they've read before because they want to read well to Marco and said they take the books home to practice before reading to the dog. She said teachers use repeated readings, poetry and scripts to work on similar reading skills for fluency and comprehension in reading.
"This was another fun, different way," Ms. Jackson said. "It tricks them into it, almost. They love it, so they don't even think about it."
Ms. Jackson has been teaching for 10 years and this is the first time she has utilized a dog in her classroom. She found the idea for the dog in New Bedford where she lives when she came across flyers that were promoting a similar reading program in the city's libraries. She asked Brown School Principal Elizabeth White if she could bring such a program to the school and received the approval. She researched dog reading programs online and found Dog B.O.N.E.S.
"It's even nice just to see them excited about picking up a book," Ms. Jackson said. "There isn't as much hesitation. I even like seeing them talk to him, ' Did you like that book.' It's not judgemental."
Last week was the third visit to the Brown School for Marco and Ms. Lopes and they will be coming back to the school every other week. Ms. Jackson said the dog is quiet while the students are reading which allows for other activities to be done with students in the classroom at the same time. The dog does not seem to be a distraction to the other students who are not reading to him.
"We just have an extra visitor," Ms. Jackson said.
Ms. White said the reading activity with Marco has been "great."
"I think it's such a nice program to have," Ms. White said. "Anything that's going to engage these students in reading is a benefit. It's a win-win."
Ms. White said the students form a connection to the pet. After the first class that Ms. Lopes and Marco came to, Ms. White said one of the students asked to take a book home to read to his hamster.
"That's perfect because that's what we wanted to happen for them, to read more," Ms. White said.

subset: commoncrawl


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Last weekend's Kentucky Derby wasn't more than an hour old before the critics were out in force on Twitter. Forget the fact California Chrome had just become the first California-bred to win the Derby in 52 years. Never mind he's won his past five races by a combined 26 lengths. Some people just don't want to give this horse any credit.
It didn't matter the Lucky Pulpit colt had just disposed of the other 18 runners by 1 3/4 lengths while eased up by jockey Victor Espinoza in the final stages. Espinoza was mindful of saving something for the Preakness Stakes on May 17, but the colt's naysayers had only the final time of 2:03.66 on their minds – the slowest Derby run over a fast track since 1974. Look at that final quarter-mile of 26.21 seconds and tell us he's anything special, they argued.
Art Sherman, at 77 the oldest trainer to saddle a Kentucky Derby winner, doesn't give a rat's you know what about the critics.
"He ran the second fastest Santa Anita Derby ever, and do you realize how many good horses have come out of the Santa Anita Derby and went on to win the Kentucky Derby?" Sherman said. "You can't go by tracks. Every surface is different. You might have a 105 Beyer in one race and a 97 in another. That doesn't mean your horse can't run. On a different surface and the right kind of scenario up front and a bouncy track, he's gonna run. I know the figure guys get all bummed out about different things, but I never pay attention to that.
"The track got a little slow at Churchill Downs with the two hours in-between (races) and the heat. I thought it was a lot faster earlier in the day than it was when we ran the Derby. But I'm from the old school. You don't have to carry a track with you. He's won on three different racetracks already. If you've got the horse and you've got his kind of talent, you can run on just about anything."
Which California Chrome proved on Derby day when he won the race on a track he didn't particularly like. According to Sherman, racing fans will see a whole different colt in the Preakness if he takes to the Pimlico surface better than he did the track at Churchill Downs.
"If he takes to the track at Baltimore, you're going to see a different horse," he said. "If he can come out of there and be fourth going around the turn and fourth down the backside and have a clear path, you're going to see old Chrome perform."
It appears California Chrome will be facing a lot of new challengers on Preakness day. Only a few Derby runners are expected to show up for the second leg of racing's Triple Crown while at least a trio of talented speedsters, including Bayern, Pablo Del Monte and Social Inclusion, are scheduled to run in the Preakness.
"I knew that was going to happen," Sherman said. "They were just waiting, and I can't blame them for wanting to try this race. They figure I'm having to come back in two weeks while there's only a couple of others that ran in the Derby. You've got Bayern, and he's got a lot of speed, and you have another horse from New York (Social Inclusion) that's been hyped back there."
Is Sherman shaking in his boots? Hardly.
"You know, they've all gotten beat," he said. "People are gonna have to realize (California Chrome) is coming off five victories straight and a lot of (these other) horses are still eligible for conditions. There are no bona fide stakes horses in there.
"If you've been in the game as long as I have, you've got to prove yourself. You're not going to get away with an easy-go just because you're fresh coming into this race. (Chrome) is a seasoned, veteran horse right now coming into these races and I think that's going to be a big help for him."
Since he's from the old school, Sherman doesn't like having to bring California Chrome back in two weeks. He likes to give his horses more time between races, but this is the Triple Crown. Critics or not, this colt has a legitimate shot to become the first Triple Crown winner since Affirmed in 1978 and the first Cal-bred ever to sweep the three races.
Like it or not, the Preakness comes two weeks after the Derby and Sherman is going to have to live with that fact. Still, if he had his way, the schedule would be a bit different.
"I really think two weeks from the Derby into the Preakness … I would love to see at least three," Sherman said. "It takes a horse about 11 days to completely recover out of a race, before the blood level and everything is right. It's pushing the envelope a little bit. Some horses have enough talent that they can overcome that.
"I'd like to see the extra week put in. I just think it's hard on the horse. I know it's tradition, but it's grueling. You've got three different tracks, a lot of travel. There's been some super horses win the Triple Crown, but they're aren't too many of them. You'd better have a runner if you're going to take the Triple Crown ride."
Sherman's got that runner and he's on the ride of his life, critics be damned.
Follow Art Wilson on Twitter at @Sham73


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In Reply to: Re: Arthritis Symptoms? posted by Kristie on December 11, 1999 at 15:04:58:
: : : I have arthritis bad in both my hips (am only 46 yrs old) due to the fact that I have shallow hip sockets and my hips have worn out. I am looking at hip replacement--one is 100% gone, the other not so bad yet. I got a cortisone shot (under x-ray) in the bad hip joint 6 weeks ago, and it is still helping incredibly.: : : I want to know if anyone else has some of the symptoms I have, which I attributed to the hip problems. But I just went to my ortho surgeon and he said they are not caused by my hip.: : : What I have is a tingly, numb-like, sometimes almost burning feeling that I though was arthritis. It is accompanied by frequent muscle crampling. Before my cortisone shot, I had this in my hips and legs and sometimes my back. Now, it is pretty well gone from the legs and hips, but since the shot I had a backache for awhile and the tingly feeling in my lower back. Now the tingliness has moved up my back and ususally, towards the end of the day, it feels like it is coming over the top of my head and over my face and cheeks. Muscles in my back can cramp up easily if I just reach around or try to twist/stretch my back (like if I reach back in the car to get something, my back cramps up) . : : : My doctor said I could have a problem in my back. I want to think this is all due to the hip problem, because otherwise I am a very healthy person. I do still take NSAIDS (Naproxen), but I don't think that is the cause. : : : I have also lost alot of weight (about 20 lb in the last year), and I was not heavy. I am now slim (whereas I was strong and not fat--I think I've mostly lost muscle due to inactivity and loss of desire to eat because for so long I experimetned with different NSAIDs that caused me constant stomach/intestine distress) and, my doctor said, my height to weight ratio is about perfect.: : : If anyone has any ideas what this strange feeling is or has had similar symptoms, please let me know. : : : Thanks, Kristie: : Hi Kristie, I have had arthritis for about 16-17 years and have already had foot surgery. Sometime down the road I'll have to have both hands redone. As it sits right now, I can still work ok. I can only offer possibilities. Have you had your back x-rayed yet from your tail bone all the way up to your neck? I wonder if you could have a pinched nerve? Maybe since your hip socket is so bad and there is little or no cushion there, one of your legs may actually appear shorter than the other since it is riding higher up into your socket thus you may even be leaning a little as you stand or walk. Leaning a little can cause your back muscles to stretch or bend in a different way than is normal. So your spine is affected and the longer you are on your feet and moving around could be causing your muscles to become more tense and tighten up eventually affecting your whole back and even your neck region. Don't quote me on any of this, but it can be logical at least to a certain degree. It seems to explain as to why your back cramps up when you sharply turn around to get something from the back seat of your car. Probably since your muscles are somewhat out of whack to begin with.: : I hope nothing is wrong with your spine and I would have a professional check out your situation, not just your hip socket. Let me know what happens Kristie. Take care and DON'T over do it. Kevin B.
: Thanks, Kevin. I will make an appointment to see my regular doc and see where/what this potential new problem is. I feel pretty sure it must be that I've done something to my back because I have walked with a very pronounced limp for the last year. After I got the cortisone injection, I still had a bad lower back ache, which was also aggravated by menstrual cramps so I did not really know what to attribute it to. This makes me want to even more just get the hip replacements so that the rest of my body can get back to normal, but I know I have at least a few more years of dealing with this before I can be healed. It scares me that my body is compensating for the hip problem by creating more problems.: Can you tell me, is this weird tingly, burning sensation a typical arthritis symptom? I forgot to take my Naproxen last night, and it is much worse this morning than it normally is--especially in the lower and mid back. So, I think the NSAIDs help control it.: Is your arthritis OA or RA? What age are you? I'm sorry you have had to make this ordeal part of your life for so long. I feel like a novice, with just over a year now of real pain and discomfort. But I have adjusted enough that I no longer feel depressed and angry about it and can function fairly well--at least with the cortisone I can. There were some things (like sex) that were getting nearly impossible and no longer are. However, I sure would love to be able to run and play and do sports again. My body feels like soft mush--like all my muscles have dissolved, although I am still relatively strong and healthy.: Anyway, now I'm rambling. Thanks again for your concern. : Kristie
Hi kristie,My heart goes out to you, at this time. You are so young to be having the symptoms you are having.I have the tingling and pain similar to yours, and I am 73 years of age:of course I,ve been having it for some time, I had two disks removedin 1994; the doctors call it spinal stenosis;yours may be different.At this time, I am taking celebrex, because I have stomach problems that other medications ar not tolerated.Before it came I was having a lot of problems with arthritis medications.I pray you can find relief from some sourceGod bless youLoraine
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Liberty News
Liberty CFO honored by his alma mater
|Don Moon (right), Liberty University's chief financial officer and vice president for investment management, accepts the award with Barry Moore, Liberty's vice president for outreach and strategic partnerships.
The Danville Community College Alumni Association honored Don C. Moon, Liberty University's chief financial officer and vice president for investment management, as one of its 2014 Outstanding DCC Alumni on April 11. The 15 honorees represented Danville Community College in Danville, Va., and its two predecessor institutions, Danville Technical Institute and Virginia Polytechnic Institute (Danville).
Moon graduated from DCC in 1975 and transferred to Virginia Tech, where he graduated in 1977 with a B.S. in finance.
He has worked at Liberty and its related ministries for more than 30 years, beginning as a financial manager for the "Old Time Gospel Hour" television program under Liberty's founder, Dr. Jerry Falwell. Several years later, Moon took on the school's growing financial management challenges, including overseeing the planning, implementation, management, and control of all financial-related activities for the university. He manages up to 10 direct reports and 200 employees in 10 finance- and investment-related departments.
In 2011, Moon was honored as one of the top financial officers for large nonprofit organizations in Virginia.
Moon is also serving his fourth term as an elected member on the Pittsylvania County School Board.


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I'm reading a book that has a section about processes. In it is this sentence:
The next method to enumerate processes is the
GetProcessByIdmethod. This method does exactly what its name implies: it will take a process ID and return information about it if it's found.
This doesn't sound right to me. Google dictionary has this as a definition
Enumerate - Mention (a number of things) one by one - for ex. "there is not space to enumerate all his works".
I think of enumerate as exactly that, "mentioning", but in the context of walking through a list. If you are getting something by ID, that doesn't sound like "enumerating", that sounds like "retrieving".
Am I wrong? Should I broaden my sense of "enumerate"?


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From The Editor
By Carol Berkin
Americans love their sports. Many of us watch the year flow by, not from month to month, but from baseball to soccer to basketball to football season—until spring training rolls around and the cycle begins once more. Over our morning coffee we have been known to glance at the front page of the newspaper and then turn with relish to the sports page. Our daughters and sons decorate their rooms with team banners and grown men and women show up at football games with cheese wedges on their heads or the name of their favorite team painted on their faces. Analogies to sports abound in our political and business conversations, as we declare someone has "struck out" with a bad idea or "fumbled the ball" with a clumsy response to a challenge. And, young or old, we seem always ready to recount our most memorable sports experiences, as players or fans.
As fans, athletes, or teachers, we know that sports instructs us in valuable lessons in team work, self discipline, and the ability to accept both victory and defeat with good grace. But how many of us realize the value of sports history in our classrooms? In this issue of History Now scholars, writers, and teachers remind us that sports history is a window onto many of the key issues and events in modern American history.
Mark Naison begins the issue with an overview of "Why Sports History is American History," reminding us that racial and gender struggles played themselves out on the baseball field, track, and in the boxing ring. New York Times columnist Gail Collins follows with "The Battle of the Sexes," an account of the famous tennis match between Billie Jean King and Bobby Riggs that put an end to the myth that women could not compete successfully against men on the tennis court. In "Before Jackie: How Strikeout King Satchel Paige Struck Down Jim Crow," Larry Tye traces the battle against racial discrimination in baseball through the career of one of the most talented players of the twentieth century. African Americans and women were not the only groups to face discrimination; in her account of the Olympian Jim Thorpe, Kate Buford shows us the tragic consequences of prejudice against Native American athletes. In his essay "The Importance of Muhammad Ali," Thomas Hauser focuses on the evolution of the boxer's political and social consciousness and the impact of his refusal to serve in the armed forces. Next, Barbara Winslow analyzes the far-reaching changes in sports generated by landmark legislation in her essay "The Impact of Title IX." And finally, in "The History of Women's Baseball," Kerry Candaele uses his own mother's professional baseball career as a jumping-off point to remind us of the wartime years when women had "a league of their own."
In their joint essay, "Sports: Illustrating U.S. History in the Classroom," our two master teachers, Bruce Lesh and Philip Nicolosi, offer readers creative and useful suggestions for effectively turning the major themes of this issue into classroom lessons. Our archivist Mary-Jo Kline provides readers with a wide variety of additional sources on American sports, while our contributing AP, high school, middle school and elementary school teachers have produced excellent lesson plans on the topic. Finally, our interactive feature, "Important Moments in Sports: Video Clips" brings Muhammad Ali's refusal to be drafted and the prelude to the King/Riggs match to life, courtesy of NBC News.
Spring training has begun—only a few weeks until we hear the magic words "play ball." We hope this issue of History Now will help teachers everywhere use sports to illuminate critical moments in modern American history, no matter what team you root for in the coming season.
Carol Berkin
Editor, History Now<
Carol Berkin is Presidential Professor of History at Baruch College and The Graduate Center, City University of New York. She is the author of several books including First Generations: Women in Colonial America, A Brilliant Solution: Inventing the American Constitution, Revolutionary Mothers: Women in the Struggle for America's Independence, and Civil War Wives: The Life and Times of Angelina Grimke Weld, Varina Howell Davis, and Julia Dent Grant.


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Photo: AP
Mahmoud Ahmadinejad is in New York City for the United Nations General Assembly, and according to Iranian state media organisation, Fars News Agency, he'll be meeting with Occupy Wall Street protesters during his stay (h/t mediaite).Here's how Fars put it:
Ahmadinejad is… set to meet American university students, artists, intellectuals and elites, including Occupy Wall Street anti-capitalist protestors, despite the ongoing efforts made by the pro-Zionist lobbies to prevent direct link between American people and the Iranian president.
Ahmadinejad is known for causing a stir whenever he comes to the city, making incendiary statements about everything from the Holocaust to the evils of American society.
Four years ago he delivered a speech at Columbia University where he said there were no homosexuals in Iran. Columbia President Lee C. Bollinger introduced the head of saying: "Let's, then, be clear at the beginning, Mr. President you exhibit all the signs of a petty and cruel dictator."
We haven't seen anything about this on Occupy's website or Facebook page, so we're not sure how seriously to take this news. That said, it's interesting that the Iranian state mouth-piece wants to connect Ahmadinejad with the movement all the same.
UPDATE: Daily Intel got in touch with Occupy Wall Street Spokesperson Dana Balicki who said that Occupy Wall Street has no plans to meet with Ahmadinejad.
"I've been trying to reach someone from Fars to ask them who they are talking about. Anyone going is doing so as an individual," Balicki said. "There is no organised effort on behalf of OWS to meet with Ahmadinejad. This smells like a propaganda effort to me." But getting a correction isn't easy: "In fact, when you e-mail them, it bounces and there are no phone numbers listed. Amazing news agency."
Anyone surprised?
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AmyLou necesitaba un nuevo diseño de logotipo y lanzó un concurso de diseño en 99designs.
Un ganador ha sido elegido entre 139 diseños de 42 diseñadores freelance.
Whitaker Painting, LLC
I have been in business for 10 years and desperately need a logo. We are a very high end company. We paint interiors and exteriors. We like to think of ourselves as the Nordstrom of painting. We are craftsman in the trade, and take our painting projects seriously. We also offer professional color consulting services.
High end homeowners. Million dollar plus homes. 40+ years old, typically couples. They work at Microsoft, are Boeing executives, people who forgot they bought a new car last week (literally happened to me a few days ago).
Classy font. (LLC should be small, possibly vertical) No paint cans, brushes, ladders, paint drips, splatters, or quirky clipart please. Graphics to include a home. Colors should be black, white, grays, or neutral, maybe a gold accent. Whitaker and Painting need to stand apart from each other so it's easier to read. (they're both really long words so when they're seen in the same font in a long line, it's not easily readable vs. a "stacked" look or different fonts to separate. The designs we like the best are symmetrical.
Descubre cómo funciona un concurso de diseño u obtén más información sobre nuestros servicios de diseño de logo.


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Kobe Bryant was questionable for last night's game against the Pacers due to flu-like symptoms, and clearly appeared to be suffering several times. It didn't keep him from putting up 40 in a losing effort against Indiana, in a game that seemed to be missing the rest of the Lakers entirely (they risked franchise records both in lowest-scoring and lowest shooting percentage) and featured show-boating referees. Sorry, Kobe, they only make Gatorade commercials when you win.
Got any stories or photos for us? Tip your editors.


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Monday, February 04, 2013
Sports News
Heat win to put Spoelstra on East All-Star's bench
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[February 04, 2013]
(AP) -- Both on the court and along the sidelines, the Miami Heat will be well represented at this month's NBA All-Star game.
LeBron James scored 30 points, Chris Bosh had 28 against his former team and the Miami Heat beat Toronto 100-85 Sunday for their 10th straight victory over the Raptors.
The victory guarantees Miami the top spot in the Eastern Conference as of Feb. 3, percentage points ahead of the New York Knicks. That means Heat coach Erik Spoelstra and his staff will be in charge when the All-Stars suit up in Houston in two weeks.
"It's awesome," James said. "It's a testament to our coaches' preparedness. As players we go out there and just try to execute their game plan. We're happy for them."
Spoelstra, the fifth-year coach who led the Heat to the NBA championship last June, called the All-Star nod "a heck of an honor."
"We're happy to go, we're excited about it," Spoelstra said. "You don't spend any time thinking about it as it's happening, you're just trying to prepare your team and make sure you have the best record. Now that it happened, it's cool. We'll make the most of it. Our staff is thrilled about it. Our players gave us congratulations afterward. That's about as rare a thing as you'll ever get in this league."
Dwyane Wade added 23 points as the Heat bounced back after Friday's 102-89 loss at Indiana, ensuring their team Super Bowl viewing party would be a happy one.
"It's good to get this one to close out this road trip so we can enjoy this evening and enjoy the second game," Spoelstra said.
James had indicated his displeasure last Wednesday after realizing he would miss the Super Bowl while flying back to Florida after the game. The Heat's private plane is not equipped with satellite TV or wireless Internet.
To accommodate James, the Heat delayed their return home for Monday's game against Charlotte until after the Super Bowl, which they planned to watch at a bar adjoining the Air Canada Centre. Real Sports Bar and Grill is operated by Raptors team owner Maple Leaf Sports and Entertainment.
"You don't want to be thinking about ways you could have helped the team win while you're trying to watch the Super Bowl," James said. "We're happy we took care of it."
James and Wade were voted onto the starting lineup for the East, while Bosh was named a reserve last week.
With injured Boston guard Rajon Rondo set to miss out after tearing a knee ligament, Spoelstra was asked whether he'd consider putting Bosh into the starting lineup.
"I've only given this thought for the last nine minutes," Spoelstra said. "But I will tell you this unequivocally: My loyalties are first and foremost always with the Miami Heat."
Whether he starts or not, Bosh said he was happy to see Spoelstra and his staff earn the trip to Houston.
"They deserve it," Bosh said. "They work hard, just like us. To be able to have that honor and that privilege, it's a great thing."
[to top of second column]
Rudy Gay led the Raptors with 29 points and DeMar DeRozan had 27. Kyle Lowry scored 10 points and Aaron Gray matched his season high with 12 rebounds for the Raptors, who have lost four of five.
"They're the champs," DeRozan said. "We couldn't stop them. We couldn't slow them down when they got it going."
James finished with team highs of eight rebounds and seven assists and Bosh scored 13 in the fourth quarter for Miami, which improved to 12-11 on the road.
Booed throughout by the sellout Toronto crowd, Bosh said he used the jeers as motivation.
"I was hearing a lot from the fans," Bosh said. "I thank them for continuing to stay on me and call me names and stuff because that helped my focus a lot. I was like
'I need to get in this to shut them up.'"
Terrence Ross and DeRozan sandwiched 3-pointers around a hook shot by Bosh, bringing Toronto to within three at 84-81 with 5:22 left. Miami's Norris Cole scored a layup but DeRozan answered with a driving dunk, and Miami called timeout up 86-83 with 4:09 left.
Bosh made one of two from the line after the stoppage, then followed with a 3 from the corner as the Heat took a 90-83 lead with 2:34 to go.
"Chris really gave us a presence in the paint," Spoelstra said. "He hit the big 3 but he was collapsing the defense with his rolls to the rim, catching it and going up strong in traffic. That makes us a different team when he gives us that presence."
James extended the lead by stealing the ball on Toronto's next possession and driving for a breakaway slam, then hitting a 3 to put the Heat up 95-83 with 1:28 left.
James scored nine points in the first, including an alley-oop dunk from Mario Chalmers and a breakaway slam off a Raptors turnover. DeRozan had 10 for Toronto but the Raptors trailed 24-21 after one quarter, the first time in nine games they hadn't led after 12 minutes.
Gay scored Toronto's first 10 points of the second to give the Raptors the lead. He finished with 16 in the quarter, putting Toronto up 50-44 at the half. Bosh scored nine points in the third and Wade added seven as the Heat took advantage of six Toronto turnovers to take a 71-67 lead into the fourth.
NOTES: Gay made his first Raptors start in place of Landry Fields (back spasms). ... Miami is 24-2 when scoring 100 points or more. ... This was the last of a four-game road trip for the Heat. Monday's game against the Bobcats is the first of five straight at home. They'll also face Houston, the Clippers, the Lakers and Portland. ... James and several Heat teammates attended Saturday night's NHL game between the Maple Leafs and Boston Bruins. James tweeted a photo from his seat accompanied by the caption "At my first hockey game. Pretty Damn cool."
Press; By IAN HARRISON]
Copyright 2013 The Associated
Press. All rights reserved. This material may not be published,
broadcast, rewritten or redistributed.
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The best investment you can make in your home – both for your pocketbook and your personal enjoyment – is in landscaping. Real estate experts agree that every dollar you put into your yard eventually will return one to two dollars when you sell. And at a time when home values are sagging, that's something to consider.
"With an increasing number of homeowners staying put rather than selling and moving up, we're seeing a growing trend of people adapting their homes to their lifestyle," says Dave Johnson, manager of Patio Town, a leading Minnesota landscape supplies retailer. "That often means investing in landscaping to make better use of what they already own."
Versatile segmental retaining wall systems enable today's homeowner to bring plenty of curb appeal to their landscape while maximizing the use of their outdoor environment. Construction of everything from raised-bed gardens and tiered walls to stairs, columns and freestanding seatwalls is possible with systems such as Versa-Lok.
What's nice about these systems is that they appeal to both the do-it-yourselfer and the professional landscaper, says Johnson.
"You can add some amazing features to your yard with a modest amount of skill and labor," Johnson explains. "Fire pits with paver patios, retaining walls, columns, sidewalks and driveways all are within the capabilities of most do-it-yourselfers. Or, you can bring your ideas to a professional landscaper and leave the design and installation to them, no matter how complex."
In recent years, segmental retaining walls have become one of the most important tools in the hands of landscapers.
"Your imagination is your only limitation when working with these systems," says Kelly O'Donahue, landscape designer with Villa Landscapes in Oakdale, Minn. "I've transformed virtually unusable back yards into stunning outdoor environments using Versa-Lok. Homeowners love it because it's readily available in consistent colors and styles that are easily matched with existing exteriors or other landscape elements."
It is a favorite among hardscape installers as well. "The Versa-Lok system is especially design-friendly because it uses solid units that can be easily cut or split to fit any application," explains Austin Keay, owner of AKeay Landscaping in North Branch, Minn. "They're like the building blocks of landscaping. You can do almost anything with them, whether you're installing an outdoor kitchen from scratch or putting a new face on an older home with a front-entry makeover."
Segmental retaining wall systems lend themselves to phased landscaping projects built over a period of years, says O'Donahue. That's a trend landscape designers are seeing more of as homeowners have become more cost-conscious.
"Some of our customers have a grand idea of what they ultimately want, but may not be able to afford all at once. So we work up a master plan for them, they start out with one phase, perhaps a circular paver patio with seatwalls and a firepit in the back yard. And that evolves over a period of years to a full-blown entertainment area, maybe an outdoor kitchen or bar, along with other features such as gardens or gazebos."
Sustainable, or "green," landscaping, also rapidly gaining in popularity, can add value to your home in other ways.
Low-rise retaining walls can be used in such features as rain gardens to significantly reduce storm water and pollutant runoff from surface areas. Plantable retaining walls, like Versa-Lok's Versa-Green system, provide earth stabilization while softening the appearance of a traditional retaining wall with vegetation. For information on Versa-Lok Retaining Wall Systems, visit versa-lok.comor call (800) 770-4525.
"Plantable wall systems essentially create a vertical garden while performing the function of a retaining wall," says O'Donahue. "You can get really creative with the color and textures of plantings you use in the wall to achieve whatever look you want."


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RIDER ALERT Limited, Demand-Response Bus Service Available On Labor Day RIDER ALERT Route 76: Select Bus Stops Will Not Be Served Due to Road Repairs RIDER ALERT Route 61: Bus Stop at Harding/Monroe Will Not Be Served Due to Construction. RIDER ALERT Route 310, 340, 360, 380, and 390 will be suspended due to summer vacation starting June 5th to August 7th.
|Home > Maps & Schedules > Hopper > Metro Hopper Route 7


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Red Tails
In the times before Martin Luther King put momentum behind his dream, African Americans had to be twice as good to get half as much as their white counterparts. Racism was justified and equality denied by spurious science.
Black soldiers were thought to be lazy, stupid and cowardly, so the government didn't want to give them the opportunity to fight for their country — just cook for it in galleys and mess halls while white soldiers earned accolades.
A few brave men challenged those rules. As part of an experiment, the U.S. government tested black pilots trained at Booker T. Washington's Tuskegee Institute. Known as the Tuskegee Airmen, these aviators, navigators, mechanics and bombardiers maintained one of the best records of any squadron during the war. Their record is more impressive because these aviators were flying second-hand planes with shoddy equipment.
But for years these men who had to fight both the Axis powers and their government's prejudices were a footnote in history.
Red Tails seeks to right this historic wrong. So it's a shame that writers John Ridley (Da Brick) and Aaron McGruder (The Boondocks) have created a flat, hollow story.
The film follows a fictionalized squadron of war-movie stereotypes. Marty 'Easy' Julian (Nate Parker: Blood Done Sign My Name) is a tortured captain who needs a few belts of booze to steady himself for battle. Joe 'Lightening' Little (David Oyelowo: The Help) is Marty's hot-shot best friend, who seeks glory instead of obeying orders. Ray 'Junior' Gannon (Tristan Wilds: 90210) is the young soldier yearning to be seen as an adult by his peers. Andrew 'Smokey' Salem (Ne-Yo: Battle: Los Angeles) is a wise-crackin, guitar-strumming southerner. And Antoine 'Coffee' Coleman (Andre Royo: Fringe) is the exasperated mechanic who patches together their beat-up planes.
These types, for they're not really characters, are led by Colonel A.J. Bullard (Terrence Howard: Winnie), who must fight racists in the military to get his men taken seriously.
A second problem beyond stereotypes is that Red Tails tries so hard to show how righteous and good these men were that it forgets to make them people. Parker is so inexpressive that you wonder just how much Julian must be drinking to be that flat. Ne-Yo and Royo are both excellent comedians, but we don't know anything about the men they portray.
Only Little, who boils with anger at injustice of his situation, feels like a real, relatable character. He romances an Italian girl, throws punches at racists and speaks up when he sees Julian drinking. Oyelowo makes Little a real, flawed man. That makes him no less of a hero.
Why aren't the other characters given depth? The Tuskegee Airmen were some of the best-trained and most educated pilots in the war, because they had to be. To not impress that upon the audience takes away from the real heroes. Poor Cuba Gooding Jr., who has won an Oscar, can only chomp on a pipe in the background.
The biggest problem, however, is the dialog, which ranges from boring to ridiculous. When the white bombardiers realize the Red Tails are providing them excellent defense, they exclaim, "Those Red Tails sure can fly!"
The film is not a total wash. Red Tails is most interesting when its protagonists are in the air. Computer-generated battle sequences are tight and tense as these pilots battle a scarred German fighter pilot and his intimidating squadron.
At the end of the day, you don't root for the Red Tails as people. You root for them because they're the good guys. Because they're fighting the Nazis. Because they're oppressed. That's a shame, because each one of the actual Tuskegee Airmen was a real and extraordinary man.


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Scituate is a town in Providence County, Rhode Island with approximately 10,300 residents. The town has been incorporated since 1731 and is known for having the largest body of freshwater in the state: the Scituate Reservoir. The treatment plant for the reservoir is also the largest in New England.
The Scituate Art Festival is a popular tradition in the town that takes place every year over Columbus Day weekend. The event began in 1967 and has been voted the "Best Annual Festival" by Rhode Island Monthly. The festival attracts hundreds of exhibitors and the proceeds go to a variety of good causes in town and also helps maintain the historical Congregational Church.
Scituate is a rural town and many homes have large, spacious yards. There is a variety of homes for sale around town from classic to contemporary with houses dating back to the early 19th century. Popular real estate styles include Colonial and Ranch but antique styles are seen throughout the town such as Victorian.
Scituate, once considered the "hinterlands" of the state, today offers both suburban amenities and a rural atmosphere of unspoiled forests, ponds, and the pristine Scituate Reservoir. The reservoir is one of the most prominent features of the town and is the largest freshwater body of water in the state, providing over 60% of the state's drinking water.
Today, Scituate is best known for its apple orchards, old, hearty farms, and the pure poetry of its stone walls stretching through the beautiful woodlands. When the leaves start to turn, that means it's time for the Scituate Art Festival, an event where the village green becomes an outdoor gallery of art, crafted works, sculpture, and antiques. It's an easy jaunt by car to Providence, and the city's low taxes and good schools make it an eminently well-rounded living environment.
Scituate is made up of the villages of: Chopmist, Clayville, Fiskeville, Glenn Rock, Hope, Jackson, North Scituate, Ponagansett, Rockland, Saundersville.
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