Description Format Source References

Data set on 1,965 Ohio elementary school buildings for 2001-2002.

The ‘ohio’ dataset contains 6 objects as follows.

- ohioSchools
Original data

`ohioschool.dat`

from http://www.spatial-econometrics.com/ (J. LeSage and R. Pace 2009). The data set contains information on, for instance, school building ID, Zip code of the location of the school, proportion of passing on five subjects, number of teacher, number of student, etc. The variables are:

col 1: zip code

col 2: lattitude (zip centroid)

col 3: longitude (zip centroid)

col 4: buidling irn

col 5: district irn

col 6: # of teachers (FTE 2001-02)

col 7: teacher attendance rate

col 8: avg years of teaching experience

col 9: avg teacher salary

col 10: Per Pupil Spending on Instruction

col 11: Per Pupil Spending on Building Operations

col 12: Per Pupil Spending on Administration

col 13: Per Pupil Spending on Pupil Support

col 14: Per Pupil Spending on Staff Support

col 15: Total Expenditures Per Pupil

col 16: Per Pupil Spending on Instruction % of Total Spending Per Pupil

col 17: Per Pupil Spending on Building Operations % of Total Spending Per Pupil

col 18: Per Pupil Spending on Administration % of Total Spending Per Pupil

col 19: Per Pupil Spending on Pupil Support % of Total Spending Per Pupil

col 20: Per Pupil Spending on Staff Support % of Total Spending Per Pupil

col 21: irn number

col 22: avg of all 4th grade proficiency scores

col 23: median of 4th grade prof scores

col 24: building enrollment

col 25: short-term students < 6 months

col 26: 4th Grade (or 9th grade) Citizenship % Passed 2001-2002

col 27: 4th Grade (or 9th grade) math % Passed 2001-2002

col 28: 4th Grade (or 9th grade) reading % Passed 2001-2002

col 29: 4th Grade (or 9th grade) writing % Passed 2001-2002

col 30: 4th Grade (or 9th grade) science % Passed 2001-2002

col 31: pincome per capita income in the zip code area

col 32: nonwhite percent of population that is non-white

col 33: poverty percent of population in poverty

col 34: samehouse % percent of population living in same house 5 years ago

col 35: public % of population attending public schools

col 36: highschool graduates, educ attainment for 25 years plus

col 37: associate degrees, educ attainment for 25 years plus

col 38: college, educ attainment for 25 years plus

col 39: graduate, educ attainment for 25 years plus

col 40: professional, educ attainment for 25 years plus- ohioGrades
The derived dataset for analyzing the percentage passed based on Zip codes. The variables are:

y: the percentage passed (4th or 9th grade) in each school

TchExp: average Teacher's experience

Subjects: for five study subjects of Citizenship, Maths, Reading, Writing and Science

Stu.Tch: student by teacher ratio

School: school index

Zip: Zip code- ohioMedian
The derived dataset for analyzing the median of 4th grade scores based on school districts. The variables are:

MedianScore: the median of 4th grade prof scores

district: school districts- ohioShape
A

`SpatialPolygonsDataFrame`

object (see package**sp**) containing the map information of ohio school districts.- ohioZipDistMat
The spatial distance matrix based on Zip codes. The codes generated this matrix are:

`Zsp <- model.matrix(~ factor(Zip) - 1, data = ohioGrades)`

`uzipC <- matrix(0, nrow = ncol(Zsp), ncol = 2)`

`Zip <- as.numeric(substr(colnames(Zsp), start = 12, stop = 16))`

`for (i in 1: ncol(Zsp)) {`

`Cord <- as.matrix(ohioSchools[(ohioSchools$V1 == Zip[i]), 2:3])`

`uzipC[i,] <- Cord[1,]`

`}`

`Dst <- as.matrix(dist(uzipC))`

`for(i in 1:nrow(Dst)) {`

`x <- Dst[i,]`

`x <- ifelse(x == 0, 0, 1/x)`

`Dst[i,] <- ifelse(x > 4, 4, x)`

`}`

`ohioZipDistMat <- Dst/4`

- ohioDistrictDistMat
The spatial distance matrix based on school districts. The codes generated this matrix are:

`ccNb <- poly2nb(ccShape)`

`W <- matrix(0, 616, 616)`

`for (i in 1:nrow(W)) {`

`tmp <- as.numeric(ccNb[[i]])`

`for (k in tmp) W[i,k] <- 1`

`}`

`W[353,] <- W[,353] <- 0`

`districtShape <- as.numeric(substr(as.character(ohioShape@data$UNSDIDFP), 3, 7))`

`dimnames(W) <- list(districtShape, districtShape)`

`districtSchool <- floor(ohioSchools[,5]/10)`

`districtSchool <- factor(districtSchool[districtSchool %in% districtShape])`

`levelsShape <- levels(factor(districtShape))`

`levelsSchool <- levels(districtSchool)`

`levels(districtSchool) <- c(levelsSchool, levelsShape[!(levelsShape %in% levelsSchool)])`

`ohioDistrictDistMat <- W[levels(districtSchool),levels(districtSchool)]`

J. LeSage and R. Pace (2009). *Introduction to Spatial Econometrics*. Chapman \& Hall/CRC, Boca Raton.

J. LeSage and R. Pace (2009). *Introduction to Spatial Econometrics*. Chapman \& Hall/CRC, Boca Raton.

M. Alam, L. Ronnegard, X. Shen (2014). **Fitting spatial models in hglm**. *Submitted*.

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