R Notebook
Mapping the percent Religious in America
Loading packages
library(tidyverse)
library(sf)
library(tmap)
library(tigris)
library(spdep)
library(data.world)
library(readr)
library(dplyr)
library(texreg)
library(ggplot2)
library(tmap)
library(tmaptools)
library(tidyr)
library(Zelig)
library(visreg)Loading data
Amer.nation <- read_csv("/Users/Deepakie/Documents/Queens College/SOC712/Data/American Nations.csv")
head(Amer.nation)religiondata$Geo_FIPS <- as.integer(religiondata$Geo_FIPS)
ct_map$GEOID <- as.integer(ct_map$GEOID)Non-Spatial Regression
Non-spatial data gives a statistical correlation that is seperate of all spatial examinations. We will use a Ecological regressions and ggplots in this section.The ecological regression produced for this analysis is the relationship between total population and % population religious. The ecological regression, shown below give the mean of all religious population and total population of each state.
religiondata_er<- religiondata %>%
group_by(StateFP) %>%
summarise(mean_p = mean(Religiousperc, na.rm = TRUE), mean_s = mean(Totalpop, na.rm = TRUE))
eco <- lm(mean_p ~ mean_s, data = religiondata_er)
summary(eco)
Call:
lm(formula = mean_p ~ mean_s, data = religiondata_er)
Residuals:
Min 1Q Median 3Q Max
-24.4110 -7.6785 0.6961 7.7478 27.2981
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.073e+01 2.199e+00 23.074 <2e-16 ***
mean_s -1.470e-05 9.640e-06 -1.525 0.133
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 11.65 on 50 degrees of freedom
Multiple R-squared: 0.04446, Adjusted R-squared: 0.02535
F-statistic: 2.327 on 1 and 50 DF, p-value: 0.1335GGPLOT
ggplot(data = religiondata, aes(x = StateFP, y = Religiousperc)) +
geom_point() +
coord_capped_cart(bottom='both', left='none') +
theme_light() + theme(panel.border=element_blank(), axis.line = element_line())
The plot shows the percent of total religious population per each states. The plot is hard to read and confusing to understand so we will now look at this analysis by a Spatial mapping.
Spatial Mapping
Merging data
religiondata <- religiondata %>%
mutate(fips = as.integer(Geo_FIPS))
ct_map <- ct_map %>%
mutate(fips = parse_integer(GEOID))
combined_data <- ct_map %>%
left_join(religiondata, by = "fips")Mapping
library(tmap)
tm_shape(combined_data) + tm_polygons("Religiousperc")Excluding Alaska and Hawaii
combined_data_sub <- combined_data %>%
filter(STATEFP != "02") %>%
filter(STATEFP != "15") %>%
filter(STATEFP != "60") %>%
filter(STATEFP != "66") %>%
filter(STATEFP != "69") %>%
filter(STATEFP != "72") %>%
filter(STATEFP != "78")tm_shape(combined_data_sub, projection = 2163) + tm_polygons("Religiousperc")Mean of Religious Population
combined_data_sub <- combined_data_sub %>%
mutate(redblue = Religiousperc - 51.41)The mean of “religiousperc” in this dataset help us differentiate the states percentage of religious population to observe differences between states in a clearer way. The mean also helps avoide extrapolation, in this case the mean is 51.41.
tm_shape(combined_data_sub, projection = 2163) + tm_polygons("redblue", palette = "-RdBu")Highlighting State lines
us_states <- combined_data_sub %>%
aggregate_map(by = "STATEFP")tm_shape(combined_data_sub, projection = 2163) + tm_polygons("redblue", palette = "-RdBu") +
tm_shape(us_states) + tm_borders(lwd = .36, col = "black", alpha = 1)
tm_shape(combined_data_sub, projection = 2163) + tm_polygons("redblue", palette = "-RdBu", border.col = "grey", border.alpha = .4) +
tm_shape(us_states) + tm_borders(lwd = .36, col = "black", alpha = 1)
This gives a spatial visualization by highlighting the state lines to understand the total percent regligious in the country among different states and counties. From the map above, the most religious areas are in scattered across the nation but we can identify large religious total percentages in the midnorth(North Dakota,South Dakota,Minnesota), midsouth(Texas, Louisiana,Oklahoma) and a little bit in midwest(mostly being in Utah). The map identifies by total religious percentage with ranges from -50 to 0, indiciating no relgiious populations, 0- 50 indicating religious population being between 0 -50%, 50 to 100% giving us higher total religious percentages and so on.
The American Nations
The American Nation: A History of the United States, written by Mark Carnes and John Garraty. The authors represent the political history of the United States tied with its social, economic and cultural development. Using the mapping file of the American Nations, this spatial analysis, shows county level percents of religious population among the 11 nations within America.
Amer.nation <- combined_data_sub %>%
aggregate_map(by = "STATEFP")tm_shape(combined_data_sub, projection = 2163) + tm_polygons("redblue", palette = "-RdBu") +
tm_shape(Amer.nation) + tm_borders(lwd = .36, col = "black", alpha = 1)
Making the boudary lines clearer
tm_shape(combined_data_sub, projection = 2163) + tm_polygons("redblue", palette = "-RdBu", border.col = "grey", border.alpha = .4) +
tm_shape(Amer.nation) + tm_borders(lwd = .50, col = "black", alpha = 1)
Tigris
The Tigris package makes creating spatial mapping easier and smoother to apply for the analysis as many functions have a parameter,CB. On slide 32 of Professor’s slides, we coded counties(CB = TRUE).
If CF is set to TRUE, it will direct tigris to load a cartographic boundry file. Cartographic boundry files default to a simplified resoultion of 1:500k. In some cases such as states, resolutions of 1.5 million and 1:20 million are available. For example, R specify the following modifications to the states() function and retrieves a simplified dataset. In other words when CB is set to FALSE, we get a detailed TIGER/line file. When it is set to TRUE, the file is generalized to (1:500K).
options(tigris_class = "sf")
t_county <- counties(cb = TRUE)
#names(t_county)t_combined_data <- t_county %>%
mutate(fips_code = parse_integer(GEOID))t_combined_data_sub <- t_combined_data %>%
filter(STATEFP != "02") %>%
filter(STATEFP != "15") %>%
filter(STATEFP != "60") %>%
filter(STATEFP != "66") %>%
filter(STATEFP != "69") %>%
filter(STATEFP != "72") %>%
filter(STATEFP != "78")us_states <- t_combined_data_sub %>%
aggregate_map(by = "STATEFP")
Amer.nation <- t_combined_data_sub %>%
aggregate_map(by = "STATEFP")
t_combined_data_sub <- combined_data_sub %>%
mutate(redblue = Religiousperc - mean(religiondata$Religiousperc))tm_shape(combined_data_sub, projection = 2163) + tm_polygons("redblue", palette = "-RdBu", border.col = "grey", border.alpha = .4) +
tm_shape(Amer.nation) + tm_borders(lwd = .50, col = "black", alpha = 1)
options(tigris_class = "sf")
t_county <- counties(cb = FALSE)
|
| | 0%
|
| | 1%
|
|= | 1%
|
|= | 2%
|
|== | 2%
|
|== | 3%
|
|=== | 3%
|
|=== | 4%
|
|==== | 4%
|
|==== | 5%
|
|===== | 5%
|
|===== | 6%
|
|====== | 6%
|
|====== | 7%
|
|======= | 7%
|
|======= | 8%
|
|======== | 8%
|
|======== | 9%
|
|========= | 9%
|
|========= | 10%
|
|========= | 11%
|
|========== | 11%
|
|========== | 12%
|
|=========== | 12%
|
|=========== | 13%
|
|============ | 13%
|
|============ | 14%
|
|============= | 14%
|
|============= | 15%
|
|============== | 15%
|
|============== | 16%
|
|=============== | 16%
|
|=============== | 17%
|
|================ | 17%
|
|================ | 18%
|
|================= | 18%
|
|================= | 19%
|
|================== | 19%
|
|================== | 20%
|
|================== | 21%
|
|=================== | 21%
|
|=================== | 22%
|
|==================== | 22%
|
|==================== | 23%
|
|===================== | 23%
|
|===================== | 24%
|
|====================== | 24%
|
|====================== | 25%
|
|======================= | 25%
|
|======================= | 26%
|
|======================== | 26%
|
|======================== | 27%
|
|========================= | 27%
|
|========================= | 28%
|
|========================== | 28%
|
|========================== | 29%
|
|=========================== | 29%
|
|=========================== | 30%
|
|=========================== | 31%
|
|============================ | 31%
|
|============================ | 32%
|
|============================= | 32%
|
|============================= | 33%
|
|============================== | 33%
|
|============================== | 34%
|
|=============================== | 34%
|
|=============================== | 35%
|
|================================ | 35%
|
|================================ | 36%
|
|================================= | 36%
|
|================================= | 37%
|
|================================== | 37%
|
|================================== | 38%
|
|=================================== | 38%
|
|=================================== | 39%
|
|==================================== | 39%
|
|==================================== | 40%
|
|==================================== | 41%
|
|===================================== | 41%
|
|===================================== | 42%
|
|====================================== | 42%
|
|====================================== | 43%
|
|======================================= | 43%
|
|======================================= | 44%
|
|======================================== | 44%
|
|======================================== | 45%
|
|========================================= | 45%
|
|========================================= | 46%
|
|========================================== | 46%
|
|========================================== | 47%
|
|=========================================== | 47%
|
|=========================================== | 48%
|
|============================================ | 48%
|
|============================================ | 49%
|
|============================================= | 49%
|
|============================================= | 50%
|
|============================================= | 51%
|
|============================================== | 51%
|
|============================================== | 52%
|
|=============================================== | 52%
|
|=============================================== | 53%
|
|================================================ | 53%
|
|================================================ | 54%
|
|================================================= | 54%
|
|================================================= | 55%
|
|================================================== | 55%
|
|================================================== | 56%
|
|=================================================== | 56%
|
|=================================================== | 57%
|
|==================================================== | 57%
|
|==================================================== | 58%
|
|===================================================== | 58%
|
|===================================================== | 59%
|
|====================================================== | 59%
|
|====================================================== | 60%
|
|====================================================== | 61%
|
|======================================================= | 61%
|
|======================================================= | 62%
|
|======================================================== | 62%
|
|======================================================== | 63%
|
|========================================================= | 63%
|
|========================================================= | 64%
|
|========================================================== | 64%
|
|========================================================== | 65%
|
|=========================================================== | 65%
|
|=========================================================== | 66%
|
|============================================================ | 66%
|
|============================================================ | 67%
|
|============================================================= | 67%
|
|============================================================= | 68%
|
|============================================================== | 68%
|
|============================================================== | 69%
|
|=============================================================== | 69%
|
|=============================================================== | 70%
|
|=============================================================== | 71%
|
|================================================================ | 71%
|
|================================================================ | 72%
|
|================================================================= | 72%
|
|================================================================= | 73%
|
|================================================================== | 73%
|
|================================================================== | 74%
|
|=================================================================== | 74%
|
|=================================================================== | 75%
|
|==================================================================== | 75%
|
|==================================================================== | 76%
|
|===================================================================== | 76%
|
|===================================================================== | 77%
|
|====================================================================== | 77%
|
|====================================================================== | 78%
|
|======================================================================= | 78%
|
|======================================================================= | 79%
|
|======================================================================== | 79%
|
|======================================================================== | 80%
|
|======================================================================== | 81%
|
|========================================================================= | 81%
|
|========================================================================= | 82%
|
|========================================================================== | 82%
|
|========================================================================== | 83%
|
|=========================================================================== | 83%
|
|=========================================================================== | 84%
|
|============================================================================ | 84%
|
|============================================================================ | 85%
|
|============================================================================= | 85%
|
|============================================================================= | 86%
|
|============================================================================== | 86%
|
|============================================================================== | 87%
|
|=============================================================================== | 87%
|
|=============================================================================== | 88%
|
|================================================================================ | 88%
|
|================================================================================ | 89%
|
|================================================================================= | 89%
|
|================================================================================= | 90%
|
|================================================================================= | 91%
|
|================================================================================== | 91%
|
|================================================================================== | 92%
|
|=================================================================================== | 92%
|
|=================================================================================== | 93%
|
|==================================================================================== | 93%
|
|==================================================================================== | 94%
|
|===================================================================================== | 94%
|
|===================================================================================== | 95%
|
|====================================================================================== | 95%
|
|====================================================================================== | 96%
|
|======================================================================================= | 96%
|
|======================================================================================= | 97%
|
|======================================================================================== | 97%
|
|======================================================================================== | 98%
|
|========================================================================================= | 98%
|
|========================================================================================= | 99%
|
|==========================================================================================| 99%
|
|==========================================================================================| 100%#names(t_county)t_combined_data <- t_county %>%
mutate(fips_code = parse_integer(GEOID))t_combined_data_sub <- t_combined_data %>%
filter(STATEFP != "02") %>%
filter(STATEFP != "15") %>%
filter(STATEFP != "60") %>%
filter(STATEFP != "66") %>%
filter(STATEFP != "69") %>%
filter(STATEFP != "72") %>%
filter(STATEFP != "78")religiondata1 <- religiondata %>%
mutate(fips= Geo_FIPS)us_states <- t_combined_data_sub %>%
aggregate_map(by = "STATEFP")
Amer.nation <- t_combined_data_sub %>%
aggregate_map(by = "STATEFP")
t_combined_data_sub <- combined_data_sub %>%
mutate(redblue = Religiousperc - mean(religiondata$Religiousperc))tm_shape(combined_data_sub, projection = 2163) + tm_polygons("redblue", palette = "-RdBu", border.col = "grey", border.alpha = .4) +
tm_shape(Amer.nation) + tm_borders(lwd = .50, col = "black", alpha = 1)
Differences between Spatial and Non-Spatial Data
Spatial data evulates the percent religious as a whole and its relationship between states but not in a precise and particular way as non-spatial. Non-spatial data is usually independent rather than multidemensional and auro correlated as spatial. Locational data as used for this analysis is assessed through spatial analysis rather than coverting location functions which have ecological fallacies due to other influenceing variables. Overall, by evaluating both spatial and non-spatial analysis for this assignment have advantages and disadvantages. Spatial data is simple but does not give a indepth analysis. When we combine both data analysis, we are able to evaluate total religious population percentage easily by location.
---
title: "R Notebook"
output: html_notebook
---
###Mapping the percent Religious in America

###Loading packages
```{r, warning=FALSE, message=FALSE}
library(tidyverse)
library(sf) 
library(tmap) 
library(tigris)  
library(spdep)
library(data.world)
library(readr)
library(dplyr)
library(texreg)
library(ggplot2)
library(tmap)
library(tmaptools)
library(tidyr)
library(Zelig)
library(visreg)
```

####### The United States have number of religious populations/groups. Overall it is found that 83% of Americans are Christian, 13% have no religion and 4% of population include Buddhists, Muslims and Jews, etc. This assignment will explore total religious groups in U.S by state and county. The data, Religion 2010, is obtained from social explorer gives total population, all religions population per county and state as well as the total percent religious in each area. It is interesting to compare the percent religious in different states as well as counties in America. 
The dataset is used to analyze the distribution of total religious populations across the country. The data is derived by counties, states which is categorixed in a numerical form later in the study. The variables used in the analysis are STATEFP which identifies each state, County which gives names of each county by states, and Religiousperc which is the percentage of population for all religion populations in the total population. In other words, it is the percentage of total religious population by location.

This analysis will visualize our data on the percent of religious population through two different methods. The first method is a non-spatial regression and ggplot which will show the relationship between states and percent religious. The second method is through a spatial visualization of percent of relgiious among all counties and states, and the 11 nations of America.

###Loading data
```{r}
religiondata <- read_csv("/Users/Deepakie/Documents/Queens College/SOC712/Data/Regliondata.csv")
#head(religiondata)
```

```{r}
religiondata <- religiondata%>%
  select(Geo_FIPS,Geo_Name, Geo_STATE,RCMS10_T001_001,RCMS10_NV009_001)%>%
  rename(County = Geo_Name,
         StateFP = Geo_STATE,
         Totalpop = RCMS10_T001_001,
         Religiousperc = RCMS10_NV009_001)
```

```{r}
head(religiondata)
```

```{r message=FALSE, warning=FALSE}
Amer.nation <- read_csv("/Users/Deepakie/Documents/Queens College/SOC712/Data/American Nations.csv")
#head(Amer.nation)
```   


```{r}
ct_map <- st_read("/Users/Deepakie/Documents/Queens College/SOC712/Data/tl_2016_us_county/tl_2016_us_county.shp", stringsAsFactors = FALSE)
```

```{r}
#names(ct_map)
```

```{r}
religiondata$Geo_FIPS <- as.integer(religiondata$Geo_FIPS)
ct_map$GEOID <- as.integer(ct_map$GEOID)
```

###Non-Spatial Regression 
Non-spatial data gives a statistical correlation that is seperate of all spatial examinations. We will use a  Ecological regressions and ggplots in this section.The ecological regression produced for this analysis is the relationship between total population and % population religious. The ecological regression, shown below give the mean of all religious population and total population of each state.  

```{r}
religiondata_er<- religiondata %>% 
  group_by(StateFP) %>% 
  summarise(mean_p = mean(Religiousperc, na.rm = TRUE), mean_s = mean(Totalpop, na.rm = TRUE))

eco <- lm(mean_p ~ mean_s, data = religiondata_er)
summary(eco)
```
#####GGPLOT
```{r}
ggplot(data = religiondata, aes(x = StateFP, y = Religiousperc)) +
    geom_point() +
  coord_capped_cart(bottom='both', left='none') +
  theme_light() + theme(panel.border=element_blank(), axis.line = element_line())
```
The plot  shows the percent of total religious population per each states. The plot is hard to read and confusing to understand so we will now look at this analysis by a Spatial mapping.  

###Spatial Mapping
###Merging data
```{r}
religiondata <- religiondata %>% 
  mutate(fips = as.integer(Geo_FIPS))
ct_map <- ct_map %>% 
  mutate(fips = parse_integer(GEOID))
combined_data <- ct_map %>% 
  left_join(religiondata, by = "fips")
```

###Mapping
```{r}
library(tmap)
tm_shape(combined_data) + tm_polygons("Religiousperc")
```

###Excluding Alaska and Hawaii
```{r}
combined_data_sub <- combined_data %>% 
  filter(STATEFP != "02") %>% 
  filter(STATEFP != "15") %>% 
  filter(STATEFP != "60") %>% 
  filter(STATEFP != "66") %>% 
  filter(STATEFP != "69") %>% 
  filter(STATEFP != "72") %>% 
  filter(STATEFP != "78")
```

```{r}
tm_shape(combined_data_sub, projection = 2163) + tm_polygons("Religiousperc")
```
##Mean of Religious Population
```{r}
combined_data_sub <- combined_data_sub %>% 
  mutate(redblue = Religiousperc - 51.41)
```
####### The mean of "religiousperc" in this dataset help us differentiate the states percentage of religious population to observe differences between states in a clearer way. The mean also helps avoide extrapolation, in this case the mean is 51.41.

```{r}
tm_shape(combined_data_sub, projection = 2163) + tm_polygons("redblue", palette = "-RdBu")
```

###Highlighting State lines
```{r}
us_states <- combined_data_sub %>% 
  aggregate_map(by = "STATEFP")
```

```{r}
tm_shape(combined_data_sub, projection = 2163) + tm_polygons("redblue", palette = "-RdBu") + 
  tm_shape(us_states) + tm_borders(lwd = .36, col = "black", alpha = 1)
```

```{r}
tm_shape(combined_data_sub, projection = 2163) + tm_polygons("redblue", palette = "-RdBu", border.col = "grey", border.alpha = .4) + 
  tm_shape(us_states) + tm_borders(lwd = .36, col = "black", alpha = 1)
```


########This gives a spatial visualization by highlighting the state lines to understand the total percent regligious in the country among different states and counties. From the map above, the most religious areas are in scattered across the nation but we can identify large religious total percentages in the midnorth(North Dakota,South Dakota,Minnesota), midsouth(Texas, Louisiana,Oklahoma) and a little bit in midwest(mostly being in Utah). The map identifies by total religious percentage with ranges from -50 to 0, indiciating no relgiious populations, 0- 50 indicating religious population being between 0 -50%, 50 to 100% giving us higher total religious percentages and so on. 

####The American Nations
The American Nation: A History of the United States, written by Mark Carnes and John Garraty. The authors represent the political history of the United States tied with its social, economic and cultural development. Using the mapping file of the American Nations, this spatial analysis, shows county level percents of religious population among the 11 nations within America.

```{r}
Amer.nation <- combined_data_sub %>% 
  aggregate_map(by = "STATEFP")
```

```{r}
tm_shape(combined_data_sub, projection = 2163) + tm_polygons("redblue", palette = "-RdBu") + 
  tm_shape(Amer.nation) + tm_borders(lwd = .36, col = "black", alpha = 1)
```

###Making the boudary lines clearer
```{r}
tm_shape(combined_data_sub, projection = 2163) + tm_polygons("redblue", palette = "-RdBu", border.col = "grey", border.alpha = .4) + 
  tm_shape(Amer.nation) + tm_borders(lwd = .50, col = "black", alpha = 1)
```

###Tigris
######## The Tigris package makes creating spatial mapping easier and smoother to apply for the analysis as many functions have a parameter,CB. On slide 32 of Professor's slides, we coded counties(CB = TRUE).
If CF is set to TRUE, it will direct tigris to load a cartographic boundry file. Cartographic boundry files default to a simplified resoultion of 1:500k. In some cases such as states, resolutions of 1.5 million and 1:20 million are available. For example, R specify the following modifications to the states() function and retrieves a simplified dataset. In other words when CB is set to FALSE, we get a detailed TIGER/line file. When it is set to TRUE, the file is generalized to (1:500K). 

```{r}
options(tigris_class = "sf")
t_county <- counties(cb = TRUE)
#names(t_county)
```

```{r}
t_combined_data <- t_county %>% 
  mutate(fips_code = parse_integer(GEOID))
```

```{r}
t_combined_data_sub <- t_combined_data %>% 
  filter(STATEFP != "02") %>% 
  filter(STATEFP != "15") %>% 
  filter(STATEFP != "60") %>% 
  filter(STATEFP != "66") %>% 
  filter(STATEFP != "69") %>% 
  filter(STATEFP != "72") %>% 
  filter(STATEFP != "78")
```

```{r}
religiondata1 <- religiondata %>% 
  mutate(fips= Geo_FIPS)
```

```{r}
us_states <- t_combined_data_sub %>% 
  aggregate_map(by = "STATEFP")
Amer.nation <- t_combined_data_sub %>% 
  aggregate_map(by = "STATEFP")
t_combined_data_sub <- combined_data_sub %>% 
  mutate(redblue = Religiousperc - mean(religiondata$Religiousperc))
```

```{r}
tm_shape(combined_data_sub, projection = 2163) + tm_polygons("redblue", palette = "-RdBu", border.col = "grey", border.alpha = .4) + 
  tm_shape(Amer.nation) + tm_borders(lwd = .50, col = "black", alpha = 1)
```









-----
```{r}
options(tigris_class = "sf")
t_county <- counties(cb = FALSE)
#names(t_county)
```

```{r}
t_combined_data <- t_county %>% 
  mutate(fips_code = parse_integer(GEOID))
```

```{r}
t_combined_data_sub <- t_combined_data %>% 
  filter(STATEFP != "02") %>% 
  filter(STATEFP != "15") %>% 
  filter(STATEFP != "60") %>% 
  filter(STATEFP != "66") %>% 
  filter(STATEFP != "69") %>% 
  filter(STATEFP != "72") %>% 
  filter(STATEFP != "78")
```

```{r}
religiondata1 <- religiondata %>% 
  mutate(fips= Geo_FIPS)
```

```{r}
us_states <- t_combined_data_sub %>% 
  aggregate_map(by = "STATEFP")
Amer.nation <- t_combined_data_sub %>% 
  aggregate_map(by = "STATEFP")
t_combined_data_sub <- combined_data_sub %>% 
  mutate(redblue = Religiousperc - mean(religiondata$Religiousperc))
```

```{r}
tm_shape(combined_data_sub, projection = 2163) + tm_polygons("redblue", palette = "-RdBu", border.col = "grey", border.alpha = .4) + 
  tm_shape(Amer.nation) + tm_borders(lwd = .50, col = "black", alpha = 1)
```

###Differences between Spatial and Non-Spatial Data
Spatial data evulates the percent religious as a whole and its relationship between states but not in a precise and particular way as non-spatial. Non-spatial data is usually independent rather than multidemensional and auro correlated as spatial. Locational data as used for this analysis is assessed through spatial analysis rather than coverting location functions which have ecological fallacies due to other influenceing variables. Overall, by evaluating both spatial and non-spatial analysis for this assignment have advantages and disadvantages. Spatial data is simple but does not give a indepth analysis. When we combine both data analysis, we are able to evaluate total religious population percentage easily by location.
