GPRas 0.1.1

algorithms for measuring concentration distribution in a spatial network.

# Geographical PageRank Algorithms

A set of algorithms for measuring concentration distribution in a spatial network.

Link to pypi: https://pypi.python.org/pypi/GPRas

==============================

## published article
for more detail info and applications

article:
Chin, W. C. B., & Wen, T. H. (2015). Geographically Modified PageRank Algorithms: Identifying the Spatial Concentration of Human Movement in a Geospatial Network. PLoS ONE 10(10): e0139509. DOI:10.1371/journal.pone.0139509.

article authors:
**Wei-Chien-Benny Chin**, Tzai-Hung Wen

open access link:
<a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0139509" target="blank">the article (PLoSONE)</a>

==============================

## Intro.
The algorithm can be used to calculate these from a spatial network, i.e. network with x, y coordinates that can be used to calculate distance (i.e. projected accordingly from longitude and latitude):

- PR
- WPR
- DDPR (proposed in the PloSOne article above)
- GPR (proposed in the PloSOne article above)
- eDDPR (proposed in the PloSOne article above)
- eGPR (proposed in the PloSOne article above)

==============================

## dependencies

- numpy
- pandas
- geopandas
- shapely
- networkx

==============================

## installing GPRas

on windows:

install shapely first. can use conda if you are using anaconda(miniconda).

```
conda install -c scitools shapely=1.5.13
```

Geopandas is needed to be installed before installing this, which could be hard to install.
First download these from here: http://www.lfd.uci.edu/~gohlke/pythonlibs/

- GDAL
- pyproj
- Fiona

Then, install them with pip install

```sh
pip install xxx-GDAL--.whl
pip install xxx-pyproj--.whl
pip install xxx-Fiona--.whl

```

Then, install GPRas

```sh
pip install GPRas
```

==============================

## Usage

import the module

```python
import GPRas as gpr

```

### data input and create the spatial graph concentration object
Two ways of data input: csv file, nx.Graph object

**for csv file**:
prepare the file paths and file setup info

```python
nodefile = 'test_data/TP_nodes.csv'
linkfile = 'test_data/TP_links.csv'
node_filesetup = {'node_id':'nid', 'xcor':'xcor', 'ycor':'ycor'}
link_filesetup = {'ori':'ori', 'des':'des', 'weight':'time'}
```

create the spatial_graph object

```python
sg = gpr.GPRas()
```

set the dataset from csv files
```python
sg.dataset.from_csv(nodefile, linkfile, node_filesetup, link_filesetup)
```

**for nx.Graph object**
```python
import networkx as nx
import pandas as pd ## just for the reading of file before creating nx.Graph
ndf = pd.read_csv(nodefile)
nii = ndf[node_filesetup['node_id']].tolist()
nxx = ndf[node_filesetup['xcor']].tolist()
nyy = ndf[node_filesetup['ycor']].tolist()
nodes_lis = [ (ii, dict(xxx=xx,yyy=yy)) for ii,xx,yy in zip(nii,nxx,nyy) ]
edf = pd.read_csv(linkfile)
eoo = edf[link_filesetup['ori']].tolist()
edd = edf[link_filesetup['des']].tolist()
edges_lis = [ (oo,dd) for oo,dd in zip(eoo,edd) ]

G = nx.Graph()
G.add_nodes_from(nodes_lis)
ag.add_edges_from(edges_lis)

## graph setup info
node_setup = dict(xcor='xxx', ycor='yyy') ## x,y columns from the nodes attr.
link_setup = None

sg = sgc()
sg.dataset.from_nx(ag, node_setup, link_setup)
```

### prepare and calculate
First, initialize the parameters

```python
sg.Initialize(iteration=5000, alpha=1., beta=1., gamma=1.)
```

Calculate all of them (6 PRs) using the above parameter
```python
sg.CalculateAll()
```

Calculate each of them using updated parameters
```python
sg.PR()
sg.WPR(alpha=2, update=True)
sg.DDPR(beta=2, update=True)
sg.GPR(alpha=3, beta=2, update=True)
sg.eDDPR(gamma=0.6, update=True)
sg.eGPR(alpha=3, gamma=0.2, update=True)
```
these calculation will change the parameters for summary output, but not the default paramters (for calculation with no paramter setup, eg. alpha=None), which are set from the initialization.

### get the results in Pandas DataFrame format
get summary df

```python
summary_df = sg.get_summary()
print summary_df.head()
```

get the nodes concentration results

```python
result_df = sg.get_results() ## default to all 6
print result_df.head()

part_result_df = sg.get_results(items=['pr', 'wpr']) ## export only PR and WPR
print part_result_df.head()
```

### export results
the results can be export directly to csv format and shapfile format

**summary table**

```python
sg.output_summary(filename='result/temp_summary.csv')
```

**to csv file**
```python
sg.to_csv(filename='result/temp_output_dd_g.csv', items=['ddpr','gpr'])

# also same as to_csv() with items=None
sg.All_to_csv(filename='result/temp_output.csv')
```

**to shp file**
prepare a crs string, or it will be default to None
```python
crs67 = '+proj=tmerc +lat_0=0 +lon_0=121 +k=0.9999 +x_0=250000 +y_0=0 +ellps=aust_SA +towgs84=-752,-358,-179,-0.0000011698,0.0000018398,0.0000009822,0.00002329 +units=m +no_defs'
```

export the files, which include a point shpfile for nodes and the concentration results, and a line shpfile for edge (no result inside, just for mapping)

output two of the results (PR and WPR) into one file

```python
sg.to_shps(filename_prefix='result/temp_shp_pr_wpr', crs=crs67, items=['pr','wpr'])
```

output all 6 PRs

```python
sg.All_to_shps(filename_prefix='result/temp_shp', crs=crs67)
```

### to networkx graph object
similar to the previous export

```python
ag,pos = sg.to_nx() ## default to all 6 PRs

ag,pos = sg.to_nx(items=["DDPR"])
```

demo for drawing the networkx object
```python
import matplotlib.pyplot as plt

ss = []
for n,d in ag.nodes_iter(data=True):
ss.append(d['DDPR_score']*5000.)

fig,ax = plt.subplots()
nx.draw_networkx_nodes(ag, pos=pos, node_size=ss)
nx.draw_networkx_edges(ag, pos=pos, alpha=0.3)
ax.set_aspect('equal')
plt.show()
```

==============================
## file structure

```sh
Geographical PageRank Algorithms
.
├── build
│   ├── bdist.linux-x86_64
│   └── lib
│   └── GPRas
│   ├── GPRa
│   │   ├── GPR_algorithms.py
│   │   ├── __init__.py
│   │   ├── input_func.py
│   │   ├── input_funcs
│   │   │   ├── DataImport_nx.py
│   │   │   ├── FileReading_csv.py
│   │   │   └── __init__.py
│   │   ├── output_func.py
│   │   └── output_funcs
│   │   ├── __init__.py
│   │   ├── to_csv.py
│   │   ├── to_nx.py
│   │   └── to_shps.py
│   ├── GPRas.py
│   └── __init__.py
├── dist
│   ├── GPRas-0.1.0-py2.7.egg
│   ├── GPRas-0.1.0-py2-none-any.whl
│   └── GPRas-0.1.0.tar.gz
├── GPRas
│   ├── example
│   │   ├── example_nx.py
│   │   ├── example.py
│   │   ├── result
│   │   │   └── demo results
│   │   └── test_data
│   │   ├── KH_links.csv
│   │   ├── KH_nodes.csv
│   │   ├── TC_links.csv
│   │   ├── TC_nodes.csv
│   │   ├── TP_links.csv
│   │   └── TP_nodes.csv
│   ├── GPRa
│   │   ├── GPR_algorithms.py
│   │   ├── __init__.py
│   │   ├── input_func.py
│   │   ├── input_funcs
│   │   │   ├── DataImport_nx.py
│   │   │   ├── FileReading_csv.py
│   │   │   └── __init__.py
│   │   ├── output_func.py
│   │   └── output_funcs
│   │   ├── __init__.py
│   │   ├── to_csv.py
│   │   ├── to_nx.py
│   │   └── to_shps.py
│   ├── GPRas.py
│   └── __init__.py
├── GPRas.egg-info
│   ├── dependency_links.txt
│   ├── PKG-INFO
│   ├── requires.txt
│   ├── SOURCES.txt
│   └── top_level.txt
├── LICENSE.txt
├── MANIFEST.in
├── README.md
└── setup.py

```