def tractCentroids(request):
"""This endpoint returns census tract centroids used to determine circle position on map"""
geos = get_censustract_geos(request)
if geos is None:
return HttpResponseNotFound("Missing one of lat/lon bounds or metro")
tracts_geo_json = geo_as_json(geos)
return HttpResponse(json.dumps(tracts_geo_json), content_type='application/json')
def tractCentroids(request):
"""This endpoint returns census tract centroids used to determine circle position on map"""
geos = get_censustract_geos(request)
if geos is None:
return HttpResponseNotFound("Missing one of lat/lon bounds or metro")
tracts_geo_json = geo_as_json(geos)
return HttpResponse(json.dumps(tracts_geo_json), content_type='application/json')
def msas(request):
try:
query = get_censustract_geos(request, metro=True)
msas = {'msas': [msa.geoid for msa in query]}
return HttpResponse(json.dumps(msas), content_type='application/json')
except:
return HttpResponseBadRequest("request failed; details: %s" % request)
def race_summary(request):
"""Race summary statistics"""
geos = get_censustract_geos(request)
if len(geos) > 0:
query = Census2010RaceStats.objects.filter(geoid__in=geos)
else:
query = Census2010RaceStats.objects.all()
return query
def race_summary(request):
"""Race summary statistics"""
geos = get_censustract_geos(request)
if len(geos) > 0:
query = Census2010RaceStats.objects.filter(geoid__in=geos)
else:
query = Census2010RaceStats.objects.all()
return query
def msas(request):
"""return a list of MSA ids visible by bounding coordinates"""
try:
msas = get_censustract_geos(request, metro=True)
msa_list = [metro.geoid for metro in msas]
return HttpResponse(json.dumps(msa_list),
content_type='application/json')
except:
return HttpResponseBadRequest("Invalid bounding coordinates")
def race_summary_csv(request):
institution_id = request.GET.get('lender')
metro = request.GET.get('metro')
action_taken_param = request.GET.get('action_taken')
action_taken_selected = action_taken_param.split(',')
tracts_in_msa = get_censustract_geos(request)
queryset = Census2010RaceStats.objects.filter(geoid__in=tracts_in_msa)
file_name = 'HMDA-Census-Tract_2013_Lender%s_MSA%s.csv' % (institution_id,
metro)
response = HttpResponse(content_type='text/csv')
response['Content-Disposition'] = 'attachment; filename=%s' % file_name
writer = csv.writer(response, csv.excel)
#response.write(u'\ufeff'.encode('utf8')) # BOM (optional...Excel needs it to open UTF-8 file properly)
writer.writerow([
smart_str(u"geoid"),
smart_str(u"Total Population"),
smart_str(u"Hispanic Percentage"),
smart_str(u"White Only Percentage"),
smart_str(u"Non Hispanic Black Only Percentage"),
smart_str(u"Non Hispanic Asian Only Percentage"),
smart_str(u"HMDA LAR Count"),
smart_str(u"Total Households"),
])
for obj in queryset:
geoid = "'%s'" % str(obj.geoid.geoid)
lar_count = HMDARecord.objects.filter(
institution_id=institution_id,
geo=obj.geoid,
action_taken__in=action_taken_selected).filter(
base_hmda_query()).count()
census_households = Census2010Households.objects.filter(
geoid=obj.geoid).first()
if census_households:
num_households = census_households.total
else:
num_households = 0
writer.writerow([
smart_str(geoid),
smart_str(obj.total_pop),
smart_str(obj.hispanic_perc * 100),
smart_str(obj.non_hisp_white_only_perc * 100),
smart_str(obj.non_hisp_black_only_perc * 100),
smart_str(obj.non_hisp_asian_only_perc * 100),
smart_str(lar_count),
smart_str(num_households),
])
return response
def loan_originations(request):
institution_id = request.GET.get('lender')
metro = request.GET.get('metro')
action_taken_param = request.GET.get('action_taken')
lender_hierarchy = request.GET.get('lh')
peers = request.GET.get('peers')
census_tracts = get_censustract_geos(request)
query = HMDARecord.objects.filter(base_hmda_query())
#if lender param key is passed in
if institution_id:
institution_selected = get_object_or_404(Institution,
pk=institution_id)
if lender_hierarchy == 'true':
hierarchy_list = institution_selected.get_lender_hierarchy(
False, False)
if len(hierarchy_list) > 0:
query = query.filter(institution__in=hierarchy_list)
else:
query = query.filter(institution=institution_selected)
elif peers == 'true' and metro:
metro_selected = Geo.objects.filter(geo_type=Geo.METRO_TYPE,
geoid=metro).first()
peer_list = institution_selected.get_peer_list(
metro_selected, True, False)
if len(peer_list) > 0:
query = query.filter(institution__in=peer_list)
else:
query = query.filter(institution=institution_selected)
else:
query = query.filter(institution=institution_selected)
if len(census_tracts) > 0:
query = query.filter(geo__in=census_tracts)
if action_taken_param:
action_taken_selected = action_taken_param.split(',')
if action_taken_selected:
query = query.filter(action_taken__in=action_taken_selected)
#count on geo_id
query = query.values('geo_id', 'geo__census2010households__total',
'geo__centlat',
'geo__centlon').annotate(volume=Count('geo_id'))
return query
def loan_originations(request):
institution_id = request.GET.get('lender')
metro = request.GET.get('metro')
action_taken_param = request.GET.get('action_taken')
lender_hierarchy = request.GET.get('lh')
peers = request.GET.get('peers')
year = request.GET.get('year')
census_tracts = get_censustract_geos(request)
query = HMDARecord.objects.all()
if institution_id:
institution_selected = get_object_or_404(
Institution, pk=institution_id)
if lender_hierarchy == 'true':
hierarchy_list = institution_selected.get_lender_hierarchy(
False, False, year)
if len(hierarchy_list) > 0:
query = query.filter(institution__in=hierarchy_list)
else:
query = query.filter(institution=institution_selected)
elif peers == 'true' and metro:
metro_selected = Geo.objects.filter(
geo_type=Geo.METRO_TYPE, geoid=metro).first()
peer_list = institution_selected.get_peer_list(
metro_selected, True, False)
if len(peer_list) > 0:
query = query.filter(institution__in=peer_list)
else:
query = query.filter(institution=institution_selected)
else:
query = query.filter(institution=institution_selected)
if len(census_tracts) > 0:
query = query.filter(geo__in=census_tracts)
if action_taken_param:
action_taken_selected = action_taken_param.split(',')
if action_taken_selected:
query = query.filter(action_taken__in=action_taken_selected)
#count on geo_id
query = query.values('geo_id', 'geo__census2010households__total',
'geo__centlat', 'geo__centlon', 'geo__state',
'geo__county',
'geo__tract').annotate(volume=Count('geo_id'))
return query
def race_summary_csv(request):
institution_id = request.GET.get('lender')
metro = request.GET.get('metro')
year = request.GET.get('year')
if institution_id and metro:
lar_data = loan_originations_as_json(request)
tracts_in_msa = get_censustract_geos(request)
queryset = Census2010RaceStats.objects.filter(geoid__in=tracts_in_msa)
file_name = 'HMDA-Census-Tract_Year%s_Lender%s_MSA%s.csv' % (
year, institution_id, metro)
response = HttpResponse(content_type='text/csv')
response['Content-Disposition'] = 'attachment; filename=%s' % file_name
writer = csv.writer(response, csv.excel)
writer.writerow([
smart_str(u"geoid"),
smart_str(u"Total Population"),
smart_str(u"Hispanic Percentage"),
smart_str(u"White Only Percentage"),
smart_str(u"Non Hispanic Black Only Percentage"),
smart_str(u"Non Hispanic Asian Only Percentage"),
smart_str(u"Originated Loans"),
smart_str(u"Total Households"),
])
for obj in queryset:
geoid = "'%s'" % str(obj.geoid.geoid)
if lar_data.get(obj.geoid.geoid, None):
lar_count = lar_data[obj.geoid.geoid]['volume']
num_households = lar_data[obj.geoid.geoid]['num_households']
else:
lar_count = 0
# if there's no loan data we can still get household data
num_households = get_object_or_404(
Census2010Households, geoid_id=obj.geoid.geoid).total
writer.writerow([
smart_str(geoid),
smart_str(obj.total_pop),
smart_str(obj.hispanic_perc * 100),
smart_str(obj.non_hisp_white_only_perc * 100),
smart_str(obj.non_hisp_black_only_perc * 100),
smart_str(obj.non_hisp_asian_only_perc * 100),
smart_str(lar_count),
smart_str(num_households),
])
return response
else:
raise Http404("Invalid Institution or Metro")
def race_summary_csv(request):
institution_id = request.GET.get('lender')
metro = request.GET.get('metro')
year = request.GET.get('year')
if institution_id and metro:
lar_data = loan_originations_as_json(request)
tracts_in_msa = get_censustract_geos(request)
queryset = Census2010RaceStats.objects.filter(geoid__in=tracts_in_msa)
file_name = 'HMDA-Census-Tract_Year%s_Lender%s_MSA%s.csv' % (year, institution_id, metro)
response = HttpResponse(content_type='text/csv')
response['Content-Disposition'] = 'attachment; filename=%s' % file_name
writer = csv.writer(response, csv.excel)
writer.writerow([
smart_str(u"geoid"),
smart_str(u"Total Population"),
smart_str(u"Hispanic Percentage"),
smart_str(u"White Only Percentage"),
smart_str(u"Non Hispanic Black Only Percentage"),
smart_str(u"Non Hispanic Asian Only Percentage"),
smart_str(u"Originated Loans"),
smart_str(u"Total Households"),
])
for obj in queryset:
geoid = "'%s'" % str(obj.geoid.geoid)
if lar_data.get(obj.geoid.geoid,None):
lar_count = lar_data[obj.geoid.geoid]['volume']
num_households = lar_data[obj.geoid.geoid]['num_households']
else:
lar_count = 0
# if there's no loan data we can still get household data
num_households = get_object_or_404(Census2010Households, geoid_id=obj.geoid.geoid).total
writer.writerow([
smart_str(geoid),
smart_str(obj.total_pop),
smart_str(obj.hispanic_perc * 100),
smart_str(obj.non_hisp_white_only_perc * 100),
smart_str(obj.non_hisp_black_only_perc * 100),
smart_str(obj.non_hisp_asian_only_perc * 100),
smart_str(lar_count),
smart_str(num_households),
])
return response
else:
raise Http404("Invalid Institution or Metro")
def race_summary_csv(request):
lar_data = loan_originations_as_json(request)
tracts_in_msa = get_censustract_geos(request)
queryset = Census2010RaceStats.objects.filter(geoid__in=tracts_in_msa)
file_name = 'HMDA-Census-Tract_2013_Lender%s_MSA%s.csv' % (request.GET.get('lender'), request.GET.get('metro'))
response = HttpResponse(content_type='text/csv')
response['Content-Disposition'] = 'attachment; filename=%s' % file_name
writer = csv.writer(response, csv.excel)
#response.write(u'\ufeff'.encode('utf8')) # BOM (optional...Excel needs it to open UTF-8 file properly)
writer.writerow([
smart_str(u"geoid"),
smart_str(u"Total Population"),
smart_str(u"Hispanic Percentage"),
smart_str(u"White Only Percentage"),
smart_str(u"Non Hispanic Black Only Percentage"),
smart_str(u"Non Hispanic Asian Only Percentage"),
smart_str(u"HMDA LAR Count"),
smart_str(u"Total Households"),
])
for obj in queryset:
geoid = "'%s'" % str(obj.geoid.geoid)
try:
lar_count = lar_data[obj.geoid.geoid]['volume']
except:
lar_count = 0
try:
num_households = lar_data[obj.geoid.geoid]['num_households']
except:
num_households = 0
writer.writerow([
smart_str(geoid),
smart_str(obj.total_pop),
smart_str(obj.hispanic_perc * 100),
smart_str(obj.non_hisp_white_only_perc * 100),
smart_str(obj.non_hisp_black_only_perc * 100),
smart_str(obj.non_hisp_asian_only_perc * 100),
smart_str(lar_count),
smart_str(num_households),
])
return response
